From 92a1e0fbae2bf7dd01206d15d4e6777462b55b16 Mon Sep 17 00:00:00 2001
From: yuanwu <yuan.wu@intel.com>
Date: Tue, 24 Sep 2024 03:06:55 +0000
Subject: [PATCH] Aligin the source code with main branch 2.0.4

Signed-off-by: yuanwu <yuan.wu@intel.com>
---
 benchmark/src/generation.rs                   |    6 +-
 benchmark/src/main.rs                         |    2 +-
 router/client/Cargo.toml                      |    1 -
 router/client/src/client.rs                   |  368 +--
 router/client/src/sharded_client.rs           |    4 -
 router/src/infer.rs                           |   13 +-
 router/src/main.rs                            |   33 +-
 router/src/queue.rs                           |  211 +-
 router/src/server.rs                          |    4 -
 router/src/validation.rs                      |   50 +-
 server/Makefile                               |   14 +-
 server/dill-0.3.7-patch.sh                    |   91 -
 server/dill-0.3.8-patch.sh                    |   91 -
 server/poetry.lock                            | 2821 ++++++++---------
 server/pyproject.toml                         |   46 +-
 server/requirements.txt                       |   88 -
 server/tests/models/test_bloom.py             |   43 +-
 server/tests/models/test_causal_lm.py         |  290 +-
 server/tests/models/test_grammar.py           |  245 --
 server/tests/models/test_seq2seq_lm.py        |    7 -
 server/tests/models/test_starcoder.py         |  372 ---
 server/tests/utils/test_tokens.py             |   44 -
 server/tests/utils/test_watermark.py          |   85 +-
 server/text_generation_server/cli.py          |  121 +-
 .../habana_quantization_env.py                |   27 -
 server/text_generation_server/interceptor.py  |    9 +-
 .../layers/gptq/exllamav2.py                  |    2 +
 .../layers/gptq/exllamav2.py.rej              |   10 -
 .../layers/gptq/quant_linear.py               |  712 ++---
 .../text_generation_server/models/__init__.py |  783 ++++-
 server/text_generation_server/models/bloom.py |   94 +-
 .../models/causal_lm.py                       | 1508 ++++-----
 .../models/custom_modeling/llava_next.py      |  427 ++-
 .../models/flash_mistral.py                   |    2 +-
 server/text_generation_server/models/model.py |   28 +-
 .../models/santacoder.py                      |   47 +-
 .../models/starcoder.py                       |   47 -
 .../models/vlm_causal_lm.py                   | 1150 +------
 server/text_generation_server/server.py       |  125 +-
 server/text_generation_server/tgi_service.py  |   37 -
 .../text_generation_server/utils/__init__.py  |    6 -
 server/text_generation_server/utils/debug.py  |   31 -
 server/text_generation_server/utils/dist.py   |   11 -
 .../utils/gptq/quant_linear.py                |  359 ---
 .../utils/logits_process.py                   |   95 +-
 server/text_generation_server/utils/tokens.py |  125 +-
 .../text_generation_server/utils/version.py   |   12 -
 .../text_generation_server/utils/watermark.py |   26 +-
 48 files changed, 3972 insertions(+), 6751 deletions(-)
 delete mode 100644 server/dill-0.3.7-patch.sh
 delete mode 100644 server/dill-0.3.8-patch.sh
 delete mode 100644 server/requirements.txt
 delete mode 100644 server/tests/models/test_grammar.py
 delete mode 100644 server/tests/models/test_starcoder.py
 delete mode 100644 server/text_generation_server/habana_quantization_env.py
 delete mode 100644 server/text_generation_server/layers/gptq/exllamav2.py.rej
 delete mode 100644 server/text_generation_server/models/starcoder.py
 delete mode 100644 server/text_generation_server/tgi_service.py
 delete mode 100644 server/text_generation_server/utils/debug.py
 delete mode 100644 server/text_generation_server/utils/gptq/quant_linear.py
 delete mode 100644 server/text_generation_server/utils/version.py

diff --git a/benchmark/src/generation.rs b/benchmark/src/generation.rs
index b2766d0cd..ea7c97789 100644
--- a/benchmark/src/generation.rs
+++ b/benchmark/src/generation.rs
@@ -163,11 +163,8 @@ async fn prefill(
 
     // Run prefill
     let start_time = Instant::now();
-
     let (_, decode_batch, _) = client.prefill(batch.clone()).await?;
 
-    let (_, decode_batch, _) = client.decode(vec![decode_batch.clone().unwrap()]).await?;
-
     // Get latency
     let latency = start_time.elapsed();
 
@@ -183,12 +180,11 @@ async fn prefill(
     };
 
     Ok((step, decode_batch))
-
 }
 
 /// Run a full decode
 async fn decode(batch: CachedBatch, client: &mut ShardedClient) -> Result<Decode, ClientError> {
-    let mut decode_length = 1; // 1 decode step was already scheduled in prefill with speculative scheduling
+    let mut decode_length = 0;
     let batch_size = batch.size;
 
     let start_time = Instant::now();
diff --git a/benchmark/src/main.rs b/benchmark/src/main.rs
index 34b91a92e..2d89e045e 100644
--- a/benchmark/src/main.rs
+++ b/benchmark/src/main.rs
@@ -51,7 +51,7 @@ struct Args {
     runs: usize,
 
     /// Number of warmup cycles
-    #[clap(default_value = "3", short, long, env)]
+    #[clap(default_value = "1", short, long, env)]
     warmups: usize,
 
     /// The location of the grpc socket. This benchmark tool bypasses the router
diff --git a/router/client/Cargo.toml b/router/client/Cargo.toml
index bc4ae72eb..d01317846 100644
--- a/router/client/Cargo.toml
+++ b/router/client/Cargo.toml
@@ -9,7 +9,6 @@ homepage.workspace = true
 futures = "^0.3"
 grpc-metadata = { path = "../grpc-metadata" }
 prost = "^0.12"
-rand = "0.8.5"
 thiserror = "^1.0"
 tokio = { version = "^1.32", features = ["sync"] }
 tonic = "^0.10"
diff --git a/router/client/src/client.rs b/router/client/src/client.rs
index 4a0d44eed..e8035106b 100644
--- a/router/client/src/client.rs
+++ b/router/client/src/client.rs
@@ -1,13 +1,9 @@
-/// Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
 /// Single shard Client
 use crate::pb::generate::v2::text_generation_service_client::TextGenerationServiceClient;
 use crate::pb::generate::v2::*;
 use crate::Result;
-use std::env;
-use rand::{distributions::Uniform, Rng};
 use grpc_metadata::InjectTelemetryContext;
-use std::cmp;
+use std::cmp::min;
 use std::time::Duration;
 use tonic::transport::{Channel, Uri};
 use tracing::instrument;
@@ -110,344 +106,72 @@ impl Client {
         max_prefill_tokens: u32,
         max_total_tokens: u32,
         max_batch_size: Option<usize>,
-        model_id: &str
     ) -> Result<Option<u32>> {
-        let warmup_enabled: bool = env::var("WARMUP_ENABLED").ok().map_or(true, |value| value.to_lowercase() == "true");
-        if !warmup_enabled {
-            return Ok(None);
-        }
-
-        let read_env_var = |key: &str, default: u32| -> u32 {
-            env::var(key).ok().map_or(default, |value| value.parse::<u32>().unwrap())
-        };
-
-        // get all possible batch sizes
-        let decode_bucket_size: u32 = read_env_var("BATCH_BUCKET_SIZE", 8);
-        let max_decode_batch_size: u32 = match max_batch_size {
-            Some(max_batch_size) => max_batch_size as u32,
-            None => decode_bucket_size
-        };
-        let decode_batch_sizes: Vec<u32> = (decode_bucket_size..max_decode_batch_size+1).step_by(decode_bucket_size as usize).collect();
-
-        let prefill_bucket_size: u32 = read_env_var("PREFILL_BATCH_BUCKET_SIZE", 4);
-        let mut max_prefill_batch_size: u32 = max_prefill_tokens / max_input_length;
-        max_prefill_batch_size = cmp::min(max_prefill_batch_size, max_decode_batch_size);
-        let prefill_batch_sizes: Vec<u32> = (prefill_bucket_size..max_prefill_batch_size+1).step_by(prefill_bucket_size as usize).collect();
-
-        // get all possible sequence lengths for prefill
-        let seq_bucket_size: u32 = read_env_var("PAD_SEQUENCE_TO_MULTIPLE_OF", 128);
-        let mut seq_lengths: Vec<u32> = (seq_bucket_size..max_input_length+1).step_by(seq_bucket_size as usize).collect();
-        if let Some(&last) = seq_lengths.last() {
-            if last < max_input_length {
-                seq_lengths.push(max_input_length);
-            }
-        }
-
-        // execute batch for each combination of batch size and sequence length
-        let mut shapes: Vec<(u32, u32)> = Vec::with_capacity(prefill_batch_sizes.len() * seq_lengths.len());
-        for batch_size in &prefill_batch_sizes {
-            for seq_length in &seq_lengths {
-                shapes.push((*batch_size, *seq_length));
-            }
-        }
-
-        let mut batch_counter: u64 = 0;
-        let mut request_counter: u64 = 0;
-        if model_id.contains("llava") {
-            let mut n_tokens = 0;
-            let mut requests = Vec::new();
-            // Create requests
-            while n_tokens < max_prefill_tokens {
-                let truncate = cmp::min(max_input_length, max_prefill_tokens - n_tokens);
-
-                let mut inputs = String::new();
-                inputs.push_str("![](data:image/jpeg;base64,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)");
-                inputs.push_str(&"_test ".to_string().repeat(max_input_length as usize));
-
-                requests.push(Request {
-                    id: 0,
-                    // We truncate the input on the server side to be sure that it has the correct size
-                    inputs,
-                    truncate,
-                    // Set sampling parameters to also take these ops into account in the max memory
-                    parameters: Some(NextTokenChooserParameters {
-                        temperature: 0.9,
-                        top_k: 10,
-                        top_p: 0.9,
-                        typical_p: 0.9,
-                        do_sample: false,
-                        seed: 0,
-                        repetition_penalty: 1.2,
-                        frequency_penalty: 0.1,
-                        watermark: true,
-                        grammar: String::new(),
-                        grammar_type: GrammarType::None as i32,
-                    }),
-                    stopping_parameters: Some(StoppingCriteriaParameters {
-                        max_new_tokens: max_total_tokens - truncate,
-                        stop_sequences: vec![],
-                        ignore_eos_token: true,
-                    }),
-                    prefill_logprobs: true,
-                    top_n_tokens: 20,
-                });
-                n_tokens += max_input_length;
-
-                // Check max_batch_size
-                if Some(requests.len()) == max_batch_size {
-                    break;
-                }
-            }
-
-            let mut batches = Vec::new();
-            batches.push(Batch {
-                id: 0,
-                size: requests.len() as u32,
-                requests,
-                max_tokens: 0,
-            });
-
-            let request = tonic::Request::new(WarmupRequest {
-                batches,
-                max_input_length,
-                max_prefill_tokens,
-                max_total_tokens,
-            })
-            .inject_context();
-            let response = self.stub.warmup(request).await?.into_inner();
-            Ok(response.max_supported_total_tokens)
-        } 
-        else {
-            for shape in shapes.iter() {
-                let (batch_size, seq_length) = shape;
-                let mut batches: Vec<Batch> = vec![
-                    self.create_warmup_batch(
-                        *shape,
-                        &mut batch_counter,
-                        &mut request_counter,
-                        max_input_length,
-                        max_total_tokens,
-                        seq_bucket_size,
-                        false,
-                        None,
-                    )
-                ];
-                // if possible, create second batch in order to trigger concatenate operation
-                if *batch_size < max_decode_batch_size {
-                    batches.push(
-                        self.create_warmup_batch(
-                            (1, *seq_length),
-                            &mut batch_counter,
-                            &mut request_counter,
-                            max_input_length,
-                            max_total_tokens,
-                            seq_bucket_size,
-                            false,
-                            None,
-                        )
-                    );
-                }
-
-                let request = tonic::Request::new(WarmupRequest {
-                    batches,
-                    max_input_length,
-                    max_prefill_tokens,
-                    max_total_tokens,
-                }).inject_context();
-                let _response = self.stub.warmup(request).await?.into_inner();
-            }
-
-            // send batches to warmup all possible decode shapes
-            if decode_batch_sizes.len() > 1 {
-                let steps_per_bucket: u32 = if decode_bucket_size <= max_prefill_batch_size {
-                    decode_bucket_size
-                } else {
-                    decode_bucket_size.div_ceil(max_prefill_batch_size)
-                };
-                let max_new_tokens: u32 = 2 * decode_batch_sizes.len() as u32 * steps_per_bucket;
-
-                let mut requests_send: u32 = cmp::min(max_prefill_batch_size, decode_bucket_size);
-                let mut batches: Vec<Batch> = vec![
-                    self.create_warmup_batch(
-                        (requests_send, seq_bucket_size),
-                        &mut batch_counter,
-                        &mut request_counter,
-                        max_input_length,
-                        max_total_tokens,
-                        seq_bucket_size,
-                        false,
-                        Some(max_new_tokens),
-                    )
-                ];
-
-                let get_current_decode_batch_size = |num: u32| -> u32 {
-                    decode_batch_sizes.iter()
-                        .filter(|&&x| x >= num)
-                        .min()
-                        .copied()
-                        .unwrap()
-                };
-
-                let mut current_decode_batch_size: u32 = get_current_decode_batch_size(requests_send);
-                while current_decode_batch_size < max_decode_batch_size {
-                    let distance_to_next_bucket = current_decode_batch_size + decode_bucket_size - requests_send;
-                    let num_requests: u32 = cmp::min(distance_to_next_bucket, max_prefill_batch_size);
-                    batches.push(
-                        self.create_warmup_batch(
-                            (num_requests, seq_bucket_size),
-                            &mut batch_counter,
-                            &mut request_counter,
-                            max_input_length,
-                            max_total_tokens,
-                            seq_bucket_size,
-                            false,
-                            Some(max_new_tokens),
-                        )
-                    );
-
-                    requests_send += num_requests;
-                    current_decode_batch_size = get_current_decode_batch_size(requests_send);
-                }
-
-                let request = tonic::Request::new(WarmupRequest {
-                    batches,
-                    max_input_length,
-                    max_prefill_tokens,
-                    max_total_tokens,
-                }).inject_context();
-                let _response = self.stub.warmup(request).await?.into_inner();
-            }
-
-            // send batches with default params to warm up Greedy search
-            let mut greedy_shapes: Vec<(u32, u32)> = Vec::with_capacity(prefill_batch_sizes.len());
-            for batch_size in &prefill_batch_sizes {
-                greedy_shapes.push((*batch_size, seq_bucket_size.clone()));
-            }
-            for greedy_shape in greedy_shapes.iter() {
-                let batches: Vec<Batch> = vec![
-                    self.create_warmup_batch(
-                        *greedy_shape,
-                        &mut batch_counter,
-                        &mut request_counter,
-                        max_input_length,
-                        max_total_tokens,
-                        seq_bucket_size,
-                        true,
-                        None,
-                    )
-                ];
-                let request = tonic::Request::new(WarmupRequest {
-                    batches,
-                    max_input_length,
-                    max_prefill_tokens,
-                    max_total_tokens,
-                }).inject_context();
-                let _response = self.stub.warmup(request).await?.into_inner();
-            }
-            Ok(None) // No support for maximum total tokens
-        }
-    }
-
-    #[instrument(skip_all)]
-    fn create_warmup_batch(
-        &mut self,
-        shape: (u32, u32),
-        batch_counter: &mut u64,
-        request_counter: &mut u64,
-        max_input_length: u32,
-        max_total_tokens: u32,
-        seq_bucket_size: u32,
-        default_params: bool,
-        max_new_tokens: Option<u32>,
-    ) -> Batch {
-        *batch_counter += 1;
-        let (batch_size, input_length) = shape;
+        let mut n_tokens = 0;
         let mut requests = Vec::new();
-        for _ in 0..batch_size {
-            *request_counter += 1;
-            let req_params = if default_params {
-                Some(NextTokenChooserParameters {
-                    temperature: 1.0,
-                    top_k: 0,
-                    top_p: 1.0,
-                    typical_p: 1.0,
-                    do_sample: false,
-                    seed: 0,
-                    repetition_penalty: 1.0,
-                    frequency_penalty: 0.0,
-                    watermark: false,
-                    grammar: String::new(),
-                    grammar_type: GrammarType::None as i32,
-                })
-            } else {
-                Some(NextTokenChooserParameters {
+        // Create requests
+        while n_tokens < max_prefill_tokens {
+            let truncate = min(max_input_length, max_prefill_tokens - n_tokens);
+
+            let mut inputs = String::new();
+            inputs.push_str(&"_test ".to_string().repeat(max_input_length as usize));
+            if n_tokens == 0 {
+                // 1 request is enough to test vision heads.
+                // Sending images on other queries messes up easily with truncation.
+                inputs.push_str("![](data:image/jpeg;base64,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)");
+            }
+
+            requests.push(Request {
+                id: 0,
+                // We truncate the input on the server side to be sure that it has the correct size
+                inputs,
+                truncate,
+                // Set sampling parameters to also take these ops into account in the max memory
+                parameters: Some(NextTokenChooserParameters {
                     temperature: 0.9,
                     top_k: 10,
                     top_p: 0.9,
                     typical_p: 0.9,
-                    do_sample: true,
+                    do_sample: false,
                     seed: 0,
                     repetition_penalty: 1.2,
                     frequency_penalty: 0.1,
-                    watermark: false,
+                    watermark: true,
                     grammar: String::new(),
                     grammar_type: GrammarType::None as i32,
-                })
-            };
-            requests.push(Request {
-                id: *request_counter,
-                inputs: self.get_random_input(input_length, seq_bucket_size),
-                truncate: max_input_length,
-                parameters: req_params,
+                }),
                 stopping_parameters: Some(StoppingCriteriaParameters {
-                    max_new_tokens: cmp::min(max_new_tokens.unwrap_or(10), max_total_tokens - max_input_length),
+                    max_new_tokens: max_total_tokens - truncate,
                     stop_sequences: vec![],
                     ignore_eos_token: true,
                 }),
-                prefill_logprobs: false,
-                top_n_tokens: 0,
+                prefill_logprobs: true,
+                top_n_tokens: 20,
             });
+            n_tokens += max_input_length;
+
+            // Check max_batch_size
+            if Some(requests.len()) == max_batch_size {
+                break;
+            }
         }
 
-        Batch {
-            id: *batch_counter,
+        let batch = Batch {
+            id: 0,
             size: requests.len() as u32,
             requests,
-            max_tokens: max_total_tokens,
-        }
-    }
+            max_tokens: 0,
+        };
 
-    #[instrument(skip_all)]
-    fn get_random_input(
-        &mut self,
-        input_length: u32,
-        seq_bucket_size: u32,
-    ) -> String {
-        let skip_tokenizer_in_tgi: bool = env::var("SKIP_TOKENIZER_IN_TGI")
-            .ok()
-            .map_or(false, |value| value.to_lowercase() == "true");
-        if skip_tokenizer_in_tgi {
-            // generate random tokens
-            let mut rng = rand::thread_rng();
-            let range = Uniform::new(2, 8192);
-            let tokens = if input_length % seq_bucket_size == 0 {
-                input_length - seq_bucket_size / 2
-            } else {
-                input_length - (input_length % seq_bucket_size) / 2
-            };
-            (0..tokens)
-                .map(|_| rng.sample(&range).to_string())
-                .collect::<Vec<String>>()
-                .join(", ")
-        } else {
-            // repeat test string to get expected input shape
-            let mut bucket_id = input_length / seq_bucket_size;
-            if input_length % seq_bucket_size != 0 {
-                bucket_id += 1
-            }
-            let repeats = cmp::max(1, (bucket_id - 1) * seq_bucket_size / 2);
-            "_test ".to_string().repeat(repeats as usize)
-        }
+        let request = tonic::Request::new(WarmupRequest {
+            batch: Some(batch),
+            max_input_length,
+            max_prefill_tokens,
+            max_total_tokens,
+        })
+        .inject_context();
+        let response = self.stub.warmup(request).await?.into_inner();
+        Ok(response.max_supported_total_tokens)
     }
 
     /// Generate one token for each request in the given batch
diff --git a/router/client/src/sharded_client.rs b/router/client/src/sharded_client.rs
index fdd84035c..e1e52d59a 100644
--- a/router/client/src/sharded_client.rs
+++ b/router/client/src/sharded_client.rs
@@ -1,5 +1,3 @@
-/// Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
 use crate::client::{DecodeTimings, PrefillTimings};
 /// Multi shard Client
 use crate::{Batch, CachedBatch, Client, Generation, HealthResponse, ShardInfo};
@@ -100,7 +98,6 @@ impl ShardedClient {
         max_prefill_tokens: u32,
         max_total_tokens: u32,
         max_batch_size: Option<usize>,
-        model_id: &str,
     ) -> Result<Option<u32>> {
         let futures: Vec<_> = self
             .clients
@@ -111,7 +108,6 @@ impl ShardedClient {
                     max_prefill_tokens,
                     max_total_tokens,
                     max_batch_size,
-                    model_id
                 ))
             })
             .collect();
diff --git a/router/src/infer.rs b/router/src/infer.rs
index 04328246d..eef429891 100644
--- a/router/src/infer.rs
+++ b/router/src/infer.rs
@@ -1,5 +1,3 @@
-/// Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
 /// Batching and inference logic
 use crate::validation::{Validation, ValidationError};
 use crate::{
@@ -65,22 +63,13 @@ impl Infer {
         max_batch_size: Option<usize>,
         max_concurrent_requests: usize,
         requires_padding: bool,
-        max_input_length: u32,
-        max_total_tokens: u32,
         window_size: Option<u32>,
         speculate: u32,
         generation_health: Arc<AtomicBool>,
         tokenizer_config: HubTokenizerConfig,
     ) -> Self {
         // Infer shared state
-        let queue = Queue::new(
-            requires_padding,
-            max_input_length,
-            max_total_tokens,
-            16,
-            window_size,
-            speculate
-        );
+        let queue = Queue::new(requires_padding, 16, window_size, speculate);
         let shared = Arc::new(Shared {
             batching_task: Notify::new(),
         });
diff --git a/router/src/main.rs b/router/src/main.rs
index 4f9f0f737..b11c4526f 100644
--- a/router/src/main.rs
+++ b/router/src/main.rs
@@ -1,5 +1,3 @@
-/// Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
 use axum::http::HeaderValue;
 use clap::Parser;
 use hf_hub::api::tokio::{Api, ApiBuilder, ApiRepo};
@@ -10,7 +8,6 @@ use opentelemetry::sdk::trace::Sampler;
 use opentelemetry::sdk::Resource;
 use opentelemetry::{global, KeyValue};
 use opentelemetry_otlp::WithExportConfig;
-use std::env;
 use std::fs::File;
 use std::io::BufReader;
 use std::net::{IpAddr, Ipv4Addr, SocketAddr};
@@ -148,25 +145,6 @@ async fn main() -> Result<(), RouterError> {
         }
     }
 
-    let (max_batch_size, max_batch_total_tokens) = match (max_batch_size, max_batch_total_tokens) {
-        (Some(_max_batch_size), Some(_max_batch_total_tokens)) => {
-            if (_max_batch_total_tokens as usize / max_total_tokens) != _max_batch_size {
-                tracing::warn!("max_batch_size was set to {_max_batch_size} while max_batch_total_tokens to {_max_batch_total_tokens}");
-                tracing::warn!("These values are not match, so max_batch_size will be preferred");
-                (Some(_max_batch_size), Some((_max_batch_size * max_total_tokens) as u32))
-            } else {
-                (Some(_max_batch_size), Some(_max_batch_total_tokens))
-            }
-        },
-        (Some(_max_batch_size), None) => (
-            Some(_max_batch_size), Some((_max_batch_size * max_total_tokens) as u32)
-        ),
-        (None, Some(_max_batch_total_tokens)) => (
-            Some(_max_batch_total_tokens as usize / max_total_tokens), Some(_max_batch_total_tokens)
-        ),
-        (None, None) => (None, None),
-    };
-
     // CORS allowed origins
     // map to go inside the option and then map to parse from String to HeaderValue
     // Finally, convert to AllowOrigin
@@ -228,9 +206,6 @@ async fn main() -> Result<(), RouterError> {
     };
 
     // Load tokenizer and model info
-    let skip_tokenizer_in_tgi = env::var("SKIP_TOKENIZER_IN_TGI")
-        .ok()
-        .map_or(false, |value| value.to_lowercase() == "true");
     let (tokenizer_filename, config_filename, tokenizer_config_filename, model_info) = match api {
         Type::None => (
             Some(local_path.join("tokenizer.json")),
@@ -279,11 +254,8 @@ async fn main() -> Result<(), RouterError> {
             )
         }
     };
-    let tokenizer: Option<Tokenizer> = if skip_tokenizer_in_tgi {
-        None
-    } else {
-        tokenizer_filename.and_then(|filename| Tokenizer::from_file(filename).ok())
-    };
+    let tokenizer: Option<Tokenizer> =
+        tokenizer_filename.and_then(|filename| Tokenizer::from_file(filename).ok());
     let config: Option<Config> = config_filename.and_then(|filename| {
         std::fs::read_to_string(filename)
             .ok()
@@ -349,7 +321,6 @@ async fn main() -> Result<(), RouterError> {
             max_batch_prefill_tokens,
             max_total_tokens as u32,
             max_batch_size,
-            &model_info.model_id
         )
         .await
         .map_err(RouterError::Warmup)?
diff --git a/router/src/queue.rs b/router/src/queue.rs
index 11690bf75..a32673dd1 100644
--- a/router/src/queue.rs
+++ b/router/src/queue.rs
@@ -1,14 +1,9 @@
-/// Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
 use crate::infer::InferError;
 use crate::infer::InferStreamResponse;
 use crate::validation::ValidGenerateRequest;
 use nohash_hasher::{BuildNoHashHasher, IntMap};
 use std::cmp::min;
-use std::cmp::{Eq, Ord, PartialEq, PartialOrd};
-use std::collections::BinaryHeap;
-use std::env;
-use std::time::Duration;
+use std::collections::VecDeque;
 use text_generation_client::{Batch, Request};
 use tokio::sync::{mpsc, oneshot};
 use tokio::time::Instant;
@@ -41,8 +36,6 @@ pub(crate) struct Queue {
 impl Queue {
     pub(crate) fn new(
         requires_padding: bool,
-        max_input_length: u32,
-        max_total_tokens: u32,
         block_size: u32,
         window_size: Option<u32>,
         speculate: u32,
@@ -53,8 +46,6 @@ impl Queue {
         // Launch background queue task
         tokio::spawn(queue_task(
             requires_padding,
-            max_input_length,
-            max_total_tokens,
             block_size,
             window_size,
             speculate,
@@ -106,21 +97,12 @@ impl Queue {
 // Background task responsible of the queue state
 async fn queue_task(
     requires_padding: bool,
-    max_input_length: u32,
-    max_total_tokens: u32,
     block_size: u32,
     window_size: Option<u32>,
     speculate: u32,
     mut receiver: mpsc::UnboundedReceiver<QueueCommand>,
 ) {
-    let mut state = State::new(
-        requires_padding,
-        max_input_length,
-        max_total_tokens,
-        block_size,
-        window_size,
-        speculate
-    );
+    let mut state = State::new(requires_padding, block_size, window_size, speculate);
 
     while let Some(cmd) = receiver.recv().await {
         match cmd {
@@ -145,104 +127,11 @@ async fn queue_task(
     }
 }
 
-#[derive(Debug)]
-struct IdentifiableEntry(u64, Entry);
-
-impl Eq for IdentifiableEntry {}
-
-impl PartialEq for IdentifiableEntry {
-    fn eq(&self, other: &Self) -> bool {
-        self.0 == other.0
-    }
-}
-
-impl Ord for IdentifiableEntry {
-    fn cmp(&self, other: &Self) -> std::cmp::Ordering {
-        let ordering = match self
-            .1
-            .request
-            .input_length
-            .cmp(&other.1.request.input_length)
-        {
-            std::cmp::Ordering::Equal => self.0.cmp(&other.0),
-            any => any,
-        };
-
-        // inverse to get min heap
-        return ordering.reverse();
-    }
-}
-
-impl PartialOrd for IdentifiableEntry {
-    fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
-        Some(self.cmp(other))
-    }
-}
-
-#[derive(Debug)]
-struct QueueImpl {
-    regular_entries: BinaryHeap<IdentifiableEntry>,
-    overdue_entries: BinaryHeap<IdentifiableEntry>,
-    overdue_threshold: Duration,
-}
-
-impl QueueImpl {
-    fn new(capacity: usize, overdue_threshold: Duration) -> Self {
-        Self {
-            regular_entries: BinaryHeap::with_capacity(capacity),
-            overdue_entries: BinaryHeap::with_capacity(capacity),
-            overdue_threshold,
-        }
-    }
-
-    fn update(&mut self) {
-        if self.regular_entries.is_empty() {
-            return;
-        }
-
-        let mut left = BinaryHeap::with_capacity(self.regular_entries.capacity());
-
-        for entry in self.regular_entries.drain() {
-            if entry.1.queue_time.elapsed() > self.overdue_threshold {
-                self.overdue_entries.push(entry);
-            } else {
-                left.push(entry);
-            }
-        }
-
-        self.regular_entries = left;
-    }
-
-    fn push(&mut self, entry: IdentifiableEntry) {
-        if entry.1.queue_time.elapsed() > self.overdue_threshold {
-            self.overdue_entries.push(entry);
-        } else {
-            self.regular_entries.push(entry);
-        }
-    }
-
-    fn pop(&mut self) -> Option<IdentifiableEntry> {
-        if !self.overdue_entries.is_empty() {
-            self.overdue_entries.pop()
-        } else {
-            self.regular_entries.pop()
-        }
-    }
-
-    fn is_empty(&self) -> bool {
-        self.regular_entries.is_empty() && self.overdue_entries.is_empty()
-    }
-
-    fn len(&self) -> usize {
-        self.regular_entries.len() + self.overdue_entries.len()
-    }
-}
-
 /// Queue State
 #[derive(Debug)]
 struct State {
-    /// Queue entries
-    entries: QueueImpl,
+    /// Queue entries organized in a Vec
+    entries: VecDeque<(u64, Entry)>,
 
     /// Id of the next entry
     next_id: u64,
@@ -253,12 +142,6 @@ struct State {
     /// Whether the model is using padding
     requires_padding: bool,
 
-    /// Maximum input length, required for padding scenario
-    max_input_length: u32,
-
-    /// Maximum input and output length, required for padding scenario
-    max_total_tokens: u32,
-
     /// Paged Attention block size
     block_size: u32,
 
@@ -272,25 +155,15 @@ struct State {
 impl State {
     fn new(
         requires_padding: bool,
-        max_input_length: u32,
-        max_total_tokens: u32,
         block_size: u32,
         window_size: Option<u32>,
         speculate: u32,
     ) -> Self {
-        let default_threshold: u64 = 120;
-        let threshold: u64 = match env::var("QUEUE_THRESHOLD_MS") {
-            Ok(val) => val.parse().unwrap_or(default_threshold),
-            Err(_) => default_threshold,
-        };
-
         Self {
-            entries: QueueImpl::new(128, Duration::from_millis(threshold)),
+            entries: VecDeque::with_capacity(128),
             next_id: 0,
             next_batch_id: 0,
             requires_padding,
-            max_input_length,
-            max_total_tokens,
             block_size,
             window_size,
             speculate,
@@ -304,7 +177,7 @@ impl State {
         entry.temp_span = Some(queue_span);
 
         // Push entry in the queue
-        self.entries.push(IdentifiableEntry(self.next_id, entry));
+        self.entries.push_back((self.next_id, entry));
         self.next_id += 1;
     }
 
@@ -329,7 +202,9 @@ impl State {
             }
         }
 
-        self.entries.update();
+        // Pad prefill_token_budget to be a multiple of block size
+        let prefill_token_budget =
+            ((prefill_token_budget + self.block_size - 1) / self.block_size) * self.block_size;
 
         // Create span for this batch to add context to inference calls
         let next_batch_span = info_span!(parent: None, "batch", batch_size = tracing::field::Empty);
@@ -339,11 +214,12 @@ impl State {
         let mut batch_entries =
             IntMap::with_capacity_and_hasher(self.entries.len(), BuildNoHashHasher::default());
 
+        let mut max_input_length = 0;
         let mut prefill_tokens: u32 = 0;
         let mut decode_tokens: u32 = 0;
 
         // Pop entries starting from the front of the queue
-        while let Some(IdentifiableEntry(id, mut entry)) = self.entries.pop() {
+        while let Some((id, mut entry)) = self.entries.pop_front() {
             // Filter entries where the response receiver was dropped (== entries where the request
             // was dropped by the client)
             if entry.response_tx.is_closed() {
@@ -355,7 +231,8 @@ impl State {
             if self.requires_padding {
                 // We pad to max input length in the Python shards
                 // We need to take these padding tokens into the equation
-                prefill_tokens = (batch_requests.len() + 1) as u32 * self.max_input_length;
+                max_input_length = max_input_length.max(entry.request.input_length);
+                prefill_tokens = (batch_requests.len() + 1) as u32 * max_input_length
             } else {
                 // pad to block size
                 prefill_tokens += ((entry.request.input_length + self.block_size - 1)
@@ -364,9 +241,7 @@ impl State {
             }
 
             if self.requires_padding {
-                // We pad to max total tokens in the Python shards
-                // We need to take these padding tokens into the equation
-                decode_tokens = (batch_requests.len() + 1) as u32 * (self.max_total_tokens - self.max_input_length);
+                decode_tokens += entry.request.stopping_parameters.max_new_tokens;
             } else {
                 let max_new_tokens = match self.window_size {
                     None => entry.request.stopping_parameters.max_new_tokens,
@@ -387,7 +262,7 @@ impl State {
                 // Entry is over budget
                 // Add it back to the front
                 tracing::debug!("Over budget: prefill_tokens={prefill_tokens} > {prefill_token_budget} || {prefill_tokens} + {decode_tokens} + {} > {token_budget}", self.speculate);
-                self.entries.push(IdentifiableEntry(id, entry));
+                self.entries.push_front((id, entry));
                 break;
             }
 
@@ -434,7 +309,7 @@ impl State {
                 for r in batch_requests.into_iter().rev() {
                     let id = r.id;
                     let entry = batch_entries.remove(&id).unwrap();
-                    self.entries.push(IdentifiableEntry(id, entry));
+                    self.entries.push_front((id, entry));
                 }
 
                 return None;
@@ -483,18 +358,6 @@ mod tests {
     };
     use tracing::info_span;
 
-    fn default_queue() -> Queue {
-        Queue::new(
-            true, 1, 2, 1, None, 0
-        )
-    }
-
-    fn default_state() -> State {
-        State::new(
-            true, 1, 2, 1, None, 0
-        )
-    }
-
     fn default_entry() -> (
         Entry,
         mpsc::UnboundedReceiver<Result<InferStreamResponse, InferError>>,
@@ -538,7 +401,7 @@ mod tests {
 
     #[test]
     fn test_append() {
-        let mut state = default_state();
+        let mut state = State::new(false, 1, None, 0);
         let (entry, _guard) = default_entry();
 
         assert_eq!(state.next_id, 0);
@@ -548,13 +411,13 @@ mod tests {
 
         assert_eq!(state.next_id, 1);
         assert_eq!(state.entries.len(), 1);
-        let id = state.entries.pop().unwrap().0;
+        let (id, _) = state.entries.remove(0).unwrap();
         assert_eq!(id, 0);
     }
 
     #[test]
     fn test_next_batch_empty() {
-        let mut state = default_state();
+        let mut state = State::new(false, 1, None, 0);
 
         assert!(state.next_batch(None, None, 1, 1).is_none());
         assert!(state.next_batch(Some(1), None, 1, 1).is_none());
@@ -562,13 +425,13 @@ mod tests {
 
     #[test]
     fn test_next_batch_min_size() {
-        let mut state = default_state();
+        let mut state = State::new(false, 1, None, 0);
         let (entry1, _guard1) = default_entry();
         let (entry2, _guard2) = default_entry();
         state.append(entry1);
         state.append(entry2);
 
-        let (entries, batch, _) = state.next_batch(None, None, 2, 4).unwrap();
+        let (entries, batch, _) = state.next_batch(None, None, 2, 2).unwrap();
         assert_eq!(entries.len(), 2);
         assert!(entries.contains_key(&0));
         assert!(entries.contains_key(&1));
@@ -588,13 +451,13 @@ mod tests {
 
         assert_eq!(state.next_id, 3);
         assert_eq!(state.entries.len(), 1);
-        let IdentifiableEntry(id, _) = state.entries.pop().unwrap();
+        let (id, _) = state.entries.remove(0).unwrap();
         assert_eq!(id, 2);
     }
 
     #[test]
     fn test_next_batch_max_size() {
-        let mut state = default_state();
+        let mut state = State::new(false, 1, None, 0);
         let (entry1, _guard1) = default_entry();
         let (entry2, _guard2) = default_entry();
         state.append(entry1);
@@ -614,13 +477,13 @@ mod tests {
 
     #[test]
     fn test_next_batch_token_budget() {
-        let mut state = default_state();
+        let mut state = State::new(false, 1, None, 0);
         let (entry1, _guard1) = default_entry();
         let (entry2, _guard2) = default_entry();
         state.append(entry1);
         state.append(entry2);
 
-        let (entries, batch, _) = state.next_batch(None, None, 1, 2).unwrap();
+        let (entries, batch, _) = state.next_batch(None, None, 1, 1).unwrap();
         assert_eq!(entries.len(), 1);
         assert!(entries.contains_key(&0));
         assert_eq!(batch.id, 0);
@@ -633,7 +496,7 @@ mod tests {
         let (entry3, _guard3) = default_entry();
         state.append(entry3);
 
-        let (entries, batch, _) = state.next_batch(None, None, 3, 6).unwrap();
+        let (entries, batch, _) = state.next_batch(None, None, 3, 3).unwrap();
         assert_eq!(entries.len(), 2);
         assert!(entries.contains_key(&1));
         assert!(entries.contains_key(&2));
@@ -647,14 +510,14 @@ mod tests {
 
     #[tokio::test]
     async fn test_queue_append() {
-        let queue = default_queue();
+        let queue = Queue::new(false, 1, None, 0);
         let (entry, _guard) = default_entry();
         queue.append(entry);
     }
 
     #[tokio::test]
     async fn test_queue_next_batch_empty() {
-        let queue = default_queue();
+        let queue = Queue::new(false, 1, None, 0);
 
         assert!(queue.next_batch(None, None, 1, 1).await.is_none());
         assert!(queue.next_batch(Some(1), None, 1, 1).await.is_none());
@@ -662,13 +525,13 @@ mod tests {
 
     #[tokio::test]
     async fn test_queue_next_batch_min_size() {
-        let queue = default_queue();
+        let queue = Queue::new(false, 1, None, 0);
         let (entry1, _guard1) = default_entry();
         let (entry2, _guard2) = default_entry();
         queue.append(entry1);
         queue.append(entry2);
 
-        let (entries, batch, _) = queue.next_batch(None, None, 2, 4).await.unwrap();
+        let (entries, batch, _) = queue.next_batch(None, None, 2, 2).await.unwrap();
         assert_eq!(entries.len(), 2);
         assert!(entries.contains_key(&0));
         assert!(entries.contains_key(&1));
@@ -685,7 +548,7 @@ mod tests {
         // Not enough token budget
         assert!(queue.next_batch(Some(1), None, 0, 0).await.is_none());
         // Ok
-        let (entries2, batch2, _) = queue.next_batch(Some(1), None, 2, 4).await.unwrap();
+        let (entries2, batch2, _) = queue.next_batch(Some(1), None, 2, 2).await.unwrap();
         assert_eq!(entries2.len(), 1);
         assert!(entries2.contains_key(&2));
         assert!(entries2.get(&2).unwrap().batch_time.is_some());
@@ -695,7 +558,7 @@ mod tests {
 
     #[tokio::test]
     async fn test_queue_next_batch_max_size() {
-        let queue = default_queue();
+        let queue = Queue::new(false, 1, None, 0);
         let (entry1, _guard1) = default_entry();
         let (entry2, _guard2) = default_entry();
         queue.append(entry1);
@@ -711,13 +574,13 @@ mod tests {
 
     #[tokio::test]
     async fn test_queue_next_batch_token_budget() {
-        let queue = default_queue();
+        let queue = Queue::new(false, 1, None, 0);
         let (entry1, _guard1) = default_entry();
         let (entry2, _guard2) = default_entry();
         queue.append(entry1);
         queue.append(entry2);
 
-        let (entries, batch, _) = queue.next_batch(None, None, 1, 2).await.unwrap();
+        let (entries, batch, _) = queue.next_batch(None, None, 1, 1).await.unwrap();
         assert_eq!(entries.len(), 1);
         assert!(entries.contains_key(&0));
         assert_eq!(batch.id, 0);
@@ -726,7 +589,7 @@ mod tests {
         let (entry3, _guard3) = default_entry();
         queue.append(entry3);
 
-        let (entries, batch, _) = queue.next_batch(None, None, 3, 6).await.unwrap();
+        let (entries, batch, _) = queue.next_batch(None, None, 3, 3).await.unwrap();
         assert_eq!(entries.len(), 2);
         assert!(entries.contains_key(&1));
         assert!(entries.contains_key(&2));
@@ -736,7 +599,7 @@ mod tests {
 
     #[tokio::test]
     async fn test_queue_next_batch_token_speculate() {
-        let queue = Queue::new(true, 1, 2, 1, None, 2);
+        let queue = Queue::new(false, 1, None, 2);
         let (entry1, _guard1) = default_entry();
         let (entry2, _guard2) = default_entry();
         queue.append(entry1);
@@ -755,7 +618,7 @@ mod tests {
 
     #[tokio::test]
     async fn test_queue_next_batch_dropped_receiver() {
-        let queue = default_queue();
+        let queue = Queue::new(false, 1, None, 0);
         let (entry, _) = default_entry();
         queue.append(entry);
 
diff --git a/router/src/server.rs b/router/src/server.rs
index 1edcc472d..e7570dedf 100644
--- a/router/src/server.rs
+++ b/router/src/server.rs
@@ -1,5 +1,3 @@
-/// Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
 use crate::config::Config;
 /// HTTP Server logic
 use crate::health::Health;
@@ -1493,8 +1491,6 @@ pub async fn run(
         max_batch_size,
         max_concurrent_requests,
         shard_info.requires_padding,
-        max_input_length as u32,
-        max_total_tokens as u32,
         shard_info.window_size,
         shard_info.speculate,
         generation_health,
diff --git a/router/src/validation.rs b/router/src/validation.rs
index ee48f7052..96b6cb271 100644
--- a/router/src/validation.rs
+++ b/router/src/validation.rs
@@ -1,12 +1,9 @@
-/// Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
 use crate::config::Config;
 /// Payload validation logic
 use crate::validation::ValidationError::{BestOfSampling, BestOfSeed, EmptyInput};
 use crate::{GenerateParameters, GenerateRequest, GrammarType};
 use jsonschema::{Draft, JSONSchema};
 use rand::{thread_rng, Rng};
-use std::{cmp, env};
 use serde_json::Value;
 use std::io::Cursor;
 use text_generation_client::{
@@ -34,7 +31,6 @@ pub struct Validation {
     disable_grammar_support: bool,
     /// Channel to communicate with the background tokenization task
     sender: Option<mpsc::UnboundedSender<TokenizerRequest>>,
-    skip_tokenizer_in_tgi: bool,
 }
 
 impl Validation {
@@ -77,10 +73,6 @@ impl Validation {
             None
         };
 
-        let skip_tokenizer_in_tgi = env::var("SKIP_TOKENIZER_IN_TGI")
-            .ok()
-            .map_or(false, |value| value.to_lowercase() == "true");
-
         Self {
             max_best_of,
             sender,
@@ -89,7 +81,6 @@ impl Validation {
             max_input_length,
             max_total_tokens,
             disable_grammar_support,
-            skip_tokenizer_in_tgi,
         }
     }
 
@@ -128,13 +119,10 @@ impl Validation {
         // If we have a fast tokenizer
         if let Some((encoding, inputs)) = self.tokenize(inputs.clone(), truncate).await? {
             // Create response channel
-            let input_length = if self.skip_tokenizer_in_tgi {
-                inputs.chars().filter(|&c| c == ',').count() + 1
+            let input_length = if let Some(truncate) = truncate {
+                std::cmp::min(encoding.len(), truncate)
             } else {
-                cmp::max(
-                    encoding.len(),
-                    truncate.unwrap_or(self.max_input_length)
-                )
+                encoding.len()
             };
 
             // Get total tokens
@@ -177,18 +165,17 @@ impl Validation {
             } else {
                 return Err(ValidationError::UnsetMaxNewTokens);
             };
-            let input_length = if self.skip_tokenizer_in_tgi {
-                inputs.chars().filter(|&c| c == ',').count() + 1
-            } else {
-                truncate.unwrap_or(self.max_input_length)
-            };
+            let mut input_length = truncate.unwrap_or(self.max_input_length);
 
+            // We don't have a tokenizer, therefore we have no idea how long is the query, let
+            // them through and hope for the best.
             // Validate MaxNewTokens
             if (input_length as u32 + max_new_tokens) > self.max_total_tokens as u32 {
-                return Err(ValidationError::MaxNewTokens(
-                    self.max_total_tokens - self.max_input_length,
-                    max_new_tokens,
-                ));
+                input_length = input_length.saturating_sub(max_new_tokens as usize);
+                // return Err(ValidationError::MaxNewTokens(
+                //     self.max_total_tokens - self.max_input_length,
+                //     max_new_tokens,
+                // ));
             }
 
             Ok((inputs, input_length, max_new_tokens))
@@ -248,14 +235,6 @@ impl Validation {
             return Err(ValidationError::FrequencyPenalty);
         }
 
-        // TODO: enable watermark with fp8 quantization
-        let quantization_enabled = env::var("QUANT_CONFIG")
-            .ok()
-            .map_or(false, |value| !value.is_empty());
-        if watermark && quantization_enabled {
-            return Err(ValidationError::WatermarkWithQuantization);
-        }
-
         // Different because the proto default value is not a valid value
         // for the user
         let top_p = top_p
@@ -730,8 +709,6 @@ pub enum ValidationError {
     InvalidImageContent(String),
     #[error("Could not fetch image: {0}")]
     FailedFetchImage(#[from] reqwest::Error),
-    #[error("`watermark` = true is not allowed with FP8 quantization.")]
-    WatermarkWithQuantization,
 }
 
 #[cfg(test)]
@@ -768,7 +745,8 @@ mod tests {
             .validate_input("Hello".to_string(), None, Some(max_new_tokens))
             .await
         {
-            Err(ValidationError::MaxNewTokens(1, 10)) => (),
+            // Err(ValidationError::MaxNewTokens(1, 10)) => (),
+            Ok((_s, 0, 10)) => (),
             r => panic!("Unexpected not max new tokens: {r:?}"),
         }
     }
@@ -801,7 +779,7 @@ mod tests {
             .validate_input("Hello".to_string(), None, Some(max_new_tokens))
             .await
         {
-            Err(ValidationError::MaxTotalTokens(6, 5, 10)) => (),
+            Err(ValidationError::MaxTotalTokens(6, 1, 10)) => (),
             _ => panic!("Unexpected not max new tokens"),
         }
     }
diff --git a/server/Makefile b/server/Makefile
index 7e38eb12b..32d017095 100644
--- a/server/Makefile
+++ b/server/Makefile
@@ -19,18 +19,12 @@ gen-server:
 
 install: gen-server
 	pip install pip --upgrade
-	pip install -r requirements.txt
-	pip install -e "."
+	pip install -r requirements_cuda.txt
+	pip install -e ".[bnb, accelerate, quantize, peft, outlines]"
 
 run-dev:
 	SAFETENSORS_FAST_GPU=1 python -m torch.distributed.run --nproc_per_node=2 text_generation_server/cli.py serve bigscience/bloom-560m --sharded
 
-install-poetry:
-	curl -sSL https://install.python-poetry.org | python3 -
-
-update-lock:
-	rm poetry.lock
-	poetry lock --no-update
-
 export-requirements:
-	poetry export -o requirements.txt --without-hashes
+	poetry export -o requirements_cuda.txt --without-hashes
+	poetry export -o requirements_rocm.txt --without-hashes
diff --git a/server/dill-0.3.7-patch.sh b/server/dill-0.3.7-patch.sh
deleted file mode 100644
index ad8c8be58..000000000
--- a/server/dill-0.3.7-patch.sh
+++ /dev/null
@@ -1,91 +0,0 @@
-#!/bin/bash
-git clone -b dill-0.3.7 https://github.com/uqfoundation/dill.git
-pushd dill
-cat <<EOF > dill-0.3.7.patch
-diff --git a/dill/_dill.py b/dill/_dill.py
-index d0cf543..f6eb662 100644
---- a/dill/_dill.py
-+++ b/dill/_dill.py
-@@ -69,7 +69,15 @@ TypeType = type # 'new-style' classes #XXX: unregistered
- XRangeType = range
- from types import MappingProxyType as DictProxyType, new_class
- from pickle import DEFAULT_PROTOCOL, HIGHEST_PROTOCOL, PickleError, PicklingError, UnpicklingError
--import __main__ as _main_module
-+class _LazyMainModule(object):
-+    _module = None
-+    @property
-+    def module(self):
-+        if self._module is None:
-+            import __main__ as _m_module
-+            self._module = _m_module
-+        return self._module
-+_main_module = _LazyMainModule()
- import marshal
- import gc
- # import zlib
-@@ -353,7 +361,7 @@ class Pickler(StockPickler):
-         _fmode = kwds.pop('fmode', None)
-         _recurse = kwds.pop('recurse', None)
-         StockPickler.__init__(self, file, *args, **kwds)
--        self._main = _main_module
-+        self._main = _main_module.module
-         self._diff_cache = {}
-         self._byref = settings['byref'] if _byref is None else _byref
-         self._strictio = False #_strictio
-@@ -435,12 +443,12 @@ class Unpickler(StockUnpickler):
-         settings = Pickler.settings
-         _ignore = kwds.pop('ignore', None)
-         StockUnpickler.__init__(self, *args, **kwds)
--        self._main = _main_module
-+        self._main = _main_module.module
-         self._ignore = settings['ignore'] if _ignore is None else _ignore
- 
-     def load(self): #NOTE: if settings change, need to update attributes
-         obj = StockUnpickler.load(self)
--        if type(obj).__module__ == getattr(_main_module, '__name__', '__main__'):
-+        if type(obj).__module__ == getattr(self._main, '__name__', '__main__'):
-             if not self._ignore:
-                 # point obj class to main
-                 try: obj.__class__ = getattr(self._main, type(obj).__name__)
-@@ -1194,11 +1202,11 @@ def save_module_dict(pickler, obj):
-         logger.trace(pickler, "D1: %s", _repr_dict(obj)) # obj
-         pickler.write(bytes('c__builtin__\n__main__\n', 'UTF-8'))
-         logger.trace(pickler, "# D1")
--    elif (not is_dill(pickler, child=False)) and (obj == _main_module.__dict__):
-+    elif (not is_dill(pickler, child=False)) and (obj == _main_module.module.__dict__):
-         logger.trace(pickler, "D3: %s", _repr_dict(obj)) # obj
-         pickler.write(bytes('c__main__\n__dict__\n', 'UTF-8'))  #XXX: works in general?
-         logger.trace(pickler, "# D3")
--    elif '__name__' in obj and obj != _main_module.__dict__ \\
-+    elif '__name__' in obj and obj != _main_module.module.__dict__ \\
-             and type(obj['__name__']) is str \\
-             and obj is getattr(_import_module(obj['__name__'],True), '__dict__', None):
-         logger.trace(pickler, "D4: %s", _repr_dict(obj)) # obj
-diff --git a/dill/session.py b/dill/session.py
-index 74234ab..1be8d89 100644
---- a/dill/session.py
-+++ b/dill/session.py
-@@ -233,7 +233,7 @@ def dump_module(
-     protocol = settings['protocol']
-     main = module
-     if main is None:
--        main = _main_module
-+        main = _main_module.module
-     elif isinstance(main, str):
-         main = _import_module(main)
-     if not isinstance(main, ModuleType):
-@@ -501,7 +501,7 @@ def load_module(
-             pass
-     assert loaded is main
-     _restore_modules(unpickler, main)
--    if main is _main_module or main is module:
-+    if main is _main_module.module or main is module:
-         return None
-     else:
-         return main
-
-EOF
-git apply dill-0.3.7.patch
-python -m pip install .
-popd
-rm -fr dill
diff --git a/server/dill-0.3.8-patch.sh b/server/dill-0.3.8-patch.sh
deleted file mode 100644
index da263960f..000000000
--- a/server/dill-0.3.8-patch.sh
+++ /dev/null
@@ -1,91 +0,0 @@
-#!/bin/bash
-git clone -b 0.3.8 https://github.com/uqfoundation/dill.git
-pushd dill
-cat <<EOF > dill-0.3.8.patch
-diff --git a/dill/_dill.py b/dill/_dill.py
-index d42432f..1d251e6 100644
---- a/dill/_dill.py
-+++ b/dill/_dill.py
-@@ -69,7 +69,15 @@ TypeType = type # 'new-style' classes #XXX: unregistered
- XRangeType = range
- from types import MappingProxyType as DictProxyType, new_class
- from pickle import DEFAULT_PROTOCOL, HIGHEST_PROTOCOL, PickleError, PicklingError, UnpicklingError
--import __main__ as _main_module
-+class _LazyMainModule(object):
-+    _module = None
-+    @property
-+    def module(self):
-+        if self._module is None:
-+            import __main__ as _m_module
-+            self._module = _m_module
-+        return self._module
-+_main_module = _LazyMainModule()
- import marshal
- import gc
- # import zlib
-@@ -355,7 +363,7 @@ class Pickler(StockPickler):
-         _fmode = kwds.pop('fmode', None)
-         _recurse = kwds.pop('recurse', None)
-         StockPickler.__init__(self, file, *args, **kwds)
--        self._main = _main_module
-+        self._main = _main_module.module
-         self._diff_cache = {}
-         self._byref = settings['byref'] if _byref is None else _byref
-         self._strictio = False #_strictio
-@@ -437,12 +445,12 @@ class Unpickler(StockUnpickler):
-         settings = Pickler.settings
-         _ignore = kwds.pop('ignore', None)
-         StockUnpickler.__init__(self, *args, **kwds)
--        self._main = _main_module
-+        self._main = _main_module.module
-         self._ignore = settings['ignore'] if _ignore is None else _ignore
- 
-     def load(self): #NOTE: if settings change, need to update attributes
-         obj = StockUnpickler.load(self)
--        if type(obj).__module__ == getattr(_main_module, '__name__', '__main__'):
-+        if type(obj).__module__ == getattr(self._main, '__name__', '__main__'):
-             if not self._ignore:
-                 # point obj class to main
-                 try: obj.__class__ = getattr(self._main, type(obj).__name__)
-@@ -1199,11 +1207,11 @@ def save_module_dict(pickler, obj):
-         logger.trace(pickler, "D1: %s", _repr_dict(obj)) # obj
-         pickler.write(bytes('c__builtin__\n__main__\n', 'UTF-8'))
-         logger.trace(pickler, "# D1")
--    elif (not is_dill(pickler, child=False)) and (obj == _main_module.__dict__):
-+    elif (not is_dill(pickler, child=False)) and (obj == _main_module.module.__dict__):
-         logger.trace(pickler, "D3: %s", _repr_dict(obj)) # obj
-         pickler.write(bytes('c__main__\n__dict__\n', 'UTF-8'))  #XXX: works in general?
-         logger.trace(pickler, "# D3")
--    elif '__name__' in obj and obj != _main_module.__dict__ \\
-+    elif '__name__' in obj and obj != _main_module.module.__dict__ \\
-             and type(obj['__name__']) is str \\
-             and obj is getattr(_import_module(obj['__name__'],True), '__dict__', None):
-         logger.trace(pickler, "D4: %s", _repr_dict(obj)) # obj
-diff --git a/dill/session.py b/dill/session.py
-index e91068a..a921b43 100644
---- a/dill/session.py
-+++ b/dill/session.py
-@@ -233,7 +233,7 @@ def dump_module(
-     protocol = settings['protocol']
-     main = module
-     if main is None:
--        main = _main_module
-+        main = _main_module.module
-     elif isinstance(main, str):
-         main = _import_module(main)
-     if not isinstance(main, ModuleType):
-@@ -501,7 +501,7 @@ def load_module(
-             pass
-     assert loaded is main
-     _restore_modules(unpickler, main)
--    if main is _main_module or main is module:
-+    if main is _main_module.module or main is module:
-         return None
-     else:
-         return main
-
-EOF
-git apply dill-0.3.8.patch
-python -m pip install .
-popd
-rm -fr dill
\ No newline at end of file
diff --git a/server/poetry.lock b/server/poetry.lock
index 8dfc122cf..2bf4ca221 100644
--- a/server/poetry.lock
+++ b/server/poetry.lock
@@ -1,19 +1,19 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "accelerate"
-version = "0.33.0"
+version = "0.29.3"
 description = "Accelerate"
-optional = false
+optional = true
 python-versions = ">=3.8.0"
 files = [
-    {file = "accelerate-0.33.0-py3-none-any.whl", hash = "sha256:0a7f33d60ba09afabd028d4f0856dd19c5a734b7a596d637d9dd6e3d0eadbaf3"},
-    {file = "accelerate-0.33.0.tar.gz", hash = "sha256:11ba481ed6ea09191775df55ce464aeeba67a024bd0261a44b77b30fb439e26a"},
+    {file = "accelerate-0.29.3-py3-none-any.whl", hash = "sha256:99d633d4b6126817c5e554487406748be95c8d1d1e659dd2fd60657e35f532dd"},
+    {file = "accelerate-0.29.3.tar.gz", hash = "sha256:1a5a845b06b24b41736b219b2b20fd021ca5dff4070a252445fd6de736e347ac"},
 ]
 
 [package.dependencies]
-huggingface-hub = ">=0.21.0"
-numpy = ">=1.17,<2.0.0"
+huggingface-hub = "*"
+numpy = ">=1.17"
 packaging = ">=20.0"
 psutil = "*"
 pyyaml = "*"
@@ -21,129 +21,101 @@ safetensors = ">=0.3.1"
 torch = ">=1.10.0"
 
 [package.extras]
-deepspeed = ["deepspeed (<=0.14.0)"]
-dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "diffusers", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.2.1,<0.3.0)", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "deepspeed", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.2.1,<0.3.0)", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
 quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.2.1,<0.3.0)"]
 rich = ["rich"]
 sagemaker = ["sagemaker"]
-test-dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+test-dev = ["bitsandbytes", "datasets", "deepspeed", "evaluate", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
 test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist"]
 test-trackers = ["comet-ml", "dvclive", "tensorboard", "wandb"]
-testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
-
-[[package]]
-name = "aiohappyeyeballs"
-version = "2.4.0"
-description = "Happy Eyeballs for asyncio"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "aiohappyeyeballs-2.4.0-py3-none-any.whl", hash = "sha256:7ce92076e249169a13c2f49320d1967425eaf1f407522d707d59cac7628d62bd"},
-    {file = "aiohappyeyeballs-2.4.0.tar.gz", hash = "sha256:55a1714f084e63d49639800f95716da97a1f173d46a16dfcfda0016abb93b6b2"},
-]
+testing = ["bitsandbytes", "datasets", "deepspeed", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
 
 [[package]]
 name = "aiohttp"
-version = "3.10.5"
+version = "3.9.5"
 description = "Async http client/server framework (asyncio)"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "aiohttp-3.10.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:18a01eba2574fb9edd5f6e5fb25f66e6ce061da5dab5db75e13fe1558142e0a3"},
-    {file = "aiohttp-3.10.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:94fac7c6e77ccb1ca91e9eb4cb0ac0270b9fb9b289738654120ba8cebb1189c6"},
-    {file = "aiohttp-3.10.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2f1f1c75c395991ce9c94d3e4aa96e5c59c8356a15b1c9231e783865e2772699"},
-    {file = "aiohttp-3.10.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4f7acae3cf1a2a2361ec4c8e787eaaa86a94171d2417aae53c0cca6ca3118ff6"},
-    {file = "aiohttp-3.10.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:94c4381ffba9cc508b37d2e536b418d5ea9cfdc2848b9a7fea6aebad4ec6aac1"},
-    {file = "aiohttp-3.10.5-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c31ad0c0c507894e3eaa843415841995bf8de4d6b2d24c6e33099f4bc9fc0d4f"},
-    {file = "aiohttp-3.10.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0912b8a8fadeb32ff67a3ed44249448c20148397c1ed905d5dac185b4ca547bb"},
-    {file = "aiohttp-3.10.5-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0d93400c18596b7dc4794d48a63fb361b01a0d8eb39f28800dc900c8fbdaca91"},
-    {file = "aiohttp-3.10.5-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:d00f3c5e0d764a5c9aa5a62d99728c56d455310bcc288a79cab10157b3af426f"},
-    {file = "aiohttp-3.10.5-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:d742c36ed44f2798c8d3f4bc511f479b9ceef2b93f348671184139e7d708042c"},
-    {file = "aiohttp-3.10.5-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:814375093edae5f1cb31e3407997cf3eacefb9010f96df10d64829362ae2df69"},
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-    {file = "aiohttp-3.10.5-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:d9a487ef090aea982d748b1b0d74fe7c3950b109df967630a20584f9a99c0683"},
-    {file = "aiohttp-3.10.5-cp310-cp310-win32.whl", hash = "sha256:d9ef084e3dc690ad50137cc05831c52b6ca428096e6deb3c43e95827f531d5ef"},
-    {file = "aiohttp-3.10.5-cp310-cp310-win_amd64.whl", hash = "sha256:66bf9234e08fe561dccd62083bf67400bdbf1c67ba9efdc3dac03650e97c6088"},
-    {file = "aiohttp-3.10.5-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:8c6a4e5e40156d72a40241a25cc226051c0a8d816610097a8e8f517aeacd59a2"},
-    {file = "aiohttp-3.10.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:2c634a3207a5445be65536d38c13791904fda0748b9eabf908d3fe86a52941cf"},
-    {file = "aiohttp-3.10.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4aff049b5e629ef9b3e9e617fa6e2dfeda1bf87e01bcfecaf3949af9e210105e"},
-    {file = "aiohttp-3.10.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1942244f00baaacaa8155eca94dbd9e8cc7017deb69b75ef67c78e89fdad3c77"},
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 ]
 
 [package.dependencies]
-aiohappyeyeballs = ">=2.3.0"
 aiosignal = ">=1.1.2"
 async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
@@ -152,13 +124,13 @@ multidict = ">=4.5,<7.0"
 yarl = ">=1.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
 version = "1.3.1"
 description = "aiosignal: a list of registered asynchronous callbacks"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "aiosignal-1.3.1-py3-none-any.whl", hash = "sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17"},
@@ -183,7 +155,7 @@ files = [
 name = "async-timeout"
 version = "4.0.3"
 description = "Timeout context manager for asyncio programs"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f"},
@@ -192,22 +164,22 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "24.2.0"
+version = "23.2.0"
 description = "Classes Without Boilerplate"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"},
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+    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
+    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
 ]
 
 [package.extras]
-benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
-cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
-dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
-docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
-tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
-tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
+cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
+dev = ["attrs[tests]", "pre-commit"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
+tests = ["attrs[tests-no-zope]", "zope-interface"]
+tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
 
 [[package]]
 name = "backoff"
@@ -220,15 +192,34 @@ files = [
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 ]
 
+[[package]]
+name = "bitsandbytes"
+version = "0.43.1"
+description = "k-bit optimizers and matrix multiplication routines."
+optional = true
+python-versions = "*"
+files = [
+    {file = "bitsandbytes-0.43.1-py3-none-manylinux_2_24_x86_64.whl", hash = "sha256:a81c826d576d6d691c7b4a7491c8fdc0f37f769795d6ca2e54afa605d2c260a3"},
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+]
+
+[package.dependencies]
+numpy = "*"
+torch = "*"
+
+[package.extras]
+benchmark = ["matplotlib", "pandas"]
+test = ["scipy"]
+
 [[package]]
 name = "certifi"
-version = "2024.7.4"
+version = "2024.2.2"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
-    {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
+    {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
+    {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
 ]
 
 [[package]]
@@ -366,66 +357,47 @@ files = [
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 ]
 
-[[package]]
-name = "coloredlogs"
-version = "15.0.1"
-description = "Colored terminal output for Python's logging module"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-files = [
-    {file = "coloredlogs-15.0.1-py2.py3-none-any.whl", hash = "sha256:612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934"},
-    {file = "coloredlogs-15.0.1.tar.gz", hash = "sha256:7c991aa71a4577af2f82600d8f8f3a89f936baeaf9b50a9c197da014e5bf16b0"},
-]
-
-[package.dependencies]
-humanfriendly = ">=9.1"
-
-[package.extras]
-cron = ["capturer (>=2.4)"]
-
 [[package]]
 name = "datasets"
-version = "2.21.0"
+version = "2.14.4"
 description = "HuggingFace community-driven open-source library of datasets"
-optional = false
+optional = true
 python-versions = ">=3.8.0"
 files = [
-    {file = "datasets-2.21.0-py3-none-any.whl", hash = "sha256:25e4e097110ce28824b746a107727ada94024cba11db8bc588d468414692b65a"},
-    {file = "datasets-2.21.0.tar.gz", hash = "sha256:998f85a8460f1bd982e5bd058f8a0808eef424249e3df1e8cdd594ccd0dc8ba2"},
+    {file = "datasets-2.14.4-py3-none-any.whl", hash = "sha256:29336bd316a7d827ccd4da2236596279b20ca2ac78f64c04c9483da7cbc2459b"},
+    {file = "datasets-2.14.4.tar.gz", hash = "sha256:ef29c2b5841de488cd343cfc26ab979bff77efa4d2285af51f1ad7db5c46a83b"},
 ]
 
 [package.dependencies]
 aiohttp = "*"
-dill = ">=0.3.0,<0.3.9"
-filelock = "*"
-fsspec = {version = ">=2023.1.0,<=2024.6.1", extras = ["http"]}
-huggingface-hub = ">=0.21.2"
+dill = ">=0.3.0,<0.3.8"
+fsspec = {version = ">=2021.11.1", extras = ["http"]}
+huggingface-hub = ">=0.14.0,<1.0.0"
 multiprocess = "*"
 numpy = ">=1.17"
 packaging = "*"
 pandas = "*"
-pyarrow = ">=15.0.0"
+pyarrow = ">=8.0.0"
 pyyaml = ">=5.1"
-requests = ">=2.32.2"
-tqdm = ">=4.66.3"
+requests = ">=2.19.0"
+tqdm = ">=4.62.1"
 xxhash = "*"
 
 [package.extras]
-apache-beam = ["apache-beam (>=2.26.0)"]
-audio = ["librosa", "soundfile (>=0.12.1)", "soxr (>=0.4.0)"]
+apache-beam = ["apache-beam (>=2.26.0,<2.44.0)"]
+audio = ["librosa", "soundfile (>=0.12.1)"]
 benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
-dev = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0)", "sqlalchemy", "tensorflow (>=2.16.0)", "tensorflow (>=2.6.0)", "tensorflow (>=2.6.0)", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "transformers (>=4.42.0)", "typing-extensions (>=4.6.1)", "zstandard"]
-docs = ["s3fs", "tensorflow (>=2.6.0)", "torch", "transformers"]
-jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
-metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk (<3.8.2)", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
-quality = ["ruff (>=0.3.0)"]
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+docs = ["s3fs", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos", "torch", "transformers"]
+jax = ["jax (>=0.2.8,!=0.3.2,<=0.3.25)", "jaxlib (>=0.1.65,<=0.3.25)"]
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+quality = ["black (>=23.1,<24.0)", "pyyaml (>=5.3.1)", "ruff (>=0.0.241)"]
 s3 = ["s3fs"]
-tensorflow = ["tensorflow (>=2.6.0)"]
-tensorflow-gpu = ["tensorflow (>=2.6.0)"]
-tests = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0)", "sqlalchemy", "tensorflow (>=2.16.0)", "tensorflow (>=2.6.0)", "tiktoken", "torch (>=2.0.0)", "transformers (>=4.42.0)", "typing-extensions (>=4.6.1)", "zstandard"]
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+tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos"]
+tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
+tests = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0,<2.44.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy (<2.0.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "transformers", "zstandard"]
 torch = ["torch"]
-vision = ["Pillow (>=9.4.0)"]
+vision = ["Pillow (>=6.2.1)"]
 
 [[package]]
 name = "deprecated"
@@ -444,50 +416,19 @@ wrapt = ">=1.10,<2"
 [package.extras]
 dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
 
-[[package]]
-name = "diffusers"
-version = "0.29.2"
-description = "State-of-the-art diffusion in PyTorch and JAX."
-optional = false
-python-versions = ">=3.8.0"
-files = [
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-]
-
-[package.dependencies]
-filelock = "*"
-huggingface-hub = ">=0.23.2"
-importlib-metadata = "*"
-numpy = "*"
-Pillow = "*"
-regex = "!=2019.12.17"
-requests = "*"
-safetensors = ">=0.3.1"
-
-[package.extras]
-dev = ["GitPython (<3.1.19)", "Jinja2", "accelerate (>=0.29.3)", "compel (==0.1.8)", "datasets", "flax (>=0.4.1)", "hf-doc-builder (>=0.3.0)", "invisible-watermark (>=0.2.0)", "isort (>=5.5.4)", "jax (>=0.4.1)", "jaxlib (>=0.4.1)", "k-diffusion (>=0.0.12)", "librosa", "parameterized", "peft (>=0.6.0)", "protobuf (>=3.20.3,<4)", "pytest", "pytest-timeout", "pytest-xdist", "requests-mock (==1.10.0)", "ruff (==0.1.5)", "safetensors (>=0.3.1)", "scipy", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "torch (>=1.4)", "torchvision", "transformers (>=4.25.1)", "urllib3 (<=2.0.0)"]
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-flax = ["flax (>=0.4.1)", "jax (>=0.4.1)", "jaxlib (>=0.4.1)"]
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-test = ["GitPython (<3.1.19)", "Jinja2", "compel (==0.1.8)", "datasets", "invisible-watermark (>=0.2.0)", "k-diffusion (>=0.0.12)", "librosa", "parameterized", "pytest", "pytest-timeout", "pytest-xdist", "requests-mock (==1.10.0)", "safetensors (>=0.3.1)", "scipy", "sentencepiece (>=0.1.91,!=0.1.92)", "torchvision", "transformers (>=4.25.1)"]
-torch = ["accelerate (>=0.29.3)", "torch (>=1.4)"]
-training = ["Jinja2", "accelerate (>=0.29.3)", "datasets", "peft (>=0.6.0)", "protobuf (>=3.20.3,<4)", "tensorboard"]
-
 [[package]]
 name = "dill"
-version = "0.3.8"
+version = "0.3.7"
 description = "serialize all of Python"
-optional = false
-python-versions = ">=3.8"
+optional = true
+python-versions = ">=3.7"
 files = [
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-    {file = "dill-0.3.8.tar.gz", hash = "sha256:3ebe3c479ad625c4553aca177444d89b486b1d84982eeacded644afc0cf797ca"},
+    {file = "dill-0.3.7-py3-none-any.whl", hash = "sha256:76b122c08ef4ce2eedcd4d1abd8e641114bfc6c2867f49f3c41facf65bf19f5e"},
+    {file = "dill-0.3.7.tar.gz", hash = "sha256:cc1c8b182eb3013e24bd475ff2e9295af86c1a38eb1aff128dac8962a9ce3c03"},
 ]
 
 [package.extras]
 graph = ["objgraph (>=1.7.2)"]
-profile = ["gprof2dot (>=2022.7.29)"]
 
 [[package]]
 name = "diskcache"
@@ -500,15 +441,26 @@ files = [
     {file = "diskcache-5.6.3.tar.gz", hash = "sha256:2c3a3fa2743d8535d832ec61c2054a1641f41775aa7c556758a109941e33e4fc"},
 ]
 
+[[package]]
+name = "einops"
+version = "0.6.1"
+description = "A new flavour of deep learning operations"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "einops-0.6.1.tar.gz", hash = "sha256:f95f8d00f4ded90dbc4b19b6f98b177332614b0357dde66997f3ae5d474dc8c8"},
+]
+
 [[package]]
 name = "exceptiongroup"
-version = "1.2.2"
+version = "1.2.1"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "exceptiongroup-1.2.1-py3-none-any.whl", hash = "sha256:5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad"},
+    {file = "exceptiongroup-1.2.1.tar.gz", hash = "sha256:a4785e48b045528f5bfe627b6ad554ff32def154f42372786903b7abcfe1aa16"},
 ]
 
 [package.extras]
@@ -516,25 +468,25 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "filelock"
-version = "3.15.4"
+version = "3.14.0"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "filelock-3.15.4-py3-none-any.whl", hash = "sha256:6ca1fffae96225dab4c6eaf1c4f4f28cd2568d3ec2a44e15a08520504de468e7"},
-    {file = "filelock-3.15.4.tar.gz", hash = "sha256:2207938cbc1844345cb01a5a95524dae30f0ce089eba5b00378295a17e3e90cb"},
+    {file = "filelock-3.14.0-py3-none-any.whl", hash = "sha256:43339835842f110ca7ae60f1e1c160714c5a6afd15a2873419ab185334975c0f"},
+    {file = "filelock-3.14.0.tar.gz", hash = "sha256:6ea72da3be9b8c82afd3edcf99f2fffbb5076335a5ae4d03248bb5b6c3eae78a"},
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+testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
 typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
 name = "frozenlist"
 version = "1.4.1"
 description = "A list-like structure which implements collections.abc.MutableSequence"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
     {file = "frozenlist-1.4.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:f9aa1878d1083b276b0196f2dfbe00c9b7e752475ed3b682025ff20c1c1f51ac"},
@@ -618,13 +570,13 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2024.6.1"
+version = "2024.5.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "fsspec-2024.5.0.tar.gz", hash = "sha256:1d021b0b0f933e3b3029ed808eb400c08ba101ca2de4b3483fbc9ca23fcee94a"},
 ]
 
 [package.dependencies]
@@ -636,7 +588,6 @@ adl = ["adlfs"]
 arrow = ["pyarrow (>=1)"]
 dask = ["dask", "distributed"]
 dev = ["pre-commit", "ruff"]
-doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
 dropbox = ["dropbox", "dropboxdrivefs", "requests"]
 full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
 fuse = ["fusepy"]
@@ -660,17 +611,17 @@ tqdm = ["tqdm"]
 
 [[package]]
 name = "googleapis-common-protos"
-version = "1.63.2"
+version = "1.63.0"
 description = "Common protobufs used in Google APIs"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "googleapis-common-protos-1.63.0.tar.gz", hash = "sha256:17ad01b11d5f1d0171c06d3ba5c04c54474e883b66b949722b4938ee2694ef4e"},
+    {file = "googleapis_common_protos-1.63.0-py2.py3-none-any.whl", hash = "sha256:ae45f75702f7c08b541f750854a678bd8f534a1a6bace6afe975f1d0a82d6632"},
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 [package.dependencies]
-protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0"
+protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0.dev0"
 
 [package.extras]
 grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
@@ -694,226 +645,242 @@ testing = ["protobuf (>=4.21.9)"]
 
 [[package]]
 name = "grpcio"
-version = "1.66.0"
+version = "1.64.0"
 description = "HTTP/2-based RPC framework"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
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-protobuf = ">=3.12.0,<4.0dev"
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 setuptools = "*"
 
 [[package]]
 name = "hf-transfer"
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 python-versions = ">=3.7"
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 ]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.24.6"
+version = "0.23.1"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "huggingface_hub-0.24.6-py3-none-any.whl", hash = "sha256:a990f3232aa985fe749bc9474060cbad75e8b2f115f6665a9fda5b9c97818970"},
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 ]
 
 [package.dependencies]
@@ -926,63 +893,30 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 cli = ["InquirerPy (==0.3.4)"]
-dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
 hf-transfer = ["hf-transfer (>=0.1.4)"]
 inference = ["aiohttp", "minijinja (>=1.0)"]
-quality = ["mypy (==1.5.1)", "ruff (>=0.5.0)"]
+quality = ["mypy (==1.5.1)", "ruff (>=0.3.0)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
 tensorflow-testing = ["keras (<3.0)", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
-torch = ["safetensors[torch]", "torch"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+torch = ["safetensors", "torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
-[[package]]
-name = "humanfriendly"
-version = "10.0"
-description = "Human friendly output for text interfaces using Python"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-files = [
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-]
-
-[package.dependencies]
-pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_version >= \"3.8\""}
-
 [[package]]
 name = "idna"
-version = "3.8"
+version = "3.7"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.5"
 files = [
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+    {file = "idna-3.7-py3-none-any.whl", hash = "sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"},
+    {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
 ]
 
-[[package]]
-name = "importlib-metadata"
-version = "8.4.0"
-description = "Read metadata from Python packages"
-optional = false
-python-versions = ">=3.8"
-files = [
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-]
-
-[package.dependencies]
-zipp = ">=0.5"
-
-[package.extras]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-perf = ["ipython"]
-test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
-
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -994,6 +928,20 @@ files = [
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 
+[[package]]
+name = "intel-openmp"
+version = "2021.4.0"
+description = "Intel OpenMP* Runtime Library"
+optional = true
+python-versions = "*"
+files = [
+    {file = "intel_openmp-2021.4.0-py2.py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.whl", hash = "sha256:41c01e266a7fdb631a7609191709322da2bbf24b252ba763f125dd651bcc7675"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:3b921236a38384e2016f0f3d65af6732cf2c12918087128a9163225451e776f2"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:e2240ab8d01472fed04f3544a878cda5da16c26232b7ea1b59132dbfb48b186e"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-win32.whl", hash = "sha256:6e863d8fd3d7e8ef389d52cf97a50fe2afe1a19247e8c0d168ce021546f96fc9"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-win_amd64.whl", hash = "sha256:eef4c8bcc8acefd7f5cd3b9384dbf73d59e2c99fc56545712ded913f43c4a94f"},
+]
+
 [[package]]
 name = "interegular"
 version = "0.3.3"
@@ -1009,7 +957,7 @@ files = [
 name = "jinja2"
 version = "3.1.4"
 description = "A very fast and expressive template engine."
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "jinja2-3.1.4-py3-none-any.whl", hash = "sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d"},
@@ -1026,7 +974,7 @@ i18n = ["Babel (>=2.7)"]
 name = "joblib"
 version = "1.4.2"
 description = "Lightweight pipelining with Python functions"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
     {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
@@ -1035,13 +983,13 @@ files = [
 
 [[package]]
 name = "jsonschema"
-version = "4.23.0"
+version = "4.22.0"
 description = "An implementation of JSON Schema validation for Python"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "jsonschema-4.23.0-py3-none-any.whl", hash = "sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566"},
-    {file = "jsonschema-4.23.0.tar.gz", hash = "sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4"},
+    {file = "jsonschema-4.22.0-py3-none-any.whl", hash = "sha256:ff4cfd6b1367a40e7bc6411caec72effadd3db0bbe5017de188f2d6108335802"},
+    {file = "jsonschema-4.22.0.tar.gz", hash = "sha256:5b22d434a45935119af990552c862e5d6d564e8f6601206b305a61fdf661a2b7"},
 ]
 
 [package.dependencies]
@@ -1052,7 +1000,7 @@ rpds-py = ">=0.7.1"
 
 [package.extras]
 format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
-format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=24.6.0)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"]
 
 [[package]]
 name = "jsonschema-specifications"
@@ -1070,13 +1018,13 @@ referencing = ">=0.31.0"
 
 [[package]]
 name = "lark"
-version = "1.2.2"
+version = "1.1.9"
 description = "a modern parsing library"
 optional = true
-python-versions = ">=3.8"
+python-versions = ">=3.6"
 files = [
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+    {file = "lark-1.1.9-py3-none-any.whl", hash = "sha256:a0dd3a87289f8ccbb325901e4222e723e7d745dbfc1803eaf5f3d2ace19cf2db"},
+    {file = "lark-1.1.9.tar.gz", hash = "sha256:15fa5236490824c2c4aba0e22d2d6d823575dcaf4cdd1848e34b6ad836240fba"},
 ]
 
 [package.extras]
@@ -1087,32 +1035,32 @@ regex = ["regex"]
 
 [[package]]
 name = "llvmlite"
-version = "0.43.0"
+version = "0.42.0"
 description = "lightweight wrapper around basic LLVM functionality"
 optional = true
 python-versions = ">=3.9"
 files = [
-    {file = "llvmlite-0.43.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a289af9a1687c6cf463478f0fa8e8aa3b6fb813317b0d70bf1ed0759eab6f761"},
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 name = "markupsafe"
 version = "2.1.5"
 description = "Safely add untrusted strings to HTML/XML markup."
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 python-versions = ">=3.7"
 files = [
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+tbb = "==2021.*"
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 [package.dependencies]
-dill = ">=0.3.8"
+dill = ">=0.3.7"
 
 [[package]]
 name = "nest-asyncio"
@@ -1357,7 +1327,7 @@ files = [
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 version = "3.2.1"
 description = "Python package for creating and manipulating graphs and networks"
-optional = false
+optional = true
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+version = "0.59.1"
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 python-versions = ">=3.9"
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+    {file = "numba-0.59.1-cp39-cp39-win_amd64.whl", hash = "sha256:8d51ccd7008a83105ad6a0082b6a2b70f1142dc7cfd76deb8c5a862367eb8c86"},
+    {file = "numba-0.59.1.tar.gz", hash = "sha256:76f69132b96028d2774ed20415e8c528a34e3299a40581bae178f0994a2f370b"},
 ]
 
 [package.dependencies]
-llvmlite = "==0.43.*"
-numpy = ">=1.22,<2.1"
+llvmlite = "==0.42.*"
+numpy = ">=1.22,<1.27"
 
 [[package]]
 name = "numpy"
@@ -1454,7 +1424,7 @@ files = [
 name = "nvidia-cublas-cu12"
 version = "12.1.3.1"
 description = "CUBLAS native runtime libraries"
-optional = false
+optional = true
 python-versions = ">=3"
 files = [
     {file = "nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl", hash = "sha256:ee53ccca76a6fc08fb9701aa95b6ceb242cdaab118c3bb152af4e579af792728"},
@@ -1465,7 +1435,7 @@ files = [
 name = "nvidia-cuda-cupti-cu12"
 version = "12.1.105"
 description = "CUDA profiling tools runtime libs."
-optional = false
+optional = true
 python-versions = ">=3"
 files = [
     {file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:e54fde3983165c624cb79254ae9818a456eb6e87a7fd4d56a2352c24ee542d7e"},
@@ -1476,7 +1446,7 @@ files = [
 name = "nvidia-cuda-nvrtc-cu12"
 version = "12.1.105"
 description = "NVRTC native runtime libraries"
-optional = false
+optional = true
 python-versions = ">=3"
 files = [
     {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:339b385f50c309763ca65456ec75e17bbefcbbf2893f462cb8b90584cd27a1c2"},
@@ -1487,7 +1457,7 @@ files = [
 name = "nvidia-cuda-runtime-cu12"
 version = "12.1.105"
 description = "CUDA Runtime native Libraries"
-optional = false
+optional = true
 python-versions = ">=3"
 files = [
     {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:6e258468ddf5796e25f1dc591a31029fa317d97a0a94ed93468fc86301d61e40"},
@@ -1496,13 +1466,12 @@ files = [
 
 [[package]]
 name = "nvidia-cudnn-cu12"
-version = "9.1.0.70"
+version = "8.9.2.26"
 description = "cuDNN runtime libraries"
-optional = false
+optional = true
 python-versions = ">=3"
 files = [
-    {file = "nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl", hash = "sha256:165764f44ef8c61fcdfdfdbe769d687e06374059fbb388b6c89ecb0e28793a6f"},
-    {file = "nvidia_cudnn_cu12-9.1.0.70-py3-none-win_amd64.whl", hash = "sha256:6278562929433d68365a07a4a1546c237ba2849852c0d4b2262a486e805b977a"},
+    {file = "nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl", hash = "sha256:5ccb288774fdfb07a7e7025ffec286971c06d8d7b4fb162525334616d7629ff9"},
 ]
 
 [package.dependencies]
@@ -1512,7 +1481,7 @@ nvidia-cublas-cu12 = "*"
 name = "nvidia-cufft-cu12"
 version = "11.0.2.54"
 description = "CUFFT native runtime libraries"
-optional = false
+optional = true
 python-versions = ">=3"
 files = [
     {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl", hash = "sha256:794e3948a1aa71fd817c3775866943936774d1c14e7628c74f6f7417224cdf56"},
@@ -1523,7 +1492,7 @@ files = [
 name = "nvidia-curand-cu12"
 version = "10.3.2.106"
 description = "CURAND native runtime libraries"
-optional = false
+optional = true
 python-versions = ">=3"
 files = [
     {file = "nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:9d264c5036dde4e64f1de8c50ae753237c12e0b1348738169cd0f8a536c0e1e0"},
@@ -1534,7 +1503,7 @@ files = [
 name = "nvidia-cusolver-cu12"
 version = "11.4.5.107"
 description = "CUDA solver native runtime libraries"
-optional = false
+optional = true
 python-versions = ">=3"
 files = [
     {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl", hash = "sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd"},
@@ -1550,7 +1519,7 @@ nvidia-nvjitlink-cu12 = "*"
 name = "nvidia-cusparse-cu12"
 version = "12.1.0.106"
 description = "CUSPARSE native runtime libraries"
-optional = false
+optional = true
 python-versions = ">=3"
 files = [
     {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:f3b50f42cf363f86ab21f720998517a659a48131e8d538dc02f8768237bd884c"},
@@ -1564,7 +1533,7 @@ nvidia-nvjitlink-cu12 = "*"
 name = "nvidia-nccl-cu12"
 version = "2.20.5"
 description = "NVIDIA Collective Communication Library (NCCL) Runtime"
-optional = false
+optional = true
 python-versions = ">=3"
 files = [
     {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1fc150d5c3250b170b29410ba682384b14581db722b2531b0d8d33c595f33d01"},
@@ -1573,21 +1542,20 @@ files = [
 
 [[package]]
 name = "nvidia-nvjitlink-cu12"
-version = "12.6.20"
+version = "12.5.40"
 description = "Nvidia JIT LTO Library"
-optional = false
+optional = true
 python-versions = ">=3"
 files = [
-    {file = "nvidia_nvjitlink_cu12-12.6.20-py3-none-manylinux2014_aarch64.whl", hash = "sha256:84fb38465a5bc7c70cbc320cfd0963eb302ee25a5e939e9f512bbba55b6072fb"},
-    {file = "nvidia_nvjitlink_cu12-12.6.20-py3-none-manylinux2014_x86_64.whl", hash = "sha256:562ab97ea2c23164823b2a89cb328d01d45cb99634b8c65fe7cd60d14562bd79"},
-    {file = "nvidia_nvjitlink_cu12-12.6.20-py3-none-win_amd64.whl", hash = "sha256:ed3c43a17f37b0c922a919203d2d36cbef24d41cc3e6b625182f8b58203644f6"},
+    {file = "nvidia_nvjitlink_cu12-12.5.40-py3-none-manylinux2014_x86_64.whl", hash = "sha256:d9714f27c1d0f0895cd8915c07a87a1d0029a0aa36acaf9156952ec2a8a12189"},
+    {file = "nvidia_nvjitlink_cu12-12.5.40-py3-none-win_amd64.whl", hash = "sha256:c3401dc8543b52d3a8158007a0c1ab4e9c768fcbd24153a48c86972102197ddd"},
 ]
 
 [[package]]
 name = "nvidia-nvtx-cu12"
 version = "12.1.105"
 description = "NVIDIA Tools Extension"
-optional = false
+optional = true
 python-versions = ">=3"
 files = [
     {file = "nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:dc21cf308ca5691e7c04d962e213f8a4aa9bbfa23d95412f452254c2caeb09e5"},
@@ -1748,75 +1716,6 @@ files = [
     {file = "opentelemetry_semantic_conventions-0.36b0.tar.gz", hash = "sha256:829dc221795467d98b773c04096e29be038d77526dc8d6ac76f546fb6279bf01"},
 ]
 
-[[package]]
-name = "optimum"
-version = "1.21.4"
-description = "Optimum Library is an extension of the Hugging Face Transformers library, providing a framework to integrate third-party libraries from Hardware Partners and interface with their specific functionality."
-optional = false
-python-versions = ">=3.7.0"
-files = [
-    {file = "optimum-1.21.4-py3-none-any.whl", hash = "sha256:d18f426bcfc7bed9754af0305e1a223100195bab804d47ea5e4f8b0562f23d46"},
-    {file = "optimum-1.21.4.tar.gz", hash = "sha256:47e751f1068c81fec7846d10c67bf39c18e41fda6cd15566dfc85c3ea82262f7"},
-]
-
-[package.dependencies]
-coloredlogs = "*"
-datasets = "*"
-huggingface-hub = ">=0.8.0"
-numpy = "<2.0"
-packaging = "*"
-sympy = "*"
-torch = ">=1.11"
-transformers = {version = ">=4.29.0,<4.44.0", extras = ["sentencepiece"]}
-
-[package.extras]
-amd = ["optimum-amd"]
-benchmark = ["evaluate (>=0.2.0)", "optuna", "scikit-learn", "seqeval", "torchvision", "tqdm"]
-dev = ["Pillow", "accelerate", "black (>=23.1,<24.0)", "diffusers (>=0.17.0)", "einops", "invisible-watermark", "parameterized", "pytest (<=8.0.0)", "pytest-xdist", "requests", "rjieba", "ruff (==0.1.5)", "sacremoses", "scikit-learn", "timm", "torchaudio", "torchvision"]
-diffusers = ["diffusers"]
-doc-build = ["accelerate"]
-exporters = ["onnx", "onnxruntime", "timm"]
-exporters-gpu = ["onnx", "onnxruntime-gpu", "timm"]
-exporters-tf = ["h5py", "numpy (<1.24.0)", "onnx", "onnxruntime", "tensorflow (>=2.4,<=2.12.1)", "tf2onnx", "timm", "transformers[sentencepiece] (>=4.26.0,<4.38.0)"]
-furiosa = ["optimum-furiosa"]
-graphcore = ["optimum-graphcore"]
-habana = ["optimum-habana", "transformers (>=4.43.0,<4.44.0)"]
-intel = ["optimum-intel (>=1.18.0)"]
-ipex = ["optimum-intel[ipex] (>=1.18.0)"]
-neural-compressor = ["optimum-intel[neural-compressor] (>=1.18.0)"]
-neuron = ["optimum-neuron[neuron] (>=0.0.20)", "transformers (>=4.36.2,<4.42.0)"]
-neuronx = ["optimum-neuron[neuronx] (>=0.0.20)", "transformers (>=4.36.2,<4.42.0)"]
-nncf = ["optimum-intel[nncf] (>=1.18.0)"]
-onnxruntime = ["datasets (>=1.2.1)", "evaluate", "onnx", "onnxruntime (>=1.11.0)", "protobuf (>=3.20.1)"]
-onnxruntime-gpu = ["accelerate", "datasets (>=1.2.1)", "evaluate", "onnx", "onnxruntime-gpu (>=1.11.0)", "protobuf (>=3.20.1)"]
-openvino = ["optimum-intel[openvino] (>=1.18.0)"]
-quality = ["black (>=23.1,<24.0)", "ruff (==0.1.5)"]
-tests = ["Pillow", "accelerate", "diffusers (>=0.17.0)", "einops", "invisible-watermark", "parameterized", "pytest (<=8.0.0)", "pytest-xdist", "requests", "rjieba", "sacremoses", "scikit-learn", "timm", "torchaudio", "torchvision"]
-
-[[package]]
-name = "optimum-habana"
-version = "1.13.2"
-description = "Optimum Habana is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU). It provides a set of tools enabling easy model loading, training and inference on single- and multi-HPU settings for different downstream tasks."
-optional = false
-python-versions = "*"
-files = [
-    {file = "optimum_habana-1.13.2-py3-none-any.whl", hash = "sha256:fbec406db58e6e24b9d4cfdbc72ac88423eeef3edaf81645dbd502141a29d052"},
-    {file = "optimum_habana-1.13.2.tar.gz", hash = "sha256:6e88c93860e33c8186f6dbd55a61ed287ed3cba241f289fcf4a4001d6e2a41f0"},
-]
-
-[package.dependencies]
-accelerate = ">=0.33.0,<0.34.0"
-diffusers = "0.29.2"
-huggingface-hub = ">=0.23.2"
-optimum = "*"
-sentence-transformers = {version = "3.0.1", extras = ["train"]}
-torch = "*"
-transformers = ">=4.43.0,<4.44.0"
-
-[package.extras]
-quality = ["hf-doc-builder", "ruff"]
-tests = ["GitPython", "datasets", "optuna", "parameterized", "peft", "psutil", "pytest (<8.0.0)", "safetensors", "scipy", "sentencepiece", "timm", "torchsde"]
-
 [[package]]
 name = "outlines"
 version = "0.0.36"
@@ -1852,20 +1751,20 @@ test = ["accelerate", "beartype (<0.16.0)", "coverage[toml] (>=5.1)", "datasets"
 
 [[package]]
 name = "packaging"
-version = "24.1"
+version = "24.0"
 description = "Core utilities for Python packages"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
-    {file = "packaging-24.1-py3-none-any.whl", hash = "sha256:5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124"},
-    {file = "packaging-24.1.tar.gz", hash = "sha256:026ed72c8ed3fcce5bf8950572258698927fd1dbda10a5e981cdf0ac37f4f002"},
+    {file = "packaging-24.0-py3-none-any.whl", hash = "sha256:2ddfb553fdf02fb784c234c7ba6ccc288296ceabec964ad2eae3777778130bc5"},
+    {file = "packaging-24.0.tar.gz", hash = "sha256:eb82c5e3e56209074766e6885bb04b8c38a0c015d0a30036ebe7ece34c9989e9"},
 ]
 
 [[package]]
 name = "pandas"
 version = "2.2.2"
 description = "Powerful data structures for data analysis, time series, and statistics"
-optional = false
+optional = true
 python-versions = ">=3.9"
 files = [
     {file = "pandas-2.2.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:90c6fca2acf139569e74e8781709dccb6fe25940488755716d1d354d6bc58bce"},
@@ -1938,7 +1837,7 @@ xml = ["lxml (>=4.9.2)"]
 name = "peft"
 version = "0.10.0"
 description = "Parameter-Efficient Fine-Tuning (PEFT)"
-optional = false
+optional = true
 python-versions = ">=3.8.0"
 files = [
     {file = "peft-0.10.0-py3-none-any.whl", hash = "sha256:d5249c97e818d3e31f92553c73c2953acd0ec12649b8b749afff7152cbc86cbb"},
@@ -1965,95 +1864,84 @@ test = ["black", "datasets", "diffusers (<0.21.0)", "hf-doc-builder", "parameter
 
 [[package]]
 name = "pillow"
-version = "10.4.0"
+version = "10.3.0"
 description = "Python Imaging Library (Fork)"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
 typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
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-description = "A python implementation of GNU readline."
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 [[package]]
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 version = "7.4.4"
@@ -2368,7 +2220,7 @@ testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "no
 name = "python-dateutil"
 version = "2.9.0.post0"
 description = "Extensions to the standard Python datetime module"
-optional = false
+optional = true
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 files = [
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@@ -2382,7 +2234,7 @@ six = ">=1.5"
 name = "pytz"
 version = "2024.1"
 description = "World timezone definitions, modern and historical"
-optional = false
+optional = true
 python-versions = "*"
 files = [
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 [[package]]
@@ -2468,101 +2318,101 @@ rpds-py = ">=0.7.0"
 
 [[package]]
 name = "regex"
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 optional = false
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 [package.dependencies]
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-joblib = ">=1.2.0"
-numpy = ">=1.19.5"
-scipy = ">=1.6.0"
-threadpoolctl = ">=3.1.0"
-
-[package.extras]
-benchmark = ["matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "pandas (>=1.1.5)"]
-build = ["cython (>=3.0.10)", "meson-python (>=0.16.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)"]
-docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "polars (>=0.20.23)", "pooch (>=1.6.0)", "pydata-sphinx-theme (>=0.15.3)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)", "sphinx (>=7.3.7)", "sphinx-copybutton (>=0.5.2)", "sphinx-design (>=0.5.0)", "sphinx-gallery (>=0.16.0)", "sphinx-prompt (>=1.4.0)", "sphinx-remove-toctrees (>=1.0.0.post1)", "sphinxcontrib-sass (>=0.3.4)", "sphinxext-opengraph (>=0.9.1)"]
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-install = ["joblib (>=1.2.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)", "threadpoolctl (>=3.1.0)"]
-maintenance = ["conda-lock (==2.5.6)"]
-tests = ["black (>=24.3.0)", "matplotlib (>=3.3.4)", "mypy (>=1.9)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "polars (>=0.20.23)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pyarrow (>=12.0.0)", "pytest (>=7.1.2)", "pytest-cov (>=2.9.0)", "ruff (>=0.2.1)", "scikit-image (>=0.17.2)"]
-
 [[package]]
 name = "scipy"
 version = "1.13.1"
@@ -2906,33 +2697,6 @@ dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pyde
 doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.12.0)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0)", "sphinx-design (>=0.4.0)"]
 test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
-[[package]]
-name = "sentence-transformers"
-version = "3.0.1"
-description = "Multilingual text embeddings"
-optional = false
-python-versions = ">=3.8.0"
-files = [
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-]
-
-[package.dependencies]
-accelerate = {version = ">=0.20.3", optional = true, markers = "extra == \"train\""}
-datasets = {version = "*", optional = true, markers = "extra == \"train\""}
-huggingface-hub = ">=0.15.1"
-numpy = "*"
-Pillow = "*"
-scikit-learn = "*"
-scipy = "*"
-torch = ">=1.11.0"
-tqdm = "*"
-transformers = ">=4.34.0,<5.0.0"
-
-[package.extras]
-dev = ["accelerate (>=0.20.3)", "datasets", "pre-commit", "pytest", "ruff (>=0.3.0)"]
-train = ["accelerate (>=0.20.3)", "datasets"]
-
 [[package]]
 name = "sentencepiece"
 version = "0.1.99"
@@ -2989,25 +2753,24 @@ files = [
 
 [[package]]
 name = "setuptools"
-version = "73.0.1"
+version = "70.0.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "setuptools-70.0.0.tar.gz", hash = "sha256:f211a66637b8fa059bb28183da127d4e86396c991a942b028c6650d4319c3fd0"},
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 [package.extras]
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+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
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 [[package]]
 name = "six"
 version = "1.16.0"
 description = "Python 2 and 3 compatibility utilities"
-optional = false
+optional = true
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 files = [
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@@ -3016,30 +2779,40 @@ files = [
 
 [[package]]
 name = "sympy"
-version = "1.13.2"
+version = "1.12"
 description = "Computer algebra system (CAS) in Python"
-optional = false
+optional = true
 python-versions = ">=3.8"
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 [package.dependencies]
-mpmath = ">=1.1.0,<1.4"
-
-[package.extras]
-dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"]
+mpmath = ">=0.19"
 
 [[package]]
-name = "threadpoolctl"
-version = "3.5.0"
-description = "threadpoolctl"
-optional = false
-python-versions = ">=3.8"
+name = "tbb"
+version = "2021.12.0"
+description = "Intel® oneAPI Threading Building Blocks (oneTBB)"
+optional = true
+python-versions = "*"
 files = [
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+]
+
+[[package]]
+name = "texttable"
+version = "1.7.0"
+description = "module to create simple ASCII tables"
+optional = true
+python-versions = "*"
+files = [
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 [[package]]
@@ -3172,43 +2945,44 @@ files = [
 
 [[package]]
 name = "torch"
-version = "2.4.0"
+version = "2.3.0"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
-optional = false
+optional = true
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
 filelock = "*"
 fsspec = "*"
 jinja2 = "*"
+mkl = {version = ">=2021.1.1,<=2021.4.0", markers = "platform_system == \"Windows\""}
 networkx = "*"
 nvidia-cublas-cu12 = {version = "12.1.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cuda-nvrtc-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cuda-runtime-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cudnn-cu12 = {version = "9.1.0.70", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cudnn-cu12 = {version = "8.9.2.26", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-curand-cu12 = {version = "10.3.2.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
@@ -3216,22 +2990,22 @@ nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"
 nvidia-nccl-cu12 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 sympy = "*"
-triton = {version = "3.0.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\""}
+triton = {version = "2.3.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
 typing-extensions = ">=4.8.0"
 
 [package.extras]
 opt-einsum = ["opt-einsum (>=3.3)"]
-optree = ["optree (>=0.11.0)"]
+optree = ["optree (>=0.9.1)"]
 
 [[package]]
 name = "tqdm"
-version = "4.66.5"
+version = "4.66.4"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.66.5-py3-none-any.whl", hash = "sha256:90279a3770753eafc9194a0364852159802111925aa30eb3f9d85b0e805ac7cd"},
-    {file = "tqdm-4.66.5.tar.gz", hash = "sha256:e1020aef2e5096702d8a025ac7d16b1577279c9d63f8375b63083e9a5f0fcbad"},
+    {file = "tqdm-4.66.4-py3-none-any.whl", hash = "sha256:b75ca56b413b030bc3f00af51fd2c1a1a5eac6a0c1cca83cbb37a5c52abce644"},
+    {file = "tqdm-4.66.4.tar.gz", hash = "sha256:e4d936c9de8727928f3be6079590e97d9abfe8d39a590be678eb5919ffc186bb"},
 ]
 
 [package.dependencies]
@@ -3245,41 +3019,38 @@ telegram = ["requests"]
 
 [[package]]
 name = "transformers"
-version = "4.43.4"
+version = "4.41.1"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "transformers-4.43.4-py3-none-any.whl", hash = "sha256:d2202ed201e0c44f80de8d753a19f6164187754630bc1f915661b9511d61c773"},
-    {file = "transformers-4.43.4.tar.gz", hash = "sha256:b62288990a65ed9bfb79191e04dbb76c9376834ae6e0dd911320a2ced63324fe"},
+    {file = "transformers-4.41.1-py3-none-any.whl", hash = "sha256:f0680e0b1a01067eccd11f62f0522409422c7d6f91d532fe0f50b136a406129d"},
+    {file = "transformers-4.41.1.tar.gz", hash = "sha256:fa859e4c66f0896633a3bf534e0d9a29a9a88478a49f94c5d8270537dc61cc42"},
 ]
 
 [package.dependencies]
 filelock = "*"
-huggingface-hub = ">=0.23.2,<1.0"
+huggingface-hub = ">=0.23.0,<1.0"
 numpy = ">=1.17"
 packaging = ">=20.0"
-protobuf = {version = "*", optional = true, markers = "extra == \"sentencepiece\""}
 pyyaml = ">=5.1"
 regex = "!=2019.12.17"
 requests = "*"
 safetensors = ">=0.4.1"
-sentencepiece = {version = ">=0.1.91,<0.1.92 || >0.1.92", optional = true, markers = "extra == \"sentencepiece\""}
 tokenizers = ">=0.19,<0.20"
 tqdm = ">=4.27"
 
 [package.extras]
 accelerate = ["accelerate (>=0.21.0)"]
 agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch"]
-all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision"]
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 audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
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 codecarbon = ["codecarbon (==1.2.0)"]
 deepspeed = ["accelerate (>=0.21.0)", "deepspeed (>=0.9.3)"]
-deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.21.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
-dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
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 flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 ftfy = ["ftfy"]
@@ -3290,41 +3061,41 @@ natten = ["natten (>=0.14.6,<0.15.0)"]
 onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
 onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
 optuna = ["optuna"]
-quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.4.4)", "urllib3 (<2.0.0)"]
+quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"]
 ray = ["ray[tune] (>=2.7.0)"]
 retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
-ruff = ["ruff (==0.4.4)"]
 sagemaker = ["sagemaker (>=2.31.0)"]
 sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
 serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
-tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
-tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
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 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
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 tokenizers = ["tokenizers (>=0.19,<0.20)"]
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 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
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 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
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 description = "A language and compiler for custom Deep Learning operations"
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 python-versions = "*"
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 ]
 
 [[package]]
 name = "yarl"
 version = "1.9.4"
 description = "Yet another URL library"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "yarl-1.9.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a8c1df72eb746f4136fe9a2e72b0c9dc1da1cbd23b5372f94b5820ff8ae30e0e"},
@@ -3722,22 +3478,15 @@ files = [
 idna = ">=2.0"
 multidict = ">=4.0"
 
-[[package]]
-name = "zipp"
-version = "3.20.0"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "zipp-3.20.0-py3-none-any.whl", hash = "sha256:58da6168be89f0be59beb194da1250516fdaa062ccebd30127ac65d30045e10d"},
-    {file = "zipp-3.20.0.tar.gz", hash = "sha256:0145e43d89664cfe1a2e533adc75adafed82fe2da404b4bbb6b026c0157bdb31"},
-]
-
-[package.extras]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+[extras]
+accelerate = ["accelerate"]
+bnb = ["bitsandbytes"]
+outlines = ["outlines"]
+peft = ["peft"]
+quantize = ["accelerate", "datasets", "texttable"]
+torch = ["torch"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "6d54ae94305899e548883c4700e93babc406ec896c114b45978111c514531906"
+content-hash = "06e67944a2b1cf9884a31e771d0e9d89877e9b3c91894982cb67d104cb834758"
diff --git a/server/pyproject.toml b/server/pyproject.toml
index c1809550f..6a3a0205e 100644
--- a/server/pyproject.toml
+++ b/server/pyproject.toml
@@ -9,34 +9,58 @@ text-generation-server = 'text_generation_server.cli:app'
 
 [tool.poetry.dependencies]
 python = ">=3.9,<3.13"
-protobuf = "^3.20.3"
+protobuf = "^4.21.7"
 grpcio = "^1.51.1"
-grpcio-status = "*"
-grpcio-reflection = "*"
+grpcio-status = "^1.51.1"
+grpcio-reflection = "^1.51.1"
 grpc-interceptor = "^0.15.0"
-typer = "^0.7.0"
+typer = "^0.6.1"
+accelerate = { version = "^0.29.1", optional = true }
+bitsandbytes = { version = "^0.43.0", optional = true }
+safetensors = "^0.4"
 loguru = "^0.6.0"
 opentelemetry-api = "^1.15.0"
 opentelemetry-exporter-otlp = "^1.15.0"
 opentelemetry-instrumentation-grpc = "^0.36b0"
 hf-transfer = "^0.1.2"
 sentencepiece = "^0.1.97"
-peft = "^0.10"
-optimum-habana = "1.13.2"
-transformers = "4.43.4"
-numpy = "1.26.4"
-accelerate = "0.33.0"
+tokenizers = "^0.19.1"
+huggingface-hub = "^0.23"
+transformers = "^4.41"
+einops = "^0.6.1"
+texttable = { version = "^1.6.7", optional = true }
+datasets = { version = "^2.14.0", optional = true }
+peft = { version = "^0.10", optional = true }
+torch = { version = "^2.3.0", optional = true }
+scipy = "^1.11.1"
+pillow = "^10.0.0"
 outlines= { version = "^0.0.36", optional = true }
 prometheus-client = "^0.20.0"
 py-cpuinfo = "^9.0.0"
 
+[tool.poetry.extras]
+torch = ["torch"]
+accelerate = ["accelerate"]
+bnb = ["bitsandbytes"]
+peft = ["peft"]
+quantize = ["texttable", "datasets", "accelerate"]
+outlines = ["outlines"]
+
 [tool.poetry.group.dev.dependencies]
-grpcio-tools = "*"
+grpcio-tools = "^1.51.1"
 pytest = "^7.3.0"
 
+
+[[tool.poetry.source]]
+name = "pytorch-gpu-src"
+url = "https://download.pytorch.org/whl/cu121"
+priority = "explicit"
+
 [tool.pytest.ini_options]
 markers = ["private: marks tests as requiring an admin hf token (deselect with '-m \"not private\"')"]
 
 [build-system]
-requires = ["poetry-core>=1.0.0"]
+requires = [
+    "poetry-core>=1.0.0",
+]
 build-backend = "poetry.core.masonry.api"
diff --git a/server/requirements.txt b/server/requirements.txt
deleted file mode 100644
index 0a091a2fe..000000000
--- a/server/requirements.txt
+++ /dev/null
@@ -1,88 +0,0 @@
-accelerate==0.33.0 ; python_version >= "3.9" and python_version < "3.13"
-aiohappyeyeballs==2.4.0 ; python_version >= "3.9" and python_version < "3.13"
-aiohttp==3.10.5 ; python_version >= "3.9" and python_version < "3.13"
-aiosignal==1.3.1 ; python_version >= "3.9" and python_version < "3.13"
-async-timeout==4.0.3 ; python_version >= "3.9" and python_version < "3.11"
-attrs==24.2.0 ; python_version >= "3.9" and python_version < "3.13"
-backoff==2.2.1 ; python_version >= "3.9" and python_version < "3.13"
-certifi==2024.7.4 ; python_version >= "3.9" and python_version < "3.13"
-charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "3.13"
-click==8.1.7 ; python_version >= "3.9" and python_version < "3.13"
-colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows")
-coloredlogs==15.0.1 ; python_version >= "3.9" and python_version < "3.13"
-datasets==2.21.0 ; python_version >= "3.9" and python_version < "3.13"
-deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13"
-diffusers==0.29.2 ; python_version >= "3.9" and python_version < "3.13"
-dill==0.3.8 ; python_version >= "3.9" and python_version < "3.13"
-filelock==3.15.4 ; python_version >= "3.9" and python_version < "3.13"
-frozenlist==1.4.1 ; python_version >= "3.9" and python_version < "3.13"
-fsspec==2024.6.1 ; python_version >= "3.9" and python_version < "3.13"
-fsspec[http]==2024.6.1 ; python_version >= "3.9" and python_version < "3.13"
-googleapis-common-protos==1.63.2 ; python_version >= "3.9" and python_version < "3.13"
-grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
-grpcio-reflection==1.48.2 ; python_version >= "3.9" and python_version < "3.13"
-grpcio-status==1.48.2 ; python_version >= "3.9" and python_version < "3.13"
-grpcio==1.66.0 ; python_version >= "3.9" and python_version < "3.13"
-hf-transfer==0.1.8 ; python_version >= "3.9" and python_version < "3.13"
-huggingface-hub==0.24.6 ; python_version >= "3.9" and python_version < "3.13"
-humanfriendly==10.0 ; python_version >= "3.9" and python_version < "3.13"
-idna==3.8 ; python_version >= "3.9" and python_version < "3.13"
-importlib-metadata==8.4.0 ; python_version >= "3.9" and python_version < "3.13"
-jinja2==3.1.4 ; python_version >= "3.9" and python_version < "3.13"
-joblib==1.4.2 ; python_version >= "3.9" and python_version < "3.13"
-loguru==0.6.0 ; python_version >= "3.9" and python_version < "3.13"
-markupsafe==2.1.5 ; python_version >= "3.9" and python_version < "3.13"
-mpmath==1.3.0 ; python_version >= "3.9" and python_version < "3.13"
-multidict==6.0.5 ; python_version >= "3.9" and python_version < "3.13"
-multiprocess==0.70.16 ; python_version >= "3.9" and python_version < "3.13"
-networkx==3.2.1 ; python_version >= "3.9" and python_version < "3.13"
-numpy==1.26.4 ; python_version >= "3.9" and python_version < "3.13"
-opentelemetry-api==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
-opentelemetry-exporter-otlp-proto-grpc==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
-opentelemetry-exporter-otlp-proto-http==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
-opentelemetry-exporter-otlp==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
-opentelemetry-instrumentation-grpc==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
-opentelemetry-instrumentation==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
-opentelemetry-proto==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
-opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
-opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
-optimum-habana==1.13.2 ; python_version >= "3.9" and python_version < "3.13"
-optimum==1.21.4 ; python_version >= "3.9" and python_version < "3.13"
-packaging==24.1 ; python_version >= "3.9" and python_version < "3.13"
-pandas==2.2.2 ; python_version >= "3.9" and python_version < "3.13"
-peft==0.10.0 ; python_version >= "3.9" and python_version < "3.13"
-pillow==10.4.0 ; python_version >= "3.9" and python_version < "3.13"
-prometheus-client==0.20.0 ; python_version >= "3.9" and python_version < "3.13"
-protobuf==3.20.3 ; python_version >= "3.9" and python_version < "3.13"
-psutil==6.0.0 ; python_version >= "3.9" and python_version < "3.13"
-py-cpuinfo==9.0.0 ; python_version >= "3.9" and python_version < "3.13"
-pyarrow==17.0.0 ; python_version >= "3.9" and python_version < "3.13"
-pyreadline3==3.4.1 ; sys_platform == "win32" and python_version >= "3.9" and python_version < "3.13"
-python-dateutil==2.9.0.post0 ; python_version >= "3.9" and python_version < "3.13"
-pytz==2024.1 ; python_version >= "3.9" and python_version < "3.13"
-pyyaml==6.0.2 ; python_version >= "3.9" and python_version < "3.13"
-regex==2024.7.24 ; python_version >= "3.9" and python_version < "3.13"
-requests==2.32.3 ; python_version >= "3.9" and python_version < "3.13"
-safetensors==0.4.4 ; python_version >= "3.9" and python_version < "3.13"
-scikit-learn==1.5.1 ; python_version >= "3.9" and python_version < "3.13"
-scipy==1.13.1 ; python_version >= "3.9" and python_version < "3.13"
-sentence-transformers[train]==3.0.1 ; python_version >= "3.9" and python_version < "3.13"
-sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
-setuptools==73.0.1 ; python_version >= "3.9" and python_version < "3.13"
-six==1.16.0 ; python_version >= "3.9" and python_version < "3.13"
-sympy==1.12.1 ; python_version >= "3.9" and python_version < "3.13"
-threadpoolctl==3.5.0 ; python_version >= "3.9" and python_version < "3.13"
-tokenizers==0.19.1 ; python_version >= "3.9" and python_version < "3.13"
-tqdm==4.66.5 ; python_version >= "3.9" and python_version < "3.13"
-transformers==4.43.4 ; python_version >= "3.9" and python_version < "3.13"
-transformers[sentencepiece]==4.43.4 ; python_version >= "3.9" and python_version < "3.13"
-triton==3.0.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version < "3.13" and python_version >= "3.9"
-typer==0.7.0 ; python_version >= "3.9" and python_version < "3.13"
-typing-extensions==4.12.2 ; python_version >= "3.9" and python_version < "3.13"
-tzdata==2024.1 ; python_version >= "3.9" and python_version < "3.13"
-urllib3==2.2.2 ; python_version >= "3.9" and python_version < "3.13"
-win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "3.13" and sys_platform == "win32"
-wrapt==1.16.0 ; python_version >= "3.9" and python_version < "3.13"
-xxhash==3.5.0 ; python_version >= "3.9" and python_version < "3.13"
-yarl==1.9.4 ; python_version >= "3.9" and python_version < "3.13"
-zipp==3.20.0 ; python_version >= "3.9" and python_version < "3.13"
diff --git a/server/tests/models/test_bloom.py b/server/tests/models/test_bloom.py
index 4b7dde819..66df708ad 100644
--- a/server/tests/models/test_bloom.py
+++ b/server/tests/models/test_bloom.py
@@ -1,5 +1,3 @@
-# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
 import pytest
 import torch
 
@@ -7,9 +5,9 @@ from copy import copy
 from transformers import AutoTokenizer
 
 from text_generation_server.pb import generate_pb2
-from text_generation_server.models.causal_lm import CausalLMBatch, PAD_SEQUENCE_TO_MULTIPLE_OF
+from text_generation_server.models.causal_lm import CausalLMBatch
 from text_generation_server.utils import weight_hub_files, download_weights
-from text_generation_server.models.bloom import BloomCausalLMBatch, BLOOM
+from text_generation_server.models.bloom import BloomCausalLMBatch, BLOOMSharded
 
 
 @pytest.fixture(scope="session")
@@ -18,7 +16,7 @@ def default_bloom():
     revision = "main"
     filenames = weight_hub_files(model_id, revision, ".safetensors")
     download_weights(filenames, model_id, revision)
-    return BLOOM(model_id)
+    return BLOOMSharded(model_id)
 
 
 @pytest.fixture(scope="session")
@@ -32,7 +30,7 @@ def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
         id=0,
         inputs="Test",
         prefill_logprobs=True,
-        truncate=PAD_SEQUENCE_TO_MULTIPLE_OF,
+        truncate=100,
         parameters=default_pb_parameters,
         stopping_parameters=default_pb_stop_parameters,
     )
@@ -46,7 +44,7 @@ def default_pb_batch(default_pb_request):
 @pytest.fixture
 def default_bloom_batch(default_pb_batch, bloom_560m_tokenizer):
     return BloomCausalLMBatch.from_pb(
-        default_pb_batch, bloom_560m_tokenizer, torch.float32, torch.device("hpu")
+        default_pb_batch, bloom_560m_tokenizer, torch.float32, torch.device("cpu")
     )
 
 
@@ -60,7 +58,7 @@ def default_multi_requests_bloom_batch(default_pb_request, bloom_560m_tokenizer)
 
     batch_pb = generate_pb2.Batch(id=0, requests=[req_0, req_1], size=2)
     return BloomCausalLMBatch.from_pb(
-        batch_pb, bloom_560m_tokenizer, torch.float32, torch.device("hpu")
+        batch_pb, bloom_560m_tokenizer, torch.float32, torch.device("cpu")
     )
 
 
@@ -68,29 +66,30 @@ def test_batch_from_pb(default_pb_batch, default_bloom_batch):
     batch = default_bloom_batch
 
     assert batch.batch_id == default_pb_batch.id
-    assert len(batch.requests) == len(default_pb_batch.requests) == default_pb_batch.size
-    for request, pb_request in zip(batch.requests, default_pb_batch.requests):
-        assert request.data == pb_request
+    assert batch.requests == default_pb_batch.requests
 
-    assert batch.input_ids[0][-1] == 3
-    assert batch.input_ids[0][-2] == 10264
-    assert torch.all(batch.input_ids[0][:-2] == 3)
+    assert len(batch.input_ids) == default_pb_batch.size
+    assert batch.input_ids[0][-1] == 10264
+    assert torch.all(batch.input_ids[0][:-1] == 3)
 
-    assert batch.attention_mask[0][-1] == 0
-    assert batch.attention_mask[0][-2] == 1
-    assert torch.all(batch.attention_mask[0][:-2] == 0)
+    assert batch.attention_mask[0][0] == 1
+    assert torch.all(batch.attention_mask[0][1:] == 0)
 
     assert batch.past_key_values is None
 
     assert all(
         [
-            torch.equal(input_ids, request.all_input_ids[:batch.input_length+1, 0])
-            for input_ids, request in zip(batch.input_ids, batch.requests)
+            torch.equal(input_ids, all_input_ids[:, 0])
+            for input_ids, all_input_ids in zip(batch.input_ids, batch.all_input_ids)
         ]
     )
 
+    assert batch.input_lengths == [1]
+
     assert len(batch) == default_pb_batch.size
-    assert batch.max_input_length == batch.input_length == PAD_SEQUENCE_TO_MULTIPLE_OF - 1
+    assert len(batch.next_token_choosers) == len(batch.stopping_criterias) == len(batch)
+
+    assert batch.max_input_length == batch.input_lengths[0]
 
 
 def test_batch_concatenate_no_prefill(default_bloom_batch):
@@ -102,7 +101,6 @@ def test_causal_lm_batch_type(default_bloom):
     assert default_bloom.batch_type == BloomCausalLMBatch
 
 
-@pytest.mark.skip
 def test_causal_lm_generate_token(default_bloom, default_bloom_batch):
     sequence_length = len(default_bloom_batch.all_input_ids[0])
     generations, next_batch, _ = default_bloom.generate_token(default_bloom_batch)
@@ -152,7 +150,6 @@ def test_causal_lm_generate_token(default_bloom, default_bloom_batch):
     assert generations[0].request_id == 0
 
 
-@pytest.mark.skip
 def test_causal_lm_generate_token_completion(default_bloom, default_bloom_batch):
     next_batch = default_bloom_batch
     for _ in range(default_bloom_batch.stopping_criterias[0].max_new_tokens - 1):
@@ -173,7 +170,6 @@ def test_causal_lm_generate_token_completion(default_bloom, default_bloom_batch)
     )
 
 
-@pytest.mark.skip
 def test_causal_lm_generate_token_completion_multi(
     default_bloom, default_multi_requests_bloom_batch
 ):
@@ -224,7 +220,6 @@ def test_causal_lm_generate_token_completion_multi(
     )
 
 
-@pytest.mark.skip
 def test_batch_concatenate(
     default_bloom, default_bloom_batch, default_multi_requests_bloom_batch
 ):
diff --git a/server/tests/models/test_causal_lm.py b/server/tests/models/test_causal_lm.py
index 6a0a474a1..250fa354b 100644
--- a/server/tests/models/test_causal_lm.py
+++ b/server/tests/models/test_causal_lm.py
@@ -1,5 +1,3 @@
-# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
 import pytest
 import torch
 
@@ -7,27 +5,19 @@ from copy import copy
 from transformers import AutoTokenizer
 
 from text_generation_server.pb import generate_pb2
-from text_generation_server.models import get_model
-from text_generation_server.models.causal_lm import (
-    CausalLMBatch,
-    PREFILL_BATCH_BUCKET_SIZE,
-    PAD_SEQUENCE_TO_MULTIPLE_OF,
-    MAX_TOTAL_TOKENS,
-    BATCH_BUCKET_SIZE,
-)
-
-PAD_TOKEN=0
+from text_generation_server.models.causal_lm import CausalLM, CausalLMBatch
 
 
 @pytest.fixture(scope="session")
 def default_causal_lm():
-    return get_model("meta-llama/Llama-2-7b-hf", None, None, None, None)
+    return CausalLM("gpt2")
 
 
 @pytest.fixture(scope="session")
-def default_tokenizer(default_causal_lm):
-    default_causal_lm.tokenizer.pad_token_id = PAD_TOKEN
-    return default_causal_lm.tokenizer
+def gpt2_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained("gpt2", padding_side="left")
+    tokenizer.pad_token_id = 50256
+    return tokenizer
 
 
 @pytest.fixture
@@ -36,7 +26,7 @@ def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
         id=0,
         inputs="Test",
         prefill_logprobs=True,
-        truncate=PAD_SEQUENCE_TO_MULTIPLE_OF,
+        truncate=100,
         parameters=default_pb_parameters,
         stopping_parameters=default_pb_stop_parameters,
     )
@@ -48,14 +38,14 @@ def default_pb_batch(default_pb_request):
 
 
 @pytest.fixture
-def default_causal_lm_batch(default_pb_batch, default_tokenizer):
+def default_causal_lm_batch(default_pb_batch, gpt2_tokenizer):
     return CausalLMBatch.from_pb(
-        default_pb_batch, default_tokenizer, torch.float32, torch.device("hpu")
+        default_pb_batch, gpt2_tokenizer, torch.float32, torch.device("cpu")
     )
 
 
 @pytest.fixture
-def default_multi_requests_causal_lm_batch(default_pb_request, default_tokenizer):
+def default_multi_requests_causal_lm_batch(default_pb_request, gpt2_tokenizer):
     req_0 = copy(default_pb_request)
     req_0.id = 1
     req_1 = default_pb_request
@@ -64,7 +54,7 @@ def default_multi_requests_causal_lm_batch(default_pb_request, default_tokenizer
 
     batch_pb = generate_pb2.Batch(id=1, requests=[req_0, req_1], size=2)
     return CausalLMBatch.from_pb(
-        batch_pb, default_tokenizer, torch.float32, torch.device("hpu")
+        batch_pb, gpt2_tokenizer, torch.float32, torch.device("cpu")
     )
 
 
@@ -72,37 +62,30 @@ def test_batch_from_pb(default_pb_batch, default_causal_lm_batch):
     batch = default_causal_lm_batch
 
     assert batch.batch_id == default_pb_batch.id
-    assert len(batch.requests) == len(default_pb_batch.requests)
+    assert batch.requests == default_pb_batch.requests
 
-    for r in range(0,len(default_pb_batch.requests)):
-        assert batch.requests[r].data == default_pb_batch.requests[r]
+    assert len(batch.input_ids) == default_pb_batch.size
+    assert batch.input_ids[0][-1] == 14402
+    assert torch.all(batch.input_ids[0][:-1] == 50256)
 
-    # For Gaudi we are adding padding of multiplication of bucket size
-    size_of_padded_to_bucket = ((default_pb_batch.size + PREFILL_BATCH_BUCKET_SIZE - 1) // PREFILL_BATCH_BUCKET_SIZE) * PREFILL_BATCH_BUCKET_SIZE
-
-    assert len(batch.input_ids) == size_of_padded_to_bucket
-
-    assert batch.input_ids[0][-2] == 4321
-    assert batch.input_ids[0][-3] == 1
-    assert torch.all(batch.input_ids[0][:-3] == PAD_TOKEN)
-    assert batch.input_ids[0][-1] == PAD_TOKEN
-
-    assert batch.attention_mask[0][-1] == 0
-    assert batch.attention_mask[0, -2] == 1
-    assert batch.attention_mask[0, -3] == 1
-    assert torch.all(batch.attention_mask[0, :-3] == 0)
+    assert batch.attention_mask[0, 0] == 1
+    assert torch.all(batch.attention_mask[0, 1:] == 0)
 
     assert batch.past_key_values is None
+
     assert all(
         [
-            torch.equal(input_ids.to('cpu'), request.all_input_ids[:batch.input_length + 1, 0])
-            for input_ids, request in zip(batch.input_ids, batch.requests)
+            torch.equal(input_ids, all_input_ids[:, 0])
+            for input_ids, all_input_ids in zip(batch.input_ids, batch.all_input_ids)
         ]
     )
 
-    assert len(batch) == default_pb_batch.size
+    assert batch.input_lengths == [1]
 
-    assert batch.max_input_length + 1 == default_pb_batch.requests[0].truncate
+    assert len(batch) == default_pb_batch.size
+    assert len(batch.next_token_choosers) == len(batch.stopping_criterias) == len(batch)
+
+    assert batch.max_input_length == batch.input_lengths[0]
 
 
 def test_batch_concatenate_no_prefill(default_causal_lm_batch):
@@ -115,66 +98,73 @@ def test_causal_lm_batch_type(default_causal_lm):
 
 
 def test_causal_lm_generate_token(default_causal_lm, default_causal_lm_batch):
+    sequence_length = len(default_causal_lm_batch.all_input_ids[0])
+    generations, next_batch, _ = default_causal_lm.generate_token(
+        default_causal_lm_batch
+    )
 
-    sequence_length = len(default_causal_lm_batch.requests[0].all_input_ids)
-    generations, next_batch, _ = default_causal_lm.generate_token([default_causal_lm_batch])
-    padding = next_batch.requests[0].stopping_criteria.max_new_tokens
-
+    assert len(generations) == len(next_batch)
     assert isinstance(next_batch, CausalLMBatch)
-    assert len(next_batch.attention_mask[0]) == PAD_SEQUENCE_TO_MULTIPLE_OF
-    assert next_batch.requests[0].all_input_ids[-padding-2] == 4321
 
-    assert torch.all(next_batch.requests[0].all_input_ids[-padding-1:] == PAD_TOKEN)
-    assert torch.all(next_batch.requests[0].all_input_ids[:-padding-3] == PAD_TOKEN)
+    assert len(next_batch.all_input_ids) == len(next_batch)
+    assert len(next_batch.all_input_ids[0]) == sequence_length + 1
+    assert len(next_batch.attention_mask[0]) == 11
+    assert next_batch.all_input_ids[0][-1] == 13
+    assert next_batch.all_input_ids[0][-2] == 14402
+    assert torch.all(next_batch.all_input_ids[0][:-2] == 50256)
 
-    generations, next_batch, _ = default_causal_lm.generate_token([default_causal_lm_batch])
-    assert torch.all(next_batch.attention_mask[0][PAD_SEQUENCE_TO_MULTIPLE_OF-3:PAD_SEQUENCE_TO_MULTIPLE_OF] == 1)
-    assert torch.all(next_batch.attention_mask[0][:PAD_SEQUENCE_TO_MULTIPLE_OF-3] == 0)
-    assert torch.all(next_batch.attention_mask[0][PAD_SEQUENCE_TO_MULTIPLE_OF+1:] == 0)
+    assert torch.all(next_batch.attention_mask[0][0:2] == 1)
+    assert torch.all(next_batch.attention_mask[0][2:] == 0)
 
-    assert next_batch.requests[0].all_input_ids[-padding-2] == 4321
-    assert next_batch.requests[0].all_input_ids[-padding-1] == 292
-    assert torch.all(next_batch.requests[0].all_input_ids[-padding:] == PAD_TOKEN)
-    assert torch.all(next_batch.requests[0].all_input_ids[:-padding-3] == PAD_TOKEN)
+    assert next_batch.input_ids.shape == (len(next_batch), 1)
+    assert next_batch.input_ids[0, 0] == 13
 
-    assert next_batch.input_length == PAD_SEQUENCE_TO_MULTIPLE_OF
-    assert next_batch.max_input_length == next_batch.input_length
+    assert next_batch.input_lengths == [2]
+    assert next_batch.max_input_length == next_batch.input_lengths[0]
 
     assert next_batch.past_key_values is not None
     assert all(
-        [p[0].shape == (BATCH_BUCKET_SIZE, 32, MAX_TOTAL_TOKENS, 128) for p in next_batch.past_key_values]
+        [p[0].shape == (1, 12, sequence_length, 64) for p in next_batch.past_key_values]
     )
     assert all(
-        [p[1].shape == (BATCH_BUCKET_SIZE, 32, MAX_TOTAL_TOKENS, 128) for p in next_batch.past_key_values]
+        [p[1].shape == (1, 12, sequence_length, 64) for p in next_batch.past_key_values]
     )
     assert all([generation.generated_text is None for generation in generations])
-    assert all([len(generation.prefill_tokens) == PAD_SEQUENCE_TO_MULTIPLE_OF-1 for generation in generations])
-    assert all([generation.tokens.token_ids[0] == 292 for generation in generations])
-    assert all([generation.tokens.texts[0] == "ing" for generation in generations])
+    assert all([len(generation.prefill_tokens) == 1 for generation in generations])
+    assert all(
+        [
+            token_id.item() == 13
+            for generation in generations
+            for token_id in generation.tokens.token_ids
+        ]
+    )
+    assert all(
+        [
+            token_text == "."
+            for generation in generations
+            for token_text in generation.tokens.texts
+        ]
+    )
     assert generations[0].request_id == 0
 
 
 def test_causal_lm_generate_token_completion(
     default_causal_lm, default_causal_lm_batch
 ):
-
     next_batch = default_causal_lm_batch
-    generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
-
-    for _ in range(default_causal_lm_batch.requests[0].stopping_criteria.max_new_tokens - 1):
-        generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
+    for _ in range(default_causal_lm_batch.stopping_criterias[0].max_new_tokens - 1):
+        generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
         assert len(generations) == len(next_batch)
 
-    generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
-
+    generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
     assert next_batch is None
 
     assert len(generations) == 1
-    assert generations[0].generated_text.text == "ing the effect of a new method for the detection"
-    assert generations[0].request_id == default_causal_lm_batch.requests[0].data.id
+    assert generations[0].generated_text.text == ".java:784) at net.minecraft."
+    assert generations[0].request_id == default_causal_lm_batch.requests[0].id
     assert (
         generations[0].generated_text.generated_tokens
-        == default_causal_lm_batch.requests[0].stopping_criteria.max_new_tokens
+        == default_causal_lm_batch.stopping_criterias[0].max_new_tokens
     )
 
 
@@ -182,49 +172,51 @@ def test_causal_lm_generate_token_completion_multi(
     default_causal_lm, default_multi_requests_causal_lm_batch
 ):
     next_batch = default_multi_requests_causal_lm_batch
-    generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
 
     for i in range(
-        default_multi_requests_causal_lm_batch.requests[1].stopping_criteria.max_new_tokens - 1
+        default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens - 1
     ):
-        generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
+        generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
         assert len(generations) == len(next_batch)
 
-    generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
+    generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
     assert next_batch is not None
 
     assert len(generations) == 2
-    assert generations[1].generated_text.text == "ing the effect of a"
+    assert generations[1].generated_text.text == ".java:784)"
     assert (
         generations[1].request_id
-        == default_multi_requests_causal_lm_batch.requests[1].data.id
+        == default_multi_requests_causal_lm_batch.requests[1].id
     )
     assert (
         generations[1].generated_text.generated_tokens
-        == default_multi_requests_causal_lm_batch.requests[1].stopping_criteria.max_new_tokens
+        == default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens
+    )
+    # Copy stopping_criterias before filtering
+    stopping_criterias = (
+        default_multi_requests_causal_lm_batch.stopping_criterias.copy()
     )
 
-    next_batch = next_batch.filter([next_batch.requests[0].data.id])
+    next_batch = next_batch.filter([next_batch.requests[0].id])
 
     for _ in range(
-        default_multi_requests_causal_lm_batch.requests[0].stopping_criteria.max_new_tokens - default_multi_requests_causal_lm_batch.requests[1].stopping_criteria.max_new_tokens - 1
+        stopping_criterias[0].max_new_tokens - stopping_criterias[1].max_new_tokens - 1
     ):
-        generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
+        generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
         assert len(generations) == len(next_batch)
 
-    generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
-
+    generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
     assert next_batch is None
 
     assert len(generations) == 1
-    assert generations[0].generated_text.text == "ing the effect of a new method for the detection"
+    assert generations[0].generated_text.text == ".java:784) at net.minecraft."
     assert (
         generations[0].request_id
-        == default_multi_requests_causal_lm_batch.requests[0].data.id
+        == default_multi_requests_causal_lm_batch.requests[0].id
     )
     assert (
         generations[0].generated_text.generated_tokens
-        == default_multi_requests_causal_lm_batch.requests[0].stopping_criteria.max_new_tokens
+        == default_multi_requests_causal_lm_batch.stopping_criterias[0].max_new_tokens
     )
 
 
@@ -232,13 +224,11 @@ def test_batch_concatenate(
     default_causal_lm, default_causal_lm_batch, default_multi_requests_causal_lm_batch
 ):
     next_batch_0 = default_causal_lm_batch
-    _, next_batch_0, _ = default_causal_lm.generate_token([next_batch_0])
-    _, next_batch_0, _ = default_causal_lm.generate_token([next_batch_0])
-    _, next_batch_0, _ = default_causal_lm.generate_token([next_batch_0])
+    _, next_batch_0, _ = default_causal_lm.generate_token(next_batch_0)
+    _, next_batch_0, _ = default_causal_lm.generate_token(next_batch_0)
 
     next_batch_1 = default_multi_requests_causal_lm_batch
-    _, next_batch_1, _ = default_causal_lm.generate_token([next_batch_1])
-    _, next_batch_1, _ = default_causal_lm.generate_token([next_batch_1])
+    _, next_batch_1, _ = default_causal_lm.generate_token(next_batch_1)
 
     # Clone past_key_values before concatenating to compare after,
     # because they are removed from the concatenated batches
@@ -251,135 +241,113 @@ def test_batch_concatenate(
 
     next_batch = CausalLMBatch.concatenate([next_batch_0, next_batch_1])
 
-    assert torch.equal(next_batch.requests[0].all_input_ids, next_batch_0.requests[0].all_input_ids)
-    assert torch.equal(next_batch.requests[1].all_input_ids, next_batch_1.requests[0].all_input_ids)
-    assert torch.equal(next_batch.requests[2].all_input_ids, next_batch_1.requests[1].all_input_ids)
-
+    assert torch.equal(next_batch.all_input_ids[0], next_batch_0.all_input_ids[0])
+    assert torch.equal(next_batch.all_input_ids[1], next_batch_1.all_input_ids[0])
+    assert torch.equal(next_batch.all_input_ids[2], next_batch_1.all_input_ids[1])
 
     assert torch.all(
-            next_batch.attention_mask[0:2, -next_batch.right_padding - 3: -next_batch.right_padding] == 1
+        next_batch.attention_mask[0, : -next_batch.padding_right_offset] == 1
     )
     assert torch.all(
-            next_batch.attention_mask[2, -next_batch.right_padding - 4: -next_batch.right_padding] == 1
+        next_batch.attention_mask[1:, 1 : -next_batch.padding_right_offset] == 1
     )
-    assert torch.all(
-            next_batch.attention_mask[3, -next_batch.right_padding - 3: -next_batch.right_padding] == 1
-    )
-
-    assert torch.all(
-            next_batch.attention_mask[0:2, :-next_batch.right_padding-3] == 0)
-    assert torch.all(
-        next_batch.attention_mask[2, :-next_batch.right_padding-4] == 0)
-    assert torch.all(
-        next_batch.attention_mask[3, :-next_batch.right_padding-3] == 0)
+    assert torch.all(next_batch.attention_mask[1:, 3:] == 0)
 
     assert next_batch.batch_id == 0
-    assert next_batch.input_ids[0,-next_batch.right_padding - 3] == 1
-    assert next_batch.input_ids[0,-next_batch.right_padding - 2] == 4321
-    assert next_batch.input_ids[0,-next_batch.right_padding - 1] == 292
+    assert next_batch.input_ids[0, 0] == 12355
+    assert torch.all(next_batch.input_ids[1:] == 13)
 
-    assert next_batch.max_input_length == 129
-
-    assert torch.all(next_batch.input_ids[0,-next_batch.right_padding:] == PAD_TOKEN)
-    assert torch.all(next_batch.input_ids[1,-next_batch.right_padding:] == PAD_TOKEN)
-    assert torch.all(next_batch.input_ids[2,-next_batch.right_padding:] == PAD_TOKEN)
-    assert torch.all(next_batch.input_ids[3,-next_batch.right_padding:] == PAD_TOKEN)
-
-    assert next_batch.input_length == PAD_SEQUENCE_TO_MULTIPLE_OF +1
-    assert next_batch.max_input_length == PAD_SEQUENCE_TO_MULTIPLE_OF +1
+    assert next_batch.input_lengths == [3, 2, 2]
+    assert next_batch.max_input_length == 3
 
     assert next_batch.requests[0] == next_batch_0.requests[0]
     assert next_batch.requests[1:] == next_batch_1.requests
 
-    assert next_batch.requests[0].stopping_criteria == next_batch_0.requests[0].stopping_criteria
-    assert next_batch.requests[1].stopping_criteria == next_batch_1.requests[0].stopping_criteria
-    assert next_batch.requests[2].stopping_criteria == next_batch_1.requests[1].stopping_criteria
-
-    assert next_batch.past_key_values is not None
-
-    assert all([p[0].shape == (8, 32, 2048, 128) for p in next_batch.past_key_values])
-    assert all([p[1].shape == (8, 32, 2048, 128) for p in next_batch.past_key_values])
+    assert next_batch.next_token_choosers[0] == next_batch_0.next_token_choosers[0]
+    assert next_batch.next_token_choosers[1:] == next_batch_1.next_token_choosers
+
+    assert next_batch.stopping_criterias[0] == next_batch_0.stopping_criterias[0]
+    assert next_batch.stopping_criterias[1:] == next_batch_1.stopping_criterias
 
     assert next_batch.past_key_values is not None
+    assert all([p[0].shape == (3, 12, 2, 64) for p in next_batch.past_key_values])
+    assert all([p[1].shape == (3, 12, 2, 64) for p in next_batch.past_key_values])
 
     for i, past in enumerate(next_batch.past_key_values):
-        assert torch.equal(next_batch_0_past_key_values[i][0][0, 0,0:128], past[0][0][0][1:129])
+        assert torch.equal(next_batch_0_past_key_values[i][0][0, :, -2:], past[0][0])
         assert torch.equal(
-            next_batch_1_past_key_values[i][0][:, :, 0:1][0], past[0][1:, :, 1 :2, :][0]
+            next_batch_1_past_key_values[i][0][:, :, -1:], past[0][1:, :, -1:, :]
         )
 
-        assert torch.equal(next_batch_0_past_key_values[i][1][0, 0,0:128], past[1][0][0][1:129])
+        assert torch.equal(next_batch_0_past_key_values[i][1][0, :, -2:], past[1][0])
         assert torch.equal(
-            next_batch_1_past_key_values[i][1][:, :, 0:1][0], past[1][1:, :, 1 :2, :][0]
+            next_batch_1_past_key_values[i][1][:, :, -1:], past[1][1:, :, -1:, :]
         )
 
-    generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
-
     for _ in range(
-        default_multi_requests_causal_lm_batch.requests[1].stopping_criteria.max_new_tokens - 2
+        default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens - 2
     ):
-        generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
+        generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
         assert len(generations) == len(next_batch)
 
-    generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
+    generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
     assert next_batch is not None
 
     assert len(generations) == 3
-    assert generations[2].generated_text.text == "ing the effect of a"
-
+    assert generations[2].generated_text.text == ".java:784)"
     assert (
         generations[2].request_id
-        == default_multi_requests_causal_lm_batch.requests[1].data.id
+        == default_multi_requests_causal_lm_batch.requests[1].id
     )
     assert (
         generations[2].generated_text.generated_tokens
-        == default_multi_requests_causal_lm_batch.requests[1].stopping_criteria.max_new_tokens
+        == default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens
     )
 
     next_batch = next_batch.filter(
-        [next_batch.requests[0].data.id, next_batch.requests[1].data.id]
+        [next_batch.requests[0].id, next_batch.requests[1].id]
     )
 
     for _ in range(
-        default_causal_lm_batch.requests[0].stopping_criteria.max_new_tokens
-        - default_multi_requests_causal_lm_batch.requests[1].stopping_criteria.max_new_tokens
+        default_causal_lm_batch.stopping_criterias[0].max_new_tokens
+        - default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens
         - 2
     ):
-        generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
+        generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
         assert len(generations) == len(next_batch)
 
-    generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
+    generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
     assert next_batch is not None
 
     assert len(generations) == 2
-    assert generations[0].generated_text.text == "ing the effect of a new method for the detection"
-    assert generations[0].request_id == default_causal_lm_batch.requests[0].data.id
+    assert generations[0].generated_text.text == ".java:784) at net.minecraft."
+    assert generations[0].request_id == default_causal_lm_batch.requests[0].id
     assert (
         generations[0].generated_text.generated_tokens
-        == default_causal_lm_batch.requests[0].stopping_criteria.max_new_tokens
+        == default_causal_lm_batch.stopping_criterias[0].max_new_tokens
     )
 
-    next_batch = next_batch.filter([next_batch.requests[1].data.id])
+    next_batch = next_batch.filter([next_batch.requests[1].id])
 
     for _ in range(
-        default_multi_requests_causal_lm_batch.requests[0].stopping_criteria.max_new_tokens
-        - default_causal_lm_batch.requests[0].stopping_criteria.max_new_tokens
-        - default_multi_requests_causal_lm_batch.requests[1].stopping_criteria.max_new_tokens
+        default_multi_requests_causal_lm_batch.stopping_criterias[0].max_new_tokens
+        - default_causal_lm_batch.stopping_criterias[0].max_new_tokens
+        - default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens
         - 4
     ):
-        generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
+        generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
         assert len(generations) == len(next_batch)
 
-    generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
+    generations, next_batch, _ = default_causal_lm.generate_token(next_batch)
     assert next_batch is None
 
     assert len(generations) == 1
-    assert generations[0].generated_text.text == "ing the effect of a new method for the detection"
+    assert generations[0].generated_text.text == ".java:784) at net.minecraft."
     assert (
         generations[0].request_id
-        == default_multi_requests_causal_lm_batch.requests[0].data.id
+        == default_multi_requests_causal_lm_batch.requests[0].id
     )
     assert (
         generations[0].generated_text.generated_tokens
-        == default_multi_requests_causal_lm_batch.requests[0].stopping_criteria.max_new_tokens
+        == default_multi_requests_causal_lm_batch.stopping_criterias[0].max_new_tokens
     )
diff --git a/server/tests/models/test_grammar.py b/server/tests/models/test_grammar.py
deleted file mode 100644
index b5e65620a..000000000
--- a/server/tests/models/test_grammar.py
+++ /dev/null
@@ -1,245 +0,0 @@
-# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
-import json
-import pytest
-import torch
-
-from copy import copy
-
-from text_generation_server.pb import generate_pb2
-from text_generation_server.models import get_model
-from text_generation_server.models.causal_lm import (
-    CausalLMBatch,
-    PAD_SEQUENCE_TO_MULTIPLE_OF,
-)
-
-PAD_TOKEN=0
-
-
-@pytest.fixture
-def default_pb_grammar_parameters():
-    grammar_schema = {
-        "properties": {
-            "activity": {
-                "type": "string"
-            },
-            "animals": {
-                "items": {
-                    "type":"string"
-                },
-                "type": "array"
-            }
-        },
-        "required": ["activity", "animals"]
-    }
-    return generate_pb2.NextTokenChooserParameters(
-        temperature=1.0,
-        repetition_penalty=1.3,
-        top_k=0,
-        top_p=1.0,
-        typical_p=1.0,
-        do_sample=False,
-        grammar_type=generate_pb2.GrammarType.GRAMMAR_TYPE_JSON,
-        grammar=json.dumps(grammar_schema).encode('utf-8'),
-    )
-
-
-@pytest.fixture(scope="session")
-def default_grammar_response():
-    return [
-        29912, 376, 29874, 312, 2068, 1115, 29871, 13, 29908, 29890,
-        638, 292, 613, 259, 376, 273, 3039, 29879, 1115,518, 1678,
-        376, 26169, 3284, 4117, 3284, 336, 617, 6150, 3108, 500, 2
-    ]
-
-
-@pytest.fixture(scope="session")
-def default_causal_lm():
-    return get_model("meta-llama/Llama-2-7b-hf", None, None, None, None)
-
-
-@pytest.fixture(scope="session")
-def default_tokenizer(default_causal_lm):
-    default_causal_lm.tokenizer.pad_token_id = PAD_TOKEN
-    return default_causal_lm.tokenizer
-
-
-@pytest.fixture
-def default_pb_request(default_pb_parameters):
-    return generate_pb2.Request(
-        id=0,
-        inputs="Test",
-        prefill_logprobs=True,
-        truncate=PAD_SEQUENCE_TO_MULTIPLE_OF,
-        parameters=default_pb_parameters,
-        stopping_parameters=generate_pb2.StoppingCriteriaParameters(stop_sequences=[], max_new_tokens=10),
-    )
-
-
-@pytest.fixture
-def default_pb_grammar_request(default_pb_grammar_parameters):
-    return generate_pb2.Request(
-        id=1,
-        inputs=f"Please use the following JSON schema to generate the output: I saw a puppy a cat and a raccoon during my bike ride in the park",
-        prefill_logprobs=True,
-        truncate=PAD_SEQUENCE_TO_MULTIPLE_OF,
-        parameters=default_pb_grammar_parameters,
-        stopping_parameters=generate_pb2.StoppingCriteriaParameters(stop_sequences=[], max_new_tokens=50),
-    )
-
-
-@pytest.fixture
-def default_pb_batch(default_pb_request):
-    return generate_pb2.Batch(id=0, requests=[default_pb_request], size=1)
-
-
-@pytest.fixture
-def default_pb_grammar_batch(default_pb_grammar_request):
-    return generate_pb2.Batch(id=1, requests=[default_pb_grammar_request], size=1)
-
-
-@pytest.fixture
-def default_causal_lm_batch(default_pb_batch, default_tokenizer):
-    return CausalLMBatch.from_pb(
-        default_pb_batch, default_tokenizer, torch.float32, torch.device("hpu")
-    )
-
-
-@pytest.fixture
-def default_causal_lm_grammar_batch(default_pb_grammar_batch, default_tokenizer):
-    return CausalLMBatch.from_pb(
-        default_pb_grammar_batch, default_tokenizer, torch.float32, torch.device("hpu")
-    )
-
-
-@pytest.fixture
-def default_two_causal_lm_grammar_batches(default_pb_grammar_request, default_tokenizer):
-    req_0 = default_pb_grammar_request
-    req_0.id = 0
-    req_1 = copy(default_pb_grammar_request)
-    req_1.id = 1
-
-    batch_0 = generate_pb2.Batch(id=0, requests=[req_0], size=1)
-    batch_1 = generate_pb2.Batch(id=1, requests=[req_1], size=1)
-    return [
-        CausalLMBatch.from_pb(
-            b, default_tokenizer, torch.float32, torch.device("hpu")
-        ) for b in [batch_0, batch_1]
-    ]
-
-
-def test_single_grammar_batch(
-    default_causal_lm, default_causal_lm_grammar_batch, default_grammar_response
-):
-    counter = 0
-    batch = default_causal_lm_grammar_batch
-
-    # prefill request
-    generations, next_batch, _ = default_causal_lm.generate_token([batch])
-
-    # generate untill done
-    while next_batch is not None:
-        generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
-        assert len(generations) == 1
-        assert generations[0].tokens.token_ids[0] == default_grammar_response[counter]
-        counter += 1
-    print(generations[0].generated_text.text)
-
-
-def test_multi_grammar_batches(
-    default_causal_lm, default_two_causal_lm_grammar_batches, default_grammar_response
-):
-    counter_0, counter_1 = 0, 0
-    batch_0, batch_1 = default_two_causal_lm_grammar_batches
-
-    # prefill first request
-    generations, next_batch, _ = default_causal_lm.generate_token([batch_0])
-    generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
-    assert len(generations) == 1
-    assert generations[0].tokens.token_ids[0] == default_grammar_response[counter_0]
-    counter_0 += 1
-
-    # prefill second request
-    generations, next_batch_1, _ = default_causal_lm.generate_token([batch_1])
-
-    # concatenate and generate
-    generations, next_batch, _ = default_causal_lm.generate_token([next_batch, next_batch_1])
-    assert len(generations) == 2
-    assert generations[0].tokens.token_ids[0] == default_grammar_response[counter_0]
-    assert generations[1].tokens.token_ids[0] == default_grammar_response[counter_1]
-    counter_0 += 1
-    counter_1 += 1
-
-    # generate untill first request is done
-    while generations[0].generated_text is None:
-        generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
-        assert len(generations) == 2
-        assert generations[0].tokens.token_ids[0] == default_grammar_response[counter_0]
-        assert generations[1].tokens.token_ids[0] == default_grammar_response[counter_1]
-        counter_0 += 1
-        counter_1 += 1
-
-    # filter finished request
-    response = generations[0].generated_text.text
-    next_batch = next_batch.filter([next_batch.requests[1].data.id])
-
-    # generate last tokens for second request
-    while next_batch is not None:
-        generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
-        assert len(generations) == 1
-        assert generations[0].tokens.token_ids[0] == default_grammar_response[counter_1]
-        counter_1 += 1
-
-    assert response == generations[0].generated_text.text
-
-
-def test_grammar_and_causal_batch(
-    default_causal_lm, default_causal_lm_grammar_batch, default_causal_lm_batch, default_grammar_response
-):
-    counter = 0
-    generations, next_batch, _ = default_causal_lm.generate_token([default_causal_lm_grammar_batch])
-    generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
-    assert len(generations) == 1
-    assert generations[0].tokens.token_ids[0] == default_grammar_response[counter]
-    counter += 1
-
-    generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
-    assert len(generations) == 1
-    assert generations[0].tokens.token_ids[0] == default_grammar_response[counter]
-    counter += 1
-
-    # prefill second request
-    generations, next_batch_1, _ = default_causal_lm.generate_token([default_causal_lm_batch])
-
-    # concatenate and generate
-    generations, next_batch, _ = default_causal_lm.generate_token([next_batch, next_batch_1])
-    assert len(generations) == 2
-    assert generations[0].tokens.token_ids[0] == default_grammar_response[counter]
-    counter += 1
-
-    # generate untill second request is done
-    for _ in range(
-        next_batch.requests[1].stopping_criteria.max_new_tokens - 1
-    ):
-        generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
-        assert len(generations) == 2
-        assert generations[0].tokens.token_ids[0] == default_grammar_response[counter]
-        counter += 1
-
-    # filter finished request
-    assert len(generations) == 2
-    assert (
-        generations[1].request_id == next_batch.requests[1].data.id
-    )
-    assert (
-        generations[1].generated_text.generated_tokens == next_batch.requests[1].stopping_criteria.max_new_tokens
-    )
-    assert generations[1].generated_text.text == "ing the effect of a new method for the detection"
-    next_batch = next_batch.filter([next_batch.requests[0].data.id])
-
-    # generate untill done
-    while next_batch is not None:
-        generations, next_batch, _ = default_causal_lm.generate_token([next_batch])
-        assert len(generations) == 1
-        assert generations[0].tokens.token_ids[0] == default_grammar_response[counter]
-        counter += 1
diff --git a/server/tests/models/test_seq2seq_lm.py b/server/tests/models/test_seq2seq_lm.py
index ba9f55789..735ab5eb5 100644
--- a/server/tests/models/test_seq2seq_lm.py
+++ b/server/tests/models/test_seq2seq_lm.py
@@ -61,7 +61,6 @@ def default_multi_requests_seq2seq_lm_batch(default_pb_request, mt0_small_tokeni
     )
 
 
-@pytest.mark.skip("seq2seq model not enabled on HPU yet")
 def test_batch_from_pb(default_pb_batch, default_seq2seq_lm_batch):
     batch = default_seq2seq_lm_batch
     sequence_length = len(default_seq2seq_lm_batch.input_ids[0])
@@ -93,18 +92,15 @@ def test_batch_from_pb(default_pb_batch, default_seq2seq_lm_batch):
     assert batch.max_decoder_input_length == batch.decoder_input_lengths[0]
 
 
-@pytest.mark.skip("seq2seq model not enabled on HPU yet")
 def test_batch_concatenate_no_prefill(default_seq2seq_lm_batch):
     with pytest.raises(ValueError):
         Seq2SeqLMBatch.concatenate([default_seq2seq_lm_batch, default_seq2seq_lm_batch])
 
 
-@pytest.mark.skip("seq2seq model not enabled on HPU yet")
 def test_seq2seq_lm_batch_type(default_seq2seq_lm):
     assert default_seq2seq_lm.batch_type == Seq2SeqLMBatch
 
 
-@pytest.mark.skip("seq2seq model not enabled on HPU yet")
 def test_seq2seq_lm_generate_token(default_seq2seq_lm, default_seq2seq_lm_batch):
     sequence_length = len(default_seq2seq_lm_batch.input_ids[0])
     generations, next_batch, _ = default_seq2seq_lm.generate_token(
@@ -172,7 +168,6 @@ def test_seq2seq_lm_generate_token(default_seq2seq_lm, default_seq2seq_lm_batch)
     assert generations[0].request_id == 0
 
 
-@pytest.mark.skip("seq2seq model not enabled on HPU yet")
 def test_seq2seq_lm_generate_token_completion(
     default_seq2seq_lm, default_seq2seq_lm_batch
 ):
@@ -190,7 +185,6 @@ def test_seq2seq_lm_generate_token_completion(
     assert generations[0].generated_text.generated_tokens == 7
 
 
-@pytest.mark.skip("seq2seq model not enabled on HPU yet")
 def test_seq2seq_lm_generate_token_completion_multi(
     default_seq2seq_lm, default_multi_requests_seq2seq_lm_batch
 ):
@@ -228,7 +222,6 @@ def test_seq2seq_lm_generate_token_completion_multi(
     assert generations[0].generated_text.generated_tokens == 7
 
 
-@pytest.mark.skip("seq2seq model not enabled on HPU yet")
 def test_batch_concatenate(
     default_seq2seq_lm,
     default_seq2seq_lm_batch,
diff --git a/server/tests/models/test_starcoder.py b/server/tests/models/test_starcoder.py
deleted file mode 100644
index 05bdd72b7..000000000
--- a/server/tests/models/test_starcoder.py
+++ /dev/null
@@ -1,372 +0,0 @@
-# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
-import pytest
-import torch
-
-from copy import copy
-
-from text_generation_server.pb import generate_pb2
-from text_generation_server.models import get_model
-from text_generation_server.models.starcoder import StarCoderCausalLMBatch
-from text_generation_server.models.causal_lm import (
-    PREFILL_BATCH_BUCKET_SIZE,
-    PAD_SEQUENCE_TO_MULTIPLE_OF,
-    MAX_TOTAL_TOKENS,
-    BATCH_BUCKET_SIZE,
-)
-PAD_TOKEN=0
-
-
-@pytest.fixture(scope="session")
-def default_starcoder():
-    return get_model("bigcode/starcoder", None, None, None, None)
-
-
-@pytest.fixture(scope="session")
-def default_tokenizer(default_starcoder):
-    default_starcoder.tokenizer.pad_token_id = PAD_TOKEN
-    return default_starcoder.tokenizer
-
-
-@pytest.fixture
-def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
-    return generate_pb2.Request(
-        id=0,
-        inputs="Test",
-        prefill_logprobs=True,
-        truncate=PAD_SEQUENCE_TO_MULTIPLE_OF,
-        parameters=default_pb_parameters,
-        stopping_parameters=default_pb_stop_parameters,
-    )
-
-
-@pytest.fixture
-def default_pb_batch(default_pb_request):
-    return generate_pb2.Batch(id=0, requests=[default_pb_request], size=1)
-
-
-@pytest.fixture
-def default_starcoder_batch(default_pb_batch, default_tokenizer):
-    return StarCoderCausalLMBatch.from_pb(
-        default_pb_batch, default_tokenizer, torch.float32, torch.device("hpu")
-    )
-
-
-@pytest.fixture
-def default_multi_requests_starcoder_batch(default_pb_request, default_tokenizer):
-    req_0 = copy(default_pb_request)
-    req_0.id = 1
-    req_1 = default_pb_request
-    req_1.id = 2
-    req_1.stopping_parameters.max_new_tokens = 5
-
-    batch_pb = generate_pb2.Batch(id=1, requests=[req_0, req_1], size=2)
-    return StarCoderCausalLMBatch.from_pb(
-        batch_pb, default_tokenizer, torch.float32, torch.device("hpu")
-    )
-
-
-def test_starcoder_batch_type(default_starcoder):
-    assert default_starcoder.batch_type == StarCoderCausalLMBatch
-
-
-def test_batch_from_pb(default_pb_batch, default_starcoder_batch):
-    batch = default_starcoder_batch
-
-    assert batch.batch_id == default_pb_batch.id
-    assert len(batch.requests) == len(default_pb_batch.requests)
-
-    for r in range(0,len(default_pb_batch.requests)):
-        assert batch.requests[r].data == default_pb_batch.requests[r]
-
-    # For Gaudi we are adding padding of multiplication of bucket size
-    size_of_padded_to_bucket = ((default_pb_batch.size + PREFILL_BATCH_BUCKET_SIZE - 1) // PREFILL_BATCH_BUCKET_SIZE) * PREFILL_BATCH_BUCKET_SIZE
-
-    assert len(batch.input_ids) == size_of_padded_to_bucket
-    assert batch.input_ids.shape == torch.Size([4, 128])
-
-    assert batch.input_ids[0][-2] == 1006
-    assert batch.input_ids[1][-2] == 49
-    assert batch.input_ids[2][-2] == 49
-    assert batch.attention_mask[0][-2] == 1
-    assert batch.attention_mask[1][-2] == 1
-    assert batch.attention_mask[2][-2] == 1
-    assert torch.all(batch.attention_mask[0, :-3] == 0)
-
-    assert batch.past_key_values is None
-    assert all(
-        [
-            torch.equal(input_ids, request.all_input_ids[:batch.input_length + 1, 0])
-            for input_ids, request in zip(batch.input_ids, batch.requests)
-        ]
-    )
-
-    assert len(batch) == default_pb_batch.size
-
-    assert batch.max_input_length + 1 == default_pb_batch.requests[0].truncate
-
-
-def test_starcoder_generate_token(default_starcoder, default_starcoder_batch):
-
-    sequence_length = len(default_starcoder_batch.requests[0].all_input_ids)
-    generations, next_batch, _ = default_starcoder.generate_token([default_starcoder_batch])
-    padding = next_batch.requests[0].stopping_criteria.max_new_tokens
-
-    assert isinstance(next_batch, StarCoderCausalLMBatch)
-    assert len(next_batch.attention_mask[0]) == PAD_SEQUENCE_TO_MULTIPLE_OF
-    assert next_batch.requests[0].all_input_ids[-padding-2] == 1006
-
-    assert torch.all(next_batch.requests[0].all_input_ids[-padding-1:] == PAD_TOKEN)
-    assert torch.all(next_batch.requests[0].all_input_ids[:-padding-3] == PAD_TOKEN)
-
-    generations, next_batch, _ = default_starcoder.generate_token([default_starcoder_batch])
-    assert torch.all(next_batch.attention_mask[0][PAD_SEQUENCE_TO_MULTIPLE_OF-2:PAD_SEQUENCE_TO_MULTIPLE_OF] == 1)
-    assert torch.all(next_batch.attention_mask[0][:PAD_SEQUENCE_TO_MULTIPLE_OF-3] == 0)
-    assert torch.all(next_batch.attention_mask[0][PAD_SEQUENCE_TO_MULTIPLE_OF+1:] == 0)
-
-    assert next_batch.requests[0].all_input_ids[-padding-2] == 1006
-    assert next_batch.requests[0].all_input_ids[-padding-1] == 26
-    assert torch.all(next_batch.requests[0].all_input_ids[-padding:] == PAD_TOKEN)
-    assert torch.all(next_batch.requests[0].all_input_ids[:-padding-3] == PAD_TOKEN)
-
-    assert next_batch.input_length == PAD_SEQUENCE_TO_MULTIPLE_OF
-    assert next_batch.max_input_length == next_batch.input_length
-
-    assert next_batch.past_key_values is not None
-    assert all(
-        [p[0].shape == (MAX_TOTAL_TOKENS, 256) for p in next_batch.past_key_values]
-    )
-    assert all(
-        [p[1].shape == (MAX_TOTAL_TOKENS, 256) for p in next_batch.past_key_values]
-    )
-    assert all([generation.generated_text is None for generation in generations])
-    assert all([len(generation.prefill_tokens) == PAD_SEQUENCE_TO_MULTIPLE_OF-1 for generation in generations])
-    assert all([generation.tokens.token_ids[0] == 26 for generation in generations])
-    assert all([generation.tokens.texts[0] == "(" for generation in generations])
-    assert generations[0].request_id == 0
-
-
-def test_starcoder_generate_token_completion(
-    default_starcoder, default_starcoder_batch
-):
-
-    next_batch = default_starcoder_batch
-    generations, next_batch, _ = default_starcoder.generate_token([next_batch])
-
-    for _ in range(default_starcoder_batch.requests[0].stopping_criteria.max_new_tokens - 1):
-        generations, next_batch, _ = default_starcoder.generate_token([next_batch])
-        assert len(generations) == len(next_batch)
-
-    generations, next_batch, _ = default_starcoder.generate_token([next_batch])
-
-    assert next_batch is None
-
-    assert len(generations) == 1
-    assert generations[0].generated_text.text == '(self):\n        """\n        Test that the test'
-    assert generations[0].request_id == default_starcoder_batch.requests[0].data.id
-    assert (
-        generations[0].generated_text.generated_tokens
-        == default_starcoder_batch.requests[0].stopping_criteria.max_new_tokens
-    )
-
-
-def test_starcoder_generate_token_completion_multi(
-    default_starcoder, default_multi_requests_starcoder_batch
-):
-    next_batch = default_multi_requests_starcoder_batch
-    generations, next_batch, _ = default_starcoder.generate_token([next_batch])
-
-    for i in range(
-        default_multi_requests_starcoder_batch.requests[1].stopping_criteria.max_new_tokens - 1
-    ):
-        generations, next_batch, _ = default_starcoder.generate_token([next_batch])
-        assert len(generations) == len(next_batch)
-
-    generations, next_batch, _ = default_starcoder.generate_token([next_batch])
-    assert next_batch is not None
-
-    assert len(generations) == 2
-    assert generations[1].generated_text.text == '(self):\n        """'
-    assert (
-        generations[1].request_id
-        == default_multi_requests_starcoder_batch.requests[1].data.id
-    )
-    assert (
-        generations[1].generated_text.generated_tokens
-        == default_multi_requests_starcoder_batch.requests[1].stopping_criteria.max_new_tokens
-    )
-
-    next_batch = next_batch.filter([next_batch.requests[0].data.id])
-
-    for _ in range(
-        default_multi_requests_starcoder_batch.requests[0].stopping_criteria.max_new_tokens - default_multi_requests_starcoder_batch.requests[1].stopping_criteria.max_new_tokens - 1
-    ):
-        generations, next_batch, _ = default_starcoder.generate_token([next_batch])
-        assert len(generations) == len(next_batch)
-
-    generations, next_batch, _ = default_starcoder.generate_token([next_batch])
-
-    assert next_batch is None
-
-    assert len(generations) == 1
-    assert generations[0].generated_text.text == '(self):\n        """\n        Test that the test'
-    assert (
-        generations[0].request_id
-        == default_multi_requests_starcoder_batch.requests[0].data.id
-    )
-    assert (
-        generations[0].generated_text.generated_tokens
-        == default_multi_requests_starcoder_batch.requests[0].stopping_criteria.max_new_tokens
-    )
-
-
-def test_batch_concatenate(
-    default_starcoder, default_starcoder_batch, default_multi_requests_starcoder_batch
-):
-    next_batch_0 = default_starcoder_batch
-    _, next_batch_0, _ = default_starcoder.generate_token([next_batch_0])
-    _, next_batch_0, _ = default_starcoder.generate_token([next_batch_0])
-    _, next_batch_0, _ = default_starcoder.generate_token([next_batch_0])
-
-    next_batch_1 = default_multi_requests_starcoder_batch
-    _, next_batch_1, _ = default_starcoder.generate_token([next_batch_1])
-    _, next_batch_1, _ = default_starcoder.generate_token([next_batch_1])
-
-    # Clone past_key_values before concatenating to compare after,
-    # because they are removed from the concatenated batches
-    next_batch_0_past_key_values = [x.clone() for x in next_batch_0.past_key_values]
-    next_batch_1_past_key_values = [x.clone() for x in next_batch_1.past_key_values]
-
-    next_batch = StarCoderCausalLMBatch.concatenate([next_batch_0, next_batch_1])
-
-    assert torch.equal(next_batch.requests[0].all_input_ids, next_batch_0.requests[0].all_input_ids)
-    assert torch.equal(next_batch.requests[1].all_input_ids, next_batch_1.requests[0].all_input_ids)
-    assert torch.equal(next_batch.requests[2].all_input_ids, next_batch_1.requests[1].all_input_ids)
-
-
-    assert torch.all(
-            next_batch.attention_mask[0:2, -next_batch.right_padding - 2: -next_batch.right_padding] == 1
-    )
-    assert torch.all(
-            next_batch.attention_mask[2, -next_batch.right_padding - 3: -next_batch.right_padding] == 1
-    )
-    assert torch.all(
-            next_batch.attention_mask[3, -next_batch.right_padding - 2: -next_batch.right_padding] == 1
-    )
-
-    assert torch.all(
-        next_batch.attention_mask[0:2, :-next_batch.right_padding-2] == 0)
-    assert torch.all(
-        next_batch.attention_mask[2, :-next_batch.right_padding-4] == 0)
-    assert torch.all(
-        next_batch.attention_mask[3, :-next_batch.right_padding-3] == 0)
-
-    assert next_batch.batch_id == 0
-    assert next_batch.input_ids[0,-next_batch.right_padding - 2] == 1006
-    assert next_batch.input_ids[0,-next_batch.right_padding - 1] == 26
-
-    assert next_batch.max_input_length == 129
-
-    assert torch.all(next_batch.input_ids[0,-next_batch.right_padding:] == PAD_TOKEN)
-    assert torch.all(next_batch.input_ids[1,-next_batch.right_padding:] == PAD_TOKEN)
-    assert torch.all(next_batch.input_ids[2,-next_batch.right_padding:] == PAD_TOKEN)
-    assert torch.all(next_batch.input_ids[3,-next_batch.right_padding:] == PAD_TOKEN)
-
-    assert next_batch.input_length == PAD_SEQUENCE_TO_MULTIPLE_OF  +1
-    assert next_batch.max_input_length == PAD_SEQUENCE_TO_MULTIPLE_OF + 1
-
-    assert next_batch.requests[0] == next_batch_0.requests[0]
-    assert next_batch.requests[1:] == next_batch_1.requests
-
-    assert next_batch.requests[0].stopping_criteria == next_batch_0.requests[0].stopping_criteria
-    assert next_batch.requests[1].stopping_criteria == next_batch_1.requests[0].stopping_criteria
-    assert next_batch.requests[2].stopping_criteria == next_batch_1.requests[1].stopping_criteria
-
-    assert next_batch.past_key_values is not None
-
-    assert all([p[0].shape == (2048, 256) for p in next_batch.past_key_values])
-    assert all([p[1].shape == (2048, 256) for p in next_batch.past_key_values])
-
-    assert next_batch.past_key_values is not None
-
-    for i, past in enumerate(next_batch.past_key_values):
-        assert torch.equal(next_batch_0_past_key_values[i][0,0,0:128], past[0][1:129][0, 0:128])
-        assert torch.equal(next_batch_0_past_key_values[i][0,1,0:128], past[1][1:129][0, 0:128])
-        assert torch.equal(
-            next_batch_1_past_key_values[i][:, :, 0:1][0][0][0], past[0][1:, :][0][0]
-        )
-
-        assert torch.equal(
-            next_batch_1_past_key_values[i][1:, :, 0:1][0][0][0], past[1][1:, :][0][0]
-        )
-
-    generations, next_batch, _ = default_starcoder.generate_token([next_batch])
-
-    for _ in range(
-        default_multi_requests_starcoder_batch.requests[1].stopping_criteria.max_new_tokens - 2
-    ):
-        generations, next_batch, _ = default_starcoder.generate_token([next_batch])
-        assert len(generations) == len(next_batch)
-
-    generations, next_batch, _ = default_starcoder.generate_token([next_batch])
-    assert next_batch is not None
-
-    assert len(generations) == 3
-    assert generations[2].generated_text.text == '(self):\n        """'
-
-    assert (
-        generations[2].request_id
-        == default_multi_requests_starcoder_batch.requests[1].data.id
-    )
-    assert (
-        generations[2].generated_text.generated_tokens
-        == default_multi_requests_starcoder_batch.requests[1].stopping_criteria.max_new_tokens
-    )
-
-    next_batch = next_batch.filter(
-        [next_batch.requests[0].data.id, next_batch.requests[1].data.id]
-    )
-
-    for _ in range(
-        default_starcoder_batch.requests[0].stopping_criteria.max_new_tokens
-        - default_multi_requests_starcoder_batch.requests[1].stopping_criteria.max_new_tokens
-        - 2
-    ):
-        generations, next_batch, _ = default_starcoder.generate_token([next_batch])
-        assert len(generations) == len(next_batch)
-
-    generations, next_batch, _ = default_starcoder.generate_token([next_batch])
-    assert next_batch is not None
-
-    assert len(generations) == 2
-    assert generations[0].generated_text.text == '(self):\n        """\n        Test that the test'
-    assert generations[0].request_id == default_starcoder_batch.requests[0].data.id
-    assert (
-        generations[0].generated_text.generated_tokens
-        == default_starcoder_batch.requests[0].stopping_criteria.max_new_tokens
-    )
-
-    next_batch = next_batch.filter([next_batch.requests[1].data.id])
-
-    for _ in range(
-        default_multi_requests_starcoder_batch.requests[0].stopping_criteria.max_new_tokens
-        - default_starcoder_batch.requests[0].stopping_criteria.max_new_tokens
-        - default_multi_requests_starcoder_batch.requests[1].stopping_criteria.max_new_tokens
-        - 4
-    ):
-        generations, next_batch, _ = default_starcoder.generate_token([next_batch])
-        assert len(generations) == len(next_batch)
-
-    generations, next_batch, _ = default_starcoder.generate_token([next_batch])
-    assert next_batch is None
-
-    assert len(generations) == 1
-    assert generations[0].generated_text.text == '(self):\n        """\n        Test that the test'
-    assert (
-        generations[0].request_id
-        == default_multi_requests_starcoder_batch.requests[0].data.id
-    )
-    assert (
-        generations[0].generated_text.generated_tokens
-        == default_multi_requests_starcoder_batch.requests[0].stopping_criteria.max_new_tokens
-    )
diff --git a/server/tests/utils/test_tokens.py b/server/tests/utils/test_tokens.py
index 94d2a8f2c..5db327767 100644
--- a/server/tests/utils/test_tokens.py
+++ b/server/tests/utils/test_tokens.py
@@ -1,26 +1,12 @@
-# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
-import pytest
 import torch
-from transformers import AutoTokenizer
-
 from text_generation_server.utils.tokens import (
     StopSequenceCriteria,
     StoppingCriteria,
     FinishReason,
     batch_top_tokens,
-    make_tokenizer_optional,
 )
 
 
-@pytest.fixture
-def skip_tokenizer_env_var():
-    import os
-    os.environ["SKIP_TOKENIZER_IN_TGI"] = "true"
-    yield
-    del os.environ['SKIP_TOKENIZER_IN_TGI']
-
-
 def test_stop_sequence_criteria():
     criteria = StopSequenceCriteria("/test;")
 
@@ -100,33 +86,3 @@ def test_batch_top_tokens():
     assert topn_tok_logprobs[2] == [[-1, -2, -3, -3]]
     assert topn_tok_logprobs[3] == [[-1, -2, -3, -3]]
     assert topn_tok_logprobs[4] == [[-1, -2, -3, -3, -4]]
-
-
-def test_pass_through_tokenizer(skip_tokenizer_env_var):
-    tokenizer = AutoTokenizer.from_pretrained(
-        'meta-llama/Llama-2-7b-chat-hf',
-        revision=None,
-        padding_side="left",
-        truncation_side="left",
-    )
-    tokenizer.pad_token_id = 2
-    make_tokenizer_optional(tokenizer)
-
-    input = ["1,  1724, 338,  6483,  6509, 29973", "?"]
-    tokenized_inputs = tokenizer(
-        input,
-        return_tensors="pt",
-        padding="max_length",
-        return_token_type_ids=False,
-        truncation=True,
-        max_length=1024,
-    )
-    assert tokenized_inputs['input_ids'].size() == torch.Size([2, 1024])
-    assert torch.equal(tokenized_inputs['input_ids'][0][1018:], torch.tensor([1, 1724, 338, 6483, 6509, 29973]))
-    assert torch.equal(tokenized_inputs['input_ids'][1][1023:], torch.tensor([tokenizer.pad_token_id]))
-    decoded_tokens = tokenizer.decode(tokenized_inputs["input_ids"][0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
-    assert decoded_tokens.split(',')[1018:] == ['1', '1724', '338', '6483', '6509', '29973']
-
-
-if __name__ == "__main__":
-    test_pass_through_tokenizer()
diff --git a/server/tests/utils/test_watermark.py b/server/tests/utils/test_watermark.py
index c7c83e8c8..5d0aaf050 100644
--- a/server/tests/utils/test_watermark.py
+++ b/server/tests/utils/test_watermark.py
@@ -1,8 +1,7 @@
-# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
+# test_watermark_logits_processor.py
 
 import os
 import numpy as np
-import pytest
 import torch
 from text_generation_server.utils.watermark import WatermarkLogitsProcessor
 
@@ -11,71 +10,35 @@ GAMMA = os.getenv("WATERMARK_GAMMA", 0.5)
 DELTA = os.getenv("WATERMARK_DELTA", 2.0)
 
 
-@pytest.fixture
-def hpu_device():
-    return torch.device("hpu")
-
-
-@pytest.fixture
-def input_ids_list():
-    return [101, 2036, 3731, 102, 2003, 103]
-
-
-@pytest.fixture
-def input_ids_tensor(hpu_device):
-    return torch.tensor(
-        [[101, 2036, 3731, 102, 2003, 103]],
-        dtype=torch.int64,
-        device=hpu_device
-    )
-
-
-@pytest.fixture
-def scores(hpu_device):
-    return torch.tensor(
-        [[0.5, 0.3, 0.2, 0.8], [0.1, 0.2, 0.7, 0.9]],
-        device=hpu_device
-    )
-
-
-def test_seed_rng(input_ids_list, hpu_device):
-    processor = WatermarkLogitsProcessor(device=hpu_device)
-    processor._seed_rng(input_ids_list)
+def test_seed_rng():
+    input_ids = [101, 2036, 3731, 102, 2003, 103]
+    processor = WatermarkLogitsProcessor()
+    processor._seed_rng(input_ids)
     assert isinstance(processor.rng, torch.Generator)
 
 
-def test_seed_rng_tensor(input_ids_tensor, hpu_device):
-    processor = WatermarkLogitsProcessor(device=hpu_device)
-    processor._seed_rng(input_ids_tensor)
-    assert isinstance(processor.rng, torch.Generator)
-
-
-def test_get_greenlist_ids(input_ids_list, hpu_device):
-    processor = WatermarkLogitsProcessor(device=hpu_device)
-    result = processor._get_greenlist_ids(input_ids_list, 10, hpu_device)
+def test_get_greenlist_ids():
+    input_ids = [101, 2036, 3731, 102, 2003, 103]
+    processor = WatermarkLogitsProcessor()
+    result = processor._get_greenlist_ids(input_ids, 10, torch.device("cpu"))
     assert max(result) <= 10
     assert len(result) == int(10 * 0.5)
 
 
-def test_get_greenlist_ids_tensor(input_ids_tensor, hpu_device):
-    processor = WatermarkLogitsProcessor(device=hpu_device)
-    result = processor._get_greenlist_ids(input_ids_tensor, 10, hpu_device)
-    assert max(result) <= 10
-    assert len(result) == int(10 * 0.5)
-
-
-def test_calc_greenlist_mask(scores, hpu_device):
-    processor = WatermarkLogitsProcessor(device=hpu_device)
-    greenlist_token_ids = torch.tensor([2, 3], device=hpu_device)
+def test_calc_greenlist_mask():
+    processor = WatermarkLogitsProcessor()
+    scores = torch.tensor([[0.5, 0.3, 0.2, 0.8], [0.1, 0.2, 0.7, 0.9]])
+    greenlist_token_ids = torch.tensor([2, 3])
     result = processor._calc_greenlist_mask(scores, greenlist_token_ids)
     assert result.tolist() == [[False, False, False, False], [False, False, True, True]]
     assert result.shape == scores.shape
 
 
-def test_bias_greenlist_logits(scores, hpu_device):
-    processor = WatermarkLogitsProcessor(device=hpu_device)
+def test_bias_greenlist_logits():
+    processor = WatermarkLogitsProcessor()
+    scores = torch.tensor([[0.5, 0.3, 0.2, 0.8], [0.1, 0.2, 0.7, 0.9]])
     green_tokens_mask = torch.tensor(
-        [[False, False, True, True], [False, False, False, True]], device=hpu_device
+        [[False, False, True, True], [False, False, False, True]]
     )
     greenlist_bias = 2.0
     result = processor._bias_greenlist_logits(scores, green_tokens_mask, greenlist_bias)
@@ -83,13 +46,9 @@ def test_bias_greenlist_logits(scores, hpu_device):
     assert result.shape == scores.shape
 
 
-def test_call(input_ids_list, scores, hpu_device):
-    processor = WatermarkLogitsProcessor(device=hpu_device)
-    result = processor(input_ids_list, scores)
-    assert result.shape == scores.shape
-
-
-def test_call_tensor(input_ids_tensor, scores, hpu_device):
-    processor = WatermarkLogitsProcessor(device=hpu_device)
-    result = processor(input_ids_tensor, scores)
+def test_call():
+    input_ids = [101, 2036, 3731, 102, 2003, 103]
+    processor = WatermarkLogitsProcessor()
+    scores = torch.tensor([[0.5, 0.3, 0.2, 0.8], [0.1, 0.2, 0.7, 0.9]])
+    result = processor(input_ids, scores)
     assert result.shape == scores.shape
diff --git a/server/text_generation_server/cli.py b/server/text_generation_server/cli.py
index 74b870249..ad623ccc8 100644
--- a/server/text_generation_server/cli.py
+++ b/server/text_generation_server/cli.py
@@ -1,8 +1,4 @@
-# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
 import os
-import psutil
-import signal
 import sys
 import typer
 
@@ -18,6 +14,8 @@ app = typer.Typer()
 
 class Quantization(str, Enum):
     bitsandbytes = "bitsandbytes"
+    bitsandbytes_nf4 = "bitsandbytes-nf4"
+    bitsandbytes_fp4 = "bitsandbytes-fp4"
     gptq = "gptq"
     awq = "awq"
     eetq = "eetq"
@@ -44,6 +42,9 @@ def serve(
     otlp_endpoint: Optional[str] = None,
 ):
     if sharded:
+        assert (
+            os.getenv("RANK", None) is not None
+        ), "RANK must be set when sharded is True"
         assert (
             os.getenv("WORLD_SIZE", None) is not None
         ), "WORLD_SIZE must be set when sharded is True"
@@ -76,73 +77,26 @@ def serve(
 
     # Downgrade enum into str for easier management later on
     quantize = None if quantize is None else quantize.value
-    dtype = "bfloat16" if dtype is None else dtype.value
-
-    logger.info("CLI SHARDED = {} DTYPE = {}".format(sharded, dtype))
-
-    if sharded:
-        tgi_file =  Path(__file__).resolve().parent / "tgi_service.py"
-        num_shard = int(os.getenv("WORLD_SIZE", "1"))
-        logger.info("CLI SHARDED = {}".format(num_shard))
-        import subprocess
-
-        cmd = f"deepspeed --num_nodes 1 --num_gpus {num_shard} --no_local_rank {tgi_file}"
-        cmd += f" --model_id {model_id} --revision {revision} --sharded {sharded}"
-        cmd += f" --dtype {dtype} --trust_remote_code {trust_remote_code} --uds_path {uds_path}"
-        if speculate is not None:
-            cmd += f"--speculate {speculate}"
-        logger.info("CLI server start deepspeed ={} ".format(cmd))
-        sys.stdout.flush()
-        sys.stderr.flush()
-        with subprocess.Popen(cmd, shell=True, executable="/bin/bash") as proc:
-            do_terminate = False
-            current_handler = signal.getsignal(signal.SIGTERM)
-            def terminate_handler(sig, frame):
-                nonlocal do_terminate
-                do_terminate = True
-                if callable(current_handler):
-                    current_handler(sig, frame)
-
-            signal.signal(signal.SIGTERM, terminate_handler)
-
-            finished = False
-            while not finished:
-                try:
-                    if do_terminate:
-                        parent = psutil.Process(proc.pid)
-                        all_procs = parent.children(recursive=True) + [parent]
-                        for p in all_procs:
-                            try:
-                                p.terminate()
-                            except psutil.NoSuchProcess:
-                                pass
-                        _, alive = psutil.wait_procs(all_procs, timeout=30)
-                        for p in alive:
-                            p.kill()
-
-                        do_terminate = False
-
-                    proc.wait(timeout=3)
-                except subprocess.TimeoutExpired:
-                    pass
-                else:
-                    finished = True
-
-            sys.stdout.flush()
-            sys.stderr.flush()
-            if proc.returncode != 0:
-                logger.error(f"{cmd}  exited with status = {proc.returncode}")
-                return proc.returncode
-    else:
-        server.serve(
-            model_id,
-            revision,
-            sharded,
-            speculate,
-            dtype,
-            trust_remote_code,
-            uds_path
+    dtype = None if dtype is None else dtype.value
+    if dtype is not None and quantize not in {
+        None,
+        "bitsandbytes",
+        "bitsandbytes-nf4",
+        "bitsandbytes-fp4",
+    }:
+        raise RuntimeError(
+            "Only 1 can be set between `dtype` and `quantize`, as they both decide how goes the final model."
         )
+    server.serve(
+        model_id,
+        revision,
+        sharded,
+        quantize,
+        speculate,
+        dtype,
+        trust_remote_code,
+        uds_path,
+    )
 
 
 @app.command()
@@ -309,24 +263,30 @@ def download_weights(
         )
 
         # Safetensors final filenames
-        local_st_files = [p.parent / f"{p.stem.lstrip('pytorch_')}.safetensors" for p in local_pt_files]
+        local_st_files = [
+            p.parent / f"{p.stem.lstrip('pytorch_')}.safetensors"
+            for p in local_pt_files
+        ]
         try:
             import transformers
-            from transformers import AutoConfig
+            import json
 
-            config = AutoConfig.from_pretrained(
-                model_id,
-                revision=revision,
-            )
-            architecture = config.architectures[0]
+            if is_local_model:
+                config_filename = os.path.join(model_id, "config.json")
+            else:
+                config_filename = hf_hub_download(
+                    model_id, revision=revision, filename="config.json"
+                )
+            with open(config_filename, "r") as f:
+                config = json.load(f)
+            architecture = config["architectures"][0]
 
             class_ = getattr(transformers, architecture)
 
             # Name for this varible depends on transformers version.
             discard_names = getattr(class_, "_tied_weights_keys", [])
-            discard_names.extend(getattr(class_, "_keys_to_ignore_on_load_missing", []))
 
-        except Exception:
+        except Exception as e:
             discard_names = []
         # Convert pytorch weights to safetensors
         utils.convert_files(local_pt_files, local_st_files, discard_names)
@@ -344,6 +304,8 @@ def quantize(
     percdamp: float = 0.01,
     act_order: bool = False,
 ):
+    if revision is None:
+        revision = "main"
     download_weights(
         model_id=model_id,
         revision=revision,
@@ -357,6 +319,7 @@ def quantize(
         bits=4,
         groupsize=128,
         output_dir=output_dir,
+        revision=revision,
         trust_remote_code=trust_remote_code,
         upload_to_model_id=upload_to_model_id,
         percdamp=percdamp,
diff --git a/server/text_generation_server/habana_quantization_env.py b/server/text_generation_server/habana_quantization_env.py
deleted file mode 100644
index 3c06fd098..000000000
--- a/server/text_generation_server/habana_quantization_env.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
-import os
-
-quant_config = os.getenv("QUANT_CONFIG", "")
-is_quantization_enabled = quant_config != ""
-
-if is_quantization_enabled:
-    os.environ.setdefault("ENABLE_EXPERIMENTAL_FLAGS", "true")
-    os.environ.setdefault("USE_DEFAULT_QUANT_PARAM", "true")
-    os.environ.setdefault("UPDATE_GRAPH_OUTPUT_MME", "false")
-    os.environ.setdefault("ENABLE_CALC_DYNAMIC_RANGE", "false")
-    os.environ.setdefault(
-        "UPDATE_MME_OUTPUT_PRECISION_FILTER", "v_proj,matmul_av")
-    os.environ.setdefault("EXPERIMENTAL_WEIGHT_SHARING", "FALSE")
-
-
-def prepare_model_for_quantization(model):
-    if is_quantization_enabled:
-        if os.getenv("USE_INC", "1") != "0":
-            from neural_compressor.torch.quantization import FP8Config, convert
-            config = FP8Config.from_json_file(quant_config)
-            model = convert(model, config)
-        else:
-            import habana_quantization_toolkit
-            habana_quantization_toolkit.prep_model(model)
-        return model
diff --git a/server/text_generation_server/interceptor.py b/server/text_generation_server/interceptor.py
index 05339282b..57df17257 100644
--- a/server/text_generation_server/interceptor.py
+++ b/server/text_generation_server/interceptor.py
@@ -1,5 +1,3 @@
-# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
 import torch
 import grpc
 
@@ -8,8 +6,6 @@ from grpc_status import rpc_status
 from grpc_interceptor.server import AsyncServerInterceptor
 from loguru import logger
 from typing import Callable, Any
-import traceback
-import os
 
 
 class ExceptionInterceptor(AsyncServerInterceptor):
@@ -24,7 +20,6 @@ class ExceptionInterceptor(AsyncServerInterceptor):
             response = method(request_or_iterator, context)
             return await response
         except Exception as err:
-            trace = " " + traceback.format_exc() if os.environ.get('DUMP_STACK') else ''
             method_name = method_name.split("/")[-1]
             logger.exception(f"Method {method_name} encountered an error.")
 
@@ -35,10 +30,8 @@ class ExceptionInterceptor(AsyncServerInterceptor):
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
 
-            from .utils.debug import dbg_trace
-            dbg_trace('EXCEPTION', traceback.format_exc())
             await context.abort_with_status(
                 rpc_status.to_status(
-                    status_pb2.Status(code=code_pb2.INTERNAL, message=str(err) + trace)
+                    status_pb2.Status(code=code_pb2.INTERNAL, message=str(err))
                 )
             )
diff --git a/server/text_generation_server/layers/gptq/exllamav2.py b/server/text_generation_server/layers/gptq/exllamav2.py
index 80836a959..321ced97f 100644
--- a/server/text_generation_server/layers/gptq/exllamav2.py
+++ b/server/text_generation_server/layers/gptq/exllamav2.py
@@ -119,6 +119,8 @@ def ext_make_q_matrix(w: dict, temp_dq, key: str = None):
                 none_tensor,
                 temp_dq,
             )
+    else:
+        RuntimeError("Cannot create handle")
 
 
 DEVICE = None
diff --git a/server/text_generation_server/layers/gptq/exllamav2.py.rej b/server/text_generation_server/layers/gptq/exllamav2.py.rej
deleted file mode 100644
index cde7b73a8..000000000
--- a/server/text_generation_server/layers/gptq/exllamav2.py.rej
+++ /dev/null
@@ -1,10 +0,0 @@
-diff a/server/text_generation_server/layers/gptq/exllamav2.py b/server/text_generation_server/layers/gptq/exllamav2.py	(rejected hunks)
-@@ -119,6 +119,8 @@ def ext_make_q_matrix(w: dict, temp_dq, key: str = None):
-                 none_tensor,
-                 temp_dq,
-             )
-+    else:
-+        RuntimeError("Cannot create handle")
- 
- 
- DEVICE = None
diff --git a/server/text_generation_server/layers/gptq/quant_linear.py b/server/text_generation_server/layers/gptq/quant_linear.py
index b52ceb0f7..f60758b61 100644
--- a/server/text_generation_server/layers/gptq/quant_linear.py
+++ b/server/text_generation_server/layers/gptq/quant_linear.py
@@ -1,356 +1,356 @@
-import math
-import numpy as np
-import torch
-import torch.nn as nn
-from torch.cuda.amp import custom_fwd
-
-import triton
-import triton.language as tl
-from . import custom_autotune
-
-
-# code based https://github.com/fpgaminer/GPTQ-triton
-@custom_autotune.autotune(
-    configs=[
-        triton.Config(
-            {
-                "BLOCK_SIZE_M": 64,
-                "BLOCK_SIZE_N": 256,
-                "BLOCK_SIZE_K": 32,
-                "GROUP_SIZE_M": 8,
-            },
-            num_stages=4,
-            num_warps=4,
-        ),
-        triton.Config(
-            {
-                "BLOCK_SIZE_M": 128,
-                "BLOCK_SIZE_N": 128,
-                "BLOCK_SIZE_K": 32,
-                "GROUP_SIZE_M": 8,
-            },
-            num_stages=4,
-            num_warps=4,
-        ),
-        triton.Config(
-            {
-                "BLOCK_SIZE_M": 64,
-                "BLOCK_SIZE_N": 128,
-                "BLOCK_SIZE_K": 32,
-                "GROUP_SIZE_M": 8,
-            },
-            num_stages=4,
-            num_warps=4,
-        ),
-        triton.Config(
-            {
-                "BLOCK_SIZE_M": 128,
-                "BLOCK_SIZE_N": 32,
-                "BLOCK_SIZE_K": 32,
-                "GROUP_SIZE_M": 8,
-            },
-            num_stages=4,
-            num_warps=4,
-        ),
-        triton.Config(
-            {
-                "BLOCK_SIZE_M": 64,
-                "BLOCK_SIZE_N": 64,
-                "BLOCK_SIZE_K": 32,
-                "GROUP_SIZE_M": 8,
-            },
-            num_stages=4,
-            num_warps=4,
-        ),
-        triton.Config(
-            {
-                "BLOCK_SIZE_M": 64,
-                "BLOCK_SIZE_N": 128,
-                "BLOCK_SIZE_K": 32,
-                "GROUP_SIZE_M": 8,
-            },
-            num_stages=2,
-            num_warps=8,
-        ),
-        triton.Config(
-            {
-                "BLOCK_SIZE_M": 64,
-                "BLOCK_SIZE_N": 64,
-                "BLOCK_SIZE_K": 64,
-                "GROUP_SIZE_M": 8,
-            },
-            num_stages=3,
-            num_warps=8,
-        ),
-        triton.Config(
-            {
-                "BLOCK_SIZE_M": 32,
-                "BLOCK_SIZE_N": 32,
-                "BLOCK_SIZE_K": 128,
-                "GROUP_SIZE_M": 8,
-            },
-            num_stages=2,
-            num_warps=4,
-        ),
-    ],
-    key=["M", "N", "K"],
-    nearest_power_of_two=True,
-    prune_configs_by={
-        "early_config_prune": custom_autotune.matmul248_kernel_config_pruner,
-        "perf_model": None,
-        "top_k": None,
-    },
-)
-@triton.jit
-def matmul_248_kernel(
-    a_ptr,
-    b_ptr,
-    c_ptr,
-    scales_ptr,
-    zeros_ptr,
-    g_ptr,
-    M,
-    N,
-    K,
-    bits,
-    maxq,
-    stride_am,
-    stride_ak,
-    stride_bk,
-    stride_bn,
-    stride_cm,
-    stride_cn,
-    stride_scales,
-    stride_zeros,
-    BLOCK_SIZE_M: tl.constexpr,
-    BLOCK_SIZE_N: tl.constexpr,
-    BLOCK_SIZE_K: tl.constexpr,
-    GROUP_SIZE_M: tl.constexpr,
-):
-    """
-    Compute the matrix multiplication C = A x B.
-    A is of shape (M, K) float16
-    B is of shape (K//8, N) int32
-    C is of shape (M, N) float16
-    scales is of shape (G, N) float16
-    zeros is of shape (G, N) float16
-    g_ptr is of shape (K) int32
-    """
-    infearure_per_bits = 32 // bits
-
-    pid = tl.program_id(axis=0)
-    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
-    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
-    num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
-    num_pid_in_group = GROUP_SIZE_M * num_pid_n
-    group_id = pid // num_pid_in_group
-    first_pid_m = group_id * GROUP_SIZE_M
-    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
-    pid_m = first_pid_m + (pid % group_size_m)
-    pid_n = (pid % num_pid_in_group) // group_size_m
-
-    offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
-    offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
-    offs_k = tl.arange(0, BLOCK_SIZE_K)
-    a_ptrs = a_ptr + (
-        offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
-    )  # (BLOCK_SIZE_M, BLOCK_SIZE_K)
-    a_mask = offs_am[:, None] < M
-    # b_ptrs is set up such that it repeats elements along the K axis 8 times
-    b_ptrs = b_ptr + (
-        (offs_k[:, None] // infearure_per_bits) * stride_bk
-        + offs_bn[None, :] * stride_bn
-    )  # (BLOCK_SIZE_K, BLOCK_SIZE_N)
-    g_ptrs = g_ptr + offs_k
-    # shifter is used to extract the N bits of each element in the 32-bit word from B
-    scales_ptrs = scales_ptr + offs_bn[None, :]
-    zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits)
-
-    shifter = (offs_k % infearure_per_bits) * bits
-    zeros_shifter = (offs_bn % infearure_per_bits) * bits
-    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
-
-    for k in range(0, num_pid_k):
-        g_idx = tl.load(g_ptrs)
-
-        # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
-        scales = tl.load(
-            scales_ptrs + g_idx[:, None] * stride_scales
-        )  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
-        zeros = tl.load(
-            zeros_ptrs + g_idx[:, None] * stride_zeros
-        )  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
-
-        zeros = (zeros >> zeros_shifter[None, :]) & maxq
-        zeros = (zeros + 1) & maxq  # eventually avoid overflow
-
-        a = tl.load(a_ptrs, mask=a_mask, other=0.0)  # (BLOCK_SIZE_M, BLOCK_SIZE_K)
-        b = tl.load(b_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
-
-        # Now we need to unpack b (which is N-bit values) into 32-bit values
-        b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
-        b = (b - zeros) * scales  # Scale and shift
-
-        accumulator += tl.dot(a, b)
-        a_ptrs += BLOCK_SIZE_K
-        b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
-        g_ptrs += BLOCK_SIZE_K
-
-    c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
-    c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
-    tl.store(c_ptrs, accumulator, mask=c_mask)
-
-
-def matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq):
-    with torch.cuda.device(input.device):
-        output = torch.empty(
-            (input.shape[0], qweight.shape[1]), device=input.device, dtype=torch.float16
-        )
-        grid = lambda META: (
-            triton.cdiv(input.shape[0], META["BLOCK_SIZE_M"])
-            * triton.cdiv(qweight.shape[1], META["BLOCK_SIZE_N"]),
-        )
-        matmul_248_kernel[grid](
-            input,
-            qweight,
-            output,
-            scales,
-            qzeros,
-            g_idx,
-            input.shape[0],
-            qweight.shape[1],
-            input.shape[1],
-            bits,
-            maxq,
-            input.stride(0),
-            input.stride(1),
-            qweight.stride(0),
-            qweight.stride(1),
-            output.stride(0),
-            output.stride(1),
-            scales.stride(0),
-            qzeros.stride(0),
-        )
-        return output
-
-
-class QuantLinearFunction(torch.autograd.Function):
-    @staticmethod
-    @custom_fwd(cast_inputs=torch.float16)
-    def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
-        output = matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq)
-        return output
-
-
-class QuantLinear(nn.Module):
-    def __init__(self, qweight, qzeros, scales, g_idx, bias, bits, groupsize):
-        super().__init__()
-        self.register_buffer("qweight", qweight)
-        self.register_buffer("qzeros", qzeros)
-        self.register_buffer("scales", scales)
-        self.register_buffer("g_idx", g_idx)
-        if bias is not None:
-            self.register_buffer("bias", bias)
-        else:
-            self.bias = None
-        if bits not in [2, 4, 8]:
-            raise NotImplementedError("Only 2,4,8 bits are supported.")
-        self.bits = bits
-        self.maxq = 2**self.bits - 1
-        self.groupsize = groupsize
-
-        self.outfeatures = qweight.shape[1]
-        self.infeatures = qweight.shape[0] * 32 // bits
-
-    @classmethod
-    def new(cls, bits, groupsize, infeatures, outfeatures, bias):
-        if bits not in [2, 4, 8]:
-            raise NotImplementedError("Only 2,4,8 bits are supported.")
-
-        qweight = torch.zeros((infeatures // 32 * bits, outfeatures), dtype=torch.int32)
-        qzeros = torch.zeros(
-            (math.ceil(infeatures / groupsize), outfeatures // 32 * bits),
-            dtype=torch.int32,
-        )
-        scales = torch.zeros(
-            (math.ceil(infeatures / groupsize), outfeatures), dtype=torch.float16
-        )
-        g_idx = torch.tensor(
-            [i // groupsize for i in range(infeatures)], dtype=torch.int32
-        )
-        if bias:
-            bias = torch.zeros((outfeatures), dtype=torch.float16)
-        else:
-            bias = None
-        return cls(qweight, qzeros, scales, g_idx, bias, bits, groupsize)
-
-    def pack(self, linear, scales, zeros, g_idx=None):
-        self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx
-
-        scales = scales.t().contiguous()
-        zeros = zeros.t().contiguous()
-        scale_zeros = zeros * scales
-        self.scales = scales.clone().half()
-        if linear.bias is not None:
-            self.bias = linear.bias.clone().half()
-
-        intweight = []
-        for idx in range(self.infeatures):
-            intweight.append(
-                torch.round(
-                    (linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]])
-                    / self.scales[self.g_idx[idx]]
-                ).to(torch.int)[:, None]
-            )
-        intweight = torch.cat(intweight, dim=1)
-        intweight = intweight.t().contiguous()
-        intweight = intweight.numpy().astype(np.uint32)
-        qweight = np.zeros(
-            (intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32
-        )
-        i = 0
-        row = 0
-        while row < qweight.shape[0]:
-            if self.bits in [2, 4, 8]:
-                for j in range(i, i + (32 // self.bits)):
-                    qweight[row] |= intweight[j] << (self.bits * (j - i))
-                i += 32 // self.bits
-                row += 1
-            else:
-                raise NotImplementedError("Only 2,4,8 bits are supported.")
-
-        qweight = qweight.astype(np.int32)
-        self.qweight = torch.from_numpy(qweight)
-
-        zeros -= 1
-        zeros = zeros.numpy().astype(np.uint32)
-        qzeros = np.zeros(
-            (zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32
-        )
-        i = 0
-        col = 0
-        while col < qzeros.shape[1]:
-            if self.bits in [2, 4, 8]:
-                for j in range(i, i + (32 // self.bits)):
-                    qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
-                i += 32 // self.bits
-                col += 1
-            else:
-                raise NotImplementedError("Only 2,4,8 bits are supported.")
-
-        qzeros = qzeros.astype(np.int32)
-        self.qzeros = torch.from_numpy(qzeros)
-
-    def forward(self, x):
-        out_shape = x.shape[:-1] + (self.outfeatures,)
-        out = QuantLinearFunction.apply(
-            x.reshape(-1, x.shape[-1]),
-            self.qweight,
-            self.scales,
-            self.qzeros,
-            self.g_idx,
-            self.bits,
-            self.maxq,
-        )
-        out = out + self.bias if self.bias is not None else out
-        return out.reshape(out_shape)
+import math
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.cuda.amp import custom_fwd
+
+import triton
+import triton.language as tl
+from . import custom_autotune
+
+
+# code based https://github.com/fpgaminer/GPTQ-triton
+@custom_autotune.autotune(
+    configs=[
+        triton.Config(
+            {
+                "BLOCK_SIZE_M": 64,
+                "BLOCK_SIZE_N": 256,
+                "BLOCK_SIZE_K": 32,
+                "GROUP_SIZE_M": 8,
+            },
+            num_stages=4,
+            num_warps=4,
+        ),
+        triton.Config(
+            {
+                "BLOCK_SIZE_M": 128,
+                "BLOCK_SIZE_N": 128,
+                "BLOCK_SIZE_K": 32,
+                "GROUP_SIZE_M": 8,
+            },
+            num_stages=4,
+            num_warps=4,
+        ),
+        triton.Config(
+            {
+                "BLOCK_SIZE_M": 64,
+                "BLOCK_SIZE_N": 128,
+                "BLOCK_SIZE_K": 32,
+                "GROUP_SIZE_M": 8,
+            },
+            num_stages=4,
+            num_warps=4,
+        ),
+        triton.Config(
+            {
+                "BLOCK_SIZE_M": 128,
+                "BLOCK_SIZE_N": 32,
+                "BLOCK_SIZE_K": 32,
+                "GROUP_SIZE_M": 8,
+            },
+            num_stages=4,
+            num_warps=4,
+        ),
+        triton.Config(
+            {
+                "BLOCK_SIZE_M": 64,
+                "BLOCK_SIZE_N": 64,
+                "BLOCK_SIZE_K": 32,
+                "GROUP_SIZE_M": 8,
+            },
+            num_stages=4,
+            num_warps=4,
+        ),
+        triton.Config(
+            {
+                "BLOCK_SIZE_M": 64,
+                "BLOCK_SIZE_N": 128,
+                "BLOCK_SIZE_K": 32,
+                "GROUP_SIZE_M": 8,
+            },
+            num_stages=2,
+            num_warps=8,
+        ),
+        triton.Config(
+            {
+                "BLOCK_SIZE_M": 64,
+                "BLOCK_SIZE_N": 64,
+                "BLOCK_SIZE_K": 64,
+                "GROUP_SIZE_M": 8,
+            },
+            num_stages=3,
+            num_warps=8,
+        ),
+        triton.Config(
+            {
+                "BLOCK_SIZE_M": 32,
+                "BLOCK_SIZE_N": 32,
+                "BLOCK_SIZE_K": 128,
+                "GROUP_SIZE_M": 8,
+            },
+            num_stages=2,
+            num_warps=4,
+        ),
+    ],
+    key=["M", "N", "K"],
+    nearest_power_of_two=True,
+    prune_configs_by={
+        "early_config_prune": custom_autotune.matmul248_kernel_config_pruner,
+        "perf_model": None,
+        "top_k": None,
+    },
+)
+@triton.jit
+def matmul_248_kernel(
+    a_ptr,
+    b_ptr,
+    c_ptr,
+    scales_ptr,
+    zeros_ptr,
+    g_ptr,
+    M,
+    N,
+    K,
+    bits,
+    maxq,
+    stride_am,
+    stride_ak,
+    stride_bk,
+    stride_bn,
+    stride_cm,
+    stride_cn,
+    stride_scales,
+    stride_zeros,
+    BLOCK_SIZE_M: tl.constexpr,
+    BLOCK_SIZE_N: tl.constexpr,
+    BLOCK_SIZE_K: tl.constexpr,
+    GROUP_SIZE_M: tl.constexpr,
+):
+    """
+    Compute the matrix multiplication C = A x B.
+    A is of shape (M, K) float16
+    B is of shape (K//8, N) int32
+    C is of shape (M, N) float16
+    scales is of shape (G, N) float16
+    zeros is of shape (G, N) float16
+    g_ptr is of shape (K) int32
+    """
+    infearure_per_bits = 32 // bits
+
+    pid = tl.program_id(axis=0)
+    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
+    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
+    num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
+    num_pid_in_group = GROUP_SIZE_M * num_pid_n
+    group_id = pid // num_pid_in_group
+    first_pid_m = group_id * GROUP_SIZE_M
+    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
+    pid_m = first_pid_m + (pid % group_size_m)
+    pid_n = (pid % num_pid_in_group) // group_size_m
+
+    offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    offs_k = tl.arange(0, BLOCK_SIZE_K)
+    a_ptrs = a_ptr + (
+        offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
+    )  # (BLOCK_SIZE_M, BLOCK_SIZE_K)
+    a_mask = offs_am[:, None] < M
+    # b_ptrs is set up such that it repeats elements along the K axis 8 times
+    b_ptrs = b_ptr + (
+        (offs_k[:, None] // infearure_per_bits) * stride_bk
+        + offs_bn[None, :] * stride_bn
+    )  # (BLOCK_SIZE_K, BLOCK_SIZE_N)
+    g_ptrs = g_ptr + offs_k
+    # shifter is used to extract the N bits of each element in the 32-bit word from B
+    scales_ptrs = scales_ptr + offs_bn[None, :]
+    zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits)
+
+    shifter = (offs_k % infearure_per_bits) * bits
+    zeros_shifter = (offs_bn % infearure_per_bits) * bits
+    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+
+    for k in range(0, num_pid_k):
+        g_idx = tl.load(g_ptrs)
+
+        # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
+        scales = tl.load(
+            scales_ptrs + g_idx[:, None] * stride_scales
+        )  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
+        zeros = tl.load(
+            zeros_ptrs + g_idx[:, None] * stride_zeros
+        )  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
+
+        zeros = (zeros >> zeros_shifter[None, :]) & maxq
+        zeros = (zeros + 1) & maxq  # eventually avoid overflow
+
+        a = tl.load(a_ptrs, mask=a_mask, other=0.0)  # (BLOCK_SIZE_M, BLOCK_SIZE_K)
+        b = tl.load(b_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
+
+        # Now we need to unpack b (which is N-bit values) into 32-bit values
+        b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
+        b = (b - zeros) * scales  # Scale and shift
+
+        accumulator += tl.dot(a, b)
+        a_ptrs += BLOCK_SIZE_K
+        b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
+        g_ptrs += BLOCK_SIZE_K
+
+    c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
+    c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
+    tl.store(c_ptrs, accumulator, mask=c_mask)
+
+
+def matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq):
+    with torch.cuda.device(input.device):
+        output = torch.empty(
+            (input.shape[0], qweight.shape[1]), device=input.device, dtype=torch.float16
+        )
+        grid = lambda META: (
+            triton.cdiv(input.shape[0], META["BLOCK_SIZE_M"])
+            * triton.cdiv(qweight.shape[1], META["BLOCK_SIZE_N"]),
+        )
+        matmul_248_kernel[grid](
+            input,
+            qweight,
+            output,
+            scales,
+            qzeros,
+            g_idx,
+            input.shape[0],
+            qweight.shape[1],
+            input.shape[1],
+            bits,
+            maxq,
+            input.stride(0),
+            input.stride(1),
+            qweight.stride(0),
+            qweight.stride(1),
+            output.stride(0),
+            output.stride(1),
+            scales.stride(0),
+            qzeros.stride(0),
+        )
+        return output
+
+
+class QuantLinearFunction(torch.autograd.Function):
+    @staticmethod
+    @custom_fwd(cast_inputs=torch.float16)
+    def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
+        output = matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq)
+        return output
+
+
+class QuantLinear(nn.Module):
+    def __init__(self, qweight, qzeros, scales, g_idx, bias, bits, groupsize):
+        super().__init__()
+        self.register_buffer("qweight", qweight)
+        self.register_buffer("qzeros", qzeros)
+        self.register_buffer("scales", scales)
+        self.register_buffer("g_idx", g_idx)
+        if bias is not None:
+            self.register_buffer("bias", bias)
+        else:
+            self.bias = None
+        if bits not in [2, 4, 8]:
+            raise NotImplementedError("Only 2,4,8 bits are supported.")
+        self.bits = bits
+        self.maxq = 2**self.bits - 1
+        self.groupsize = groupsize
+
+        self.outfeatures = qweight.shape[1]
+        self.infeatures = qweight.shape[0] * 32 // bits
+
+    @classmethod
+    def new(cls, bits, groupsize, infeatures, outfeatures, bias):
+        if bits not in [2, 4, 8]:
+            raise NotImplementedError("Only 2,4,8 bits are supported.")
+
+        qweight = torch.zeros((infeatures // 32 * bits, outfeatures), dtype=torch.int32)
+        qzeros = torch.zeros(
+            (math.ceil(infeatures / groupsize), outfeatures // 32 * bits),
+            dtype=torch.int32,
+        )
+        scales = torch.zeros(
+            (math.ceil(infeatures / groupsize), outfeatures), dtype=torch.float16
+        )
+        g_idx = torch.tensor(
+            [i // groupsize for i in range(infeatures)], dtype=torch.int32
+        )
+        if bias:
+            bias = torch.zeros((outfeatures), dtype=torch.float16)
+        else:
+            bias = None
+        return cls(qweight, qzeros, scales, g_idx, bias, bits, groupsize)
+
+    def pack(self, linear, scales, zeros, g_idx=None):
+        self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx
+
+        scales = scales.t().contiguous()
+        zeros = zeros.t().contiguous()
+        scale_zeros = zeros * scales
+        self.scales = scales.clone().half()
+        if linear.bias is not None:
+            self.bias = linear.bias.clone().half()
+
+        intweight = []
+        for idx in range(self.infeatures):
+            intweight.append(
+                torch.round(
+                    (linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]])
+                    / self.scales[self.g_idx[idx]]
+                ).to(torch.int)[:, None]
+            )
+        intweight = torch.cat(intweight, dim=1)
+        intweight = intweight.t().contiguous()
+        intweight = intweight.numpy().astype(np.uint32)
+        qweight = np.zeros(
+            (intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32
+        )
+        i = 0
+        row = 0
+        while row < qweight.shape[0]:
+            if self.bits in [2, 4, 8]:
+                for j in range(i, i + (32 // self.bits)):
+                    qweight[row] |= intweight[j] << (self.bits * (j - i))
+                i += 32 // self.bits
+                row += 1
+            else:
+                raise NotImplementedError("Only 2,4,8 bits are supported.")
+
+        qweight = qweight.astype(np.int32)
+        self.qweight = torch.from_numpy(qweight)
+
+        zeros -= 1
+        zeros = zeros.numpy().astype(np.uint32)
+        qzeros = np.zeros(
+            (zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32
+        )
+        i = 0
+        col = 0
+        while col < qzeros.shape[1]:
+            if self.bits in [2, 4, 8]:
+                for j in range(i, i + (32 // self.bits)):
+                    qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
+                i += 32 // self.bits
+                col += 1
+            else:
+                raise NotImplementedError("Only 2,4,8 bits are supported.")
+
+        qzeros = qzeros.astype(np.int32)
+        self.qzeros = torch.from_numpy(qzeros)
+
+    def forward(self, x):
+        out_shape = x.shape[:-1] + (self.outfeatures,)
+        out = QuantLinearFunction.apply(
+            x.reshape(-1, x.shape[-1]),
+            self.qweight,
+            self.scales,
+            self.qzeros,
+            self.g_idx,
+            self.bits,
+            self.maxq,
+        )
+        out = out + self.bias if self.bias is not None else out
+        return out.reshape(out_shape)
diff --git a/server/text_generation_server/models/__init__.py b/server/text_generation_server/models/__init__.py
index 30bd8f6cb..d4a325a90 100644
--- a/server/text_generation_server/models/__init__.py
+++ b/server/text_generation_server/models/__init__.py
@@ -1,4 +1,5 @@
 import torch
+import enum
 import os
 
 from loguru import logger
@@ -8,36 +9,269 @@ from huggingface_hub import hf_hub_download, HfApi
 from typing import Optional
 from pathlib import Path
 
-# Needed to properly setup habana_frameworks
-import text_generation_server.habana_quantization_env as hq_env
-
 from text_generation_server.utils.speculate import get_speculate, set_speculate
 from text_generation_server.models.model import Model
 from text_generation_server.models.causal_lm import CausalLM
-from text_generation_server.models.bloom import BLOOM
-from text_generation_server.models.starcoder import StarCoder
-from text_generation_server.models.vlm_causal_lm import VlmCausalLM
-from text_generation_server.models.custom_modeling.llava_next import (
-    LlavaNextForConditionalGeneration,
-)
+from text_generation_server.models.flash_causal_lm import FlashCausalLM
+from text_generation_server.models.bloom import BLOOMSharded
+from text_generation_server.models.mpt import MPTSharded
+from text_generation_server.models.seq2seq_lm import Seq2SeqLM
+from text_generation_server.models.rw import RW
+from text_generation_server.models.opt import OPTSharded
+from text_generation_server.models.galactica import GalacticaSharded
+from text_generation_server.models.santacoder import SantaCoder
+from text_generation_server.models.t5 import T5Sharded
+from text_generation_server.models.gpt_neox import GPTNeoxSharded
+from text_generation_server.models.phi import Phi
 
+# The flag below controls whether to allow TF32 on matmul. This flag defaults to False
+# in PyTorch 1.12 and later.
+torch.backends.cuda.matmul.allow_tf32 = True
 
-
-from optimum.habana.transformers.modeling_utils import adapt_transformers_to_gaudi
-
+# The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
+torch.backends.cudnn.allow_tf32 = True
 
 # Disable gradients
 torch.set_grad_enabled(False)
 
+__all__ = [
+    "Model",
+    "BLOOMSharded",
+    "CausalLM",
+    "GalacticaSharded",
+    "Seq2SeqLM",
+    "SantaCoder",
+    "OPTSharded",
+    "T5Sharded",
+    "get_model",
+]
+
+FLASH_ATT_ERROR_MESSAGE = "{} requires Flash Attention enabled models."
+
+FLASH_ATTENTION = True
+
+try:
+    from text_generation_server.models.flash_rw import FlashRWSharded
+    from text_generation_server.models.flash_gpt2 import FlashGPT2
+    from text_generation_server.models.flash_neox import FlashNeoXSharded
+    from text_generation_server.models.flash_llama import (
+        FlashLlama,
+    )
+    from text_generation_server.models.flash_qwen2 import (
+        FlashQwen2,
+    )
+    from text_generation_server.models.flash_cohere import (
+        FlashCohere,
+    )
+    from text_generation_server.models.flash_gemma import (
+        FlashGemma,
+    )
+    from text_generation_server.models.pali_gemma import (
+        PaliGemma,
+    )
+    from text_generation_server.models.flash_santacoder import (
+        FlashSantacoderSharded,
+    )
+    from text_generation_server.models.idefics import IDEFICSSharded
+    from text_generation_server.models.llava_next import LlavaNext
+    from text_generation_server.models.idefics2 import Idefics2
+    from text_generation_server.models.flash_mistral import FlashMistral
+    from text_generation_server.models.flash_mixtral import FlashMixtral
+    from text_generation_server.models.flash_phi import FlashPhi
+    from text_generation_server.models.flash_starcoder2 import FlashStarcoder2
+    from text_generation_server.models.flash_dbrx import FlashDbrx
+    from text_generation_server.utils.flash_attn import (
+        HAS_FLASH_ATTN_V2_CUDA,
+        HAS_FLASH_ATTN_V2_ROCM,
+    )
+except ImportError as e:
+    logger.warning(f"Could not import Flash Attention enabled models: {e}")
+    FLASH_ATTENTION = False
+    HAS_FLASH_ATTN_V2_CUDA = False
+    HAS_FLASH_ATTN_V2_ROCM = False
+
+if FLASH_ATTENTION:
+    __all__.append(FlashGPT2)
+    __all__.append(FlashNeoXSharded)
+    __all__.append(FlashRWSharded)
+    __all__.append(FlashSantacoderSharded)
+    __all__.append(FlashLlama)
+    __all__.append(IDEFICSSharded)
+    __all__.append(FlashMistral)
+    __all__.append(FlashMixtral)
+    __all__.append(FlashDbrx)
+    __all__.append(FlashPhi)
+    __all__.append(FlashQwen2)
+    __all__.append(FlashStarcoder2)
+    __all__.append(FlashGemma)
+    __all__.append(FlashCohere)
+
+MAMBA_AVAILABLE = True
+try:
+    from text_generation_server.models.mamba import Mamba
+except ImportError as e:
+    logger.warning(f"Could not import Mamba: {e}")
+    MAMBA_AVAILABLE = False
+
+if MAMBA_AVAILABLE:
+    __all__.append(Mamba)
+
+
+class ModelType(enum.Enum):
+    IDEFICS2 = {
+        "type": "idefics2",
+        "name": "Idefics 2",
+        "url": "https://huggingface.co/HuggingFaceM4/idefics2-8b",
+        "multimodal": True,
+    }
+    LLAVA_NEXT = {
+        "type": "llava_next",
+        "name": "Llava Next (1.6)",
+        "url": "https://huggingface.co/llava-hf/llava-v1.6-vicuna-13b-hf",
+        "multimodal": True,
+    }
+    LLAMA = {
+        "type": "llama",
+        "name": "Llama",
+        "url": "https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct",
+    }
+    PHI3 = {
+        "type": "phi3",
+        "name": "Phi 3",
+        "url": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct",
+    }
+    GEMMA = {
+        "type": "gemma",
+        "name": "Gemma",
+        "url": "https://huggingface.co/google/gemma-7b",
+    }
+    COHERE = {
+        "type": "cohere",
+        "name": "Cohere",
+        "url": "https://huggingface.co/CohereForAI/c4ai-command-r-plus",
+    }
+    DBRX = {
+        "type": "dbrx",
+        "name": "Dbrx",
+        "url": "https://huggingface.co/databricks/dbrx-instruct",
+    }
+    MAMBA = {
+        "type": "ssm",
+        "name": "Mamba",
+        "url": "https://huggingface.co/state-spaces/mamba-2.8b-slimpj",
+    }
+    MISTRAL = {
+        "type": "mistral",
+        "name": "Mistral",
+        "url": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2",
+    }
+    MIXTRAL = {
+        "type": "mixtral",
+        "name": "Mixtral",
+        "url": "https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1",
+    }
+    GPT_BIGCODE = {
+        "type": "gpt_bigcode",
+        "name": "Gpt Bigcode",
+        "url": "https://huggingface.co/bigcode/gpt_bigcode-santacoder",
+    }
+    PHI = {
+        "type": "phi",
+        "name": "Phi",
+        "url": "https://huggingface.co/microsoft/phi-1_5",
+    }
+    BAICHUAN = {
+        "type": "baichuan",
+        "name": "Baichuan",
+        "url": "https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat",
+    }
+    FALCON = {
+        "type": "falcon",
+        "name": "Falcon",
+        "url": "https://huggingface.co/tiiuae/falcon-7b-instruct",
+    }
+    STARCODER2 = {
+        "type": "starcoder2",
+        "name": "StarCoder 2",
+        "url": "https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1",
+    }
+    QWEN2 = {
+        "type": "qwen2",
+        "name": "Qwen 2",
+        "url": "https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1",
+    }
+    OPT = {
+        "type": "opt",
+        "name": "Opt",
+        "url": "https://huggingface.co/facebook/opt-6.7b",
+    }
+    T5 = {
+        "type": "t5",
+        "name": "T5",
+        "url": "https://huggingface.co/google/flan-t5-xxl",
+    }
+    GALACTICA = {
+        "type": "galactica",
+        "name": "Galactica",
+        "url": "https://huggingface.co/facebook/galactica-120b",
+    }
+    SANTACODER = {
+        "type": "santacoder",
+        "name": "SantaCoder",
+        "url": "https://huggingface.co/bigcode/santacoder",
+    }
+    BLOOM = {
+        "type": "bloom",
+        "name": "Bloom",
+        "url": "https://huggingface.co/bigscience/bloom-560m",
+    }
+    MPT = {
+        "type": "mpt",
+        "name": "Mpt",
+        "url": "https://huggingface.co/mosaicml/mpt-7b-instruct",
+    }
+    GPT2 = {
+        "type": "gpt2",
+        "name": "Gpt2",
+        "url": "https://huggingface.co/openai-community/gpt2",
+    }
+    GPT_NEOX = {
+        "type": "gpt_neox",
+        "name": "Gpt Neox",
+        "url": "https://huggingface.co/EleutherAI/gpt-neox-20b",
+    }
+    IDEFICS = {
+        "type": "idefics",
+        "name": "Idefics",
+        "url": "https://huggingface.co/HuggingFaceM4/idefics-9b",
+        "multimodal": True,
+    }
+
+
+__GLOBALS = locals()
+for data in ModelType:
+    __GLOBALS[data.name] = data.value["type"]
+
 
 def get_model(
     model_id: str,
     revision: Optional[str],
+    sharded: bool,
+    quantize: Optional[str],
     speculate: Optional[int],
-    dtype: Optional[torch.dtype],
+    dtype: Optional[str],
     trust_remote_code: bool,
 ) -> Model:
-    adapt_transformers_to_gaudi()
+    if dtype is None:
+        # Keep it as default for now and let
+        # every model resolve their own default dtype.
+        dtype = None
+    elif dtype == "float16":
+        dtype = torch.float16
+    elif dtype == "bfloat16":
+        dtype = torch.bfloat16
+    else:
+        raise RuntimeError(f"Unknown dtype {dtype}")
 
     if speculate is not None:
         set_speculate(speculate)
@@ -152,38 +386,535 @@ def get_model(
     if speculate > 0:
         logger.info(f"Using speculation {method} with {speculate} input ids.")
 
-    model_type = config_dict["model_type"]
+    if model_type is None:
+        # TODO: fix how we determine model type for Mamba
+        if "ssm_cfg" in config_dict:
+            # *only happens in Mamba case
+            model_type = "ssm"
+        else:
+            raise RuntimeError(
+                f"Could not determine model type for {model_id} revision {revision}"
+            )
+    quantization_config = config_dict.get("quantization_config", None)
+    if quantization_config is not None and quantize is None:
+        method = quantization_config.get("quant_method", None)
+        if method in {"gptq", "awq"}:
+            logger.info(f"Auto selecting quantization method {method}")
+            quantize = method
+        else:
+            logger.info(f"Unknown quantization method {method}")
 
-    if model_type == "gpt_bigcode":
-        return StarCoder(model_id, revision, dtype)
-
-    if model_type == "bloom":
-        return BLOOM(
+    if model_type == MAMBA:
+        return Mamba(
             model_id,
             revision,
+            quantize=quantize,
             speculator=speculator,
             dtype=dtype,
             trust_remote_code=trust_remote_code,
         )
 
-    if model_type == "llava_next":
-        return VlmCausalLM(
-            model_class=LlavaNextForConditionalGeneration,
-            model_id=model_id,
-            revision=revision,
-            quantize=None,
+    if model_id.startswith("facebook/galactica"):
+        return GalacticaSharded(
+            model_id,
+            revision,
+            quantize=quantize,
             speculator=speculator,
             dtype=dtype,
             trust_remote_code=trust_remote_code,
         )
 
+    if (
+        model_type == GPT_BIGCODE
+        or model_type == GPT2
+        and model_id.startswith("bigcode/")
+    ):
+        if FLASH_ATTENTION:
+            return FlashSantacoderSharded(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        elif sharded:
+            raise NotImplementedError(
+                FLASH_ATT_ERROR_MESSAGE.format("Sharded Santacoder")
+            )
+        else:
+            return SantaCoder(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+
+    if model_type == BLOOM:
+        return BLOOMSharded(
+            model_id,
+            revision,
+            quantize=quantize,
+            speculator=speculator,
+            dtype=dtype,
+            trust_remote_code=trust_remote_code,
+        )
+    elif model_type == MPT:
+        return MPTSharded(
+            model_id,
+            revision,
+            quantize=quantize,
+            speculator=speculator,
+            dtype=dtype,
+            trust_remote_code=trust_remote_code,
+        )
+    elif model_type == GPT2:
+        if FLASH_ATTENTION:
+            try:
+                return FlashGPT2(
+                    model_id,
+                    revision,
+                    quantize=quantize,
+                    speculator=speculator,
+                    dtype=dtype,
+                    trust_remote_code=trust_remote_code,
+                )
+            except RuntimeError as e:
+                # Lots of legacy models with various weight names.
+                logger.warning(f"Couldn't load flash gpt2 variant: {e}")
+                return CausalLM(
+                    model_id,
+                    revision,
+                    quantize=quantize,
+                    speculator=speculator,
+                    dtype=dtype,
+                    trust_remote_code=trust_remote_code,
+                )
+        elif sharded:
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded GPT-2"))
+        else:
+            return CausalLM(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+    elif model_type == GPT_NEOX:
+        if FLASH_ATTENTION:
+            return FlashNeoXSharded(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        elif sharded:
+            return GPTNeoxSharded(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        else:
+            return CausalLM(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+
+    elif model_type == PHI:
+        if FLASH_ATTENTION:
+            return FlashPhi(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        else:
+            return CausalLM(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+
+    elif model_type == "phi-msft":
+        if FLASH_ATTENTION:
+            raise NotImplementedError(
+                "Legacy phi-msft is not supported with Flash Attention"
+            )
+        else:
+            return Phi(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+
+    elif model_type == LLAMA or model_type == BAICHUAN or model_type == PHI3:
+        if FLASH_ATTENTION:
+            return FlashLlama(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        elif sharded:
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Llama"))
+        else:
+            return CausalLM(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+    if model_type == GEMMA:
+        if FLASH_ATTENTION:
+            return FlashGemma(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        elif sharded:
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Gemma"))
+        else:
+            return CausalLM(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+
+    if model_type == COHERE:
+        if FLASH_ATTENTION:
+            return FlashCohere(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        elif sharded:
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Cohere"))
+        else:
+            return CausalLM(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+
+    if model_type == DBRX:
+        if FLASH_ATTENTION:
+            return FlashDbrx(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        elif sharded:
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded DBRX"))
+        else:
+            return CausalLM(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+
+    if model_type in ["RefinedWeb", "RefinedWebModel", FALCON]:
+        if sharded:
+            if FLASH_ATTENTION:
+                if config_dict.get("alibi", False):
+                    raise NotImplementedError("sharded is not supported for this model")
+                return FlashRWSharded(
+                    model_id,
+                    revision,
+                    quantize=quantize,
+                    speculator=speculator,
+                    dtype=dtype,
+                    trust_remote_code=trust_remote_code,
+                )
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format(f"Sharded Falcon"))
+        else:
+            if FLASH_ATTENTION and not config_dict.get("alibi", False):
+                return FlashRWSharded(
+                    model_id,
+                    revision,
+                    quantize=quantize,
+                    speculator=speculator,
+                    dtype=dtype,
+                    trust_remote_code=trust_remote_code,
+                )
+            else:
+                return RW(
+                    model_id,
+                    revision,
+                    quantize=quantize,
+                    speculator=speculator,
+                    dtype=dtype,
+                    trust_remote_code=trust_remote_code,
+                )
+
+    if model_type == MISTRAL:
+        sliding_window = config_dict.get("sliding_window", -1)
+        if (
+            ((sliding_window is None or sliding_window == -1) and FLASH_ATTENTION)
+            or HAS_FLASH_ATTN_V2_CUDA
+            or HAS_FLASH_ATTN_V2_ROCM
+        ):
+            return FlashMistral(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        elif sharded:
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Mistral"))
+        else:
+            return CausalLM(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+
+    if model_type == MIXTRAL:
+        sliding_window = config_dict.get("sliding_window", -1)
+        if (
+            ((sliding_window is None or sliding_window == -1) and FLASH_ATTENTION)
+            or HAS_FLASH_ATTN_V2_CUDA
+            or HAS_FLASH_ATTN_V2_ROCM
+        ):
+            return FlashMixtral(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        elif sharded:
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Mixtral"))
+        else:
+            return CausalLM(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+
+    if model_type == STARCODER2:
+        sliding_window = config_dict.get("sliding_window", -1)
+        if (
+            ((sliding_window is None or sliding_window == -1) and FLASH_ATTENTION)
+            or HAS_FLASH_ATTN_V2_CUDA
+            or HAS_FLASH_ATTN_V2_ROCM
+        ):
+            return FlashStarcoder2(
+                model_id,
+                revision,
+                quantize=quantize,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        elif sharded:
+            raise NotImplementedError(
+                FLASH_ATT_ERROR_MESSAGE.format("Sharded Starcoder2")
+            )
+        else:
+            return CausalLM(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+
+    if model_type == QWEN2:
+        sliding_window = config_dict.get("sliding_window", -1)
+        if (
+            ((sliding_window is None or sliding_window == -1) and FLASH_ATTENTION)
+            or HAS_FLASH_ATTN_V2_CUDA
+            or HAS_FLASH_ATTN_V2_ROCM
+        ):
+            return FlashQwen2(
+                model_id,
+                revision,
+                quantize=quantize,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        elif sharded:
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Qwen2"))
+        else:
+            return CausalLM(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+
+    if model_type == OPT:
+        return OPTSharded(
+            model_id,
+            revision,
+            quantize=quantize,
+            speculator=speculator,
+            dtype=dtype,
+            trust_remote_code=trust_remote_code,
+        )
+
+    if model_type == T5:
+        return T5Sharded(
+            model_id,
+            revision,
+            quantize=quantize,
+            speculator=speculator,
+            dtype=dtype,
+            trust_remote_code=trust_remote_code,
+        )
+    if model_type == IDEFICS:
+        if FLASH_ATTENTION:
+            return IDEFICSSharded(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        else:
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
+    if model_type == IDEFICS2:
+        if FLASH_ATTENTION:
+            return Idefics2(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        else:
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
+    if model_type == "paligemma":
+        if FLASH_ATTENTION:
+            return PaliGemma(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        else:
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
+
+    if model_type == LLAVA_NEXT:
+        if FLASH_ATTENTION:
+            return LlavaNext(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        else:
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("LlavaNext"))
+
+    if sharded:
+        raise NotImplementedError("sharded is not supported for AutoModel")
+    if quantize == "gptq":
+        raise NotImplementedError(
+            "gptq quantization is not supported for AutoModel, you can try to quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`"
+        )
+    if quantize == "awq":
+        raise NotImplementedError("awq quantization is not supported for AutoModel")
+    elif (quantize == "bitsandbytes-fp4") or (quantize == "bitsandbytes-nf4"):
+        raise NotImplementedError("4bit quantization is not supported for AutoModel")
+    elif quantize == "eetq":
+        raise NotImplementedError("Eetq quantization is not supported for AutoModel")
     if model_type in modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
         return CausalLM(
             model_id,
             revision,
+            quantize=quantize,
+            speculator=speculator,
+            dtype=dtype,
+            trust_remote_code=trust_remote_code,
+        )
+    if model_type in modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES:
+        return Seq2SeqLM(
+            model_id,
+            revision,
+            quantize=quantize,
             speculator=speculator,
             dtype=dtype,
             trust_remote_code=trust_remote_code,
         )
 
+    auto_map = config_dict.get("auto_map", None)
+    if trust_remote_code and auto_map is not None:
+        if "AutoModelForCausalLM" in auto_map.keys():
+            return CausalLM(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        if "AutoModelForSeq2SeqLM" in auto_map.keys():
+            return Seq2SeqLM(
+                model_id,
+                revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+
     raise ValueError(f"Unsupported model type {model_type}")
diff --git a/server/text_generation_server/models/bloom.py b/server/text_generation_server/models/bloom.py
index 6fe643748..65c9f317b 100644
--- a/server/text_generation_server/models/bloom.py
+++ b/server/text_generation_server/models/bloom.py
@@ -1,14 +1,25 @@
-# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
 import torch
+import torch.distributed
 
 from typing import Optional, Type
 
-from transformers import PreTrainedTokenizerBase
+from transformers import (
+    AutoTokenizer,
+    AutoConfig,
+    PreTrainedTokenizerBase,
+)
 
+from text_generation_server.models.custom_modeling.bloom_modeling import (
+    BloomForCausalLM,
+)
 from text_generation_server.models import CausalLM
 from text_generation_server.models.causal_lm import CausalLMBatch
 from text_generation_server.pb import generate_pb2
+from text_generation_server.utils import (
+    initialize_torch_distributed,
+    weight_files,
+    Weights,
+)
 
 
 class BloomCausalLMBatch(CausalLMBatch):
@@ -20,33 +31,88 @@ class BloomCausalLMBatch(CausalLMBatch):
         dtype: torch.dtype,
         device: torch.device,
     ) -> "CausalLMBatch":
-        batch = super().from_pb(
-            pb=pb,
-            tokenizer=tokenizer,
-            dtype=dtype,
-            device=device,
-        )
+        batch = super().from_pb(pb=pb, tokenizer=tokenizer, dtype=dtype, device=device)
         batch.keys_head_dim_last = False
         return batch
 
 
-class BLOOM(CausalLM):
+class BLOOMSharded(CausalLM):
     def __init__(
         self,
         model_id: str,
         revision: Optional[str] = None,
+        quantize: Optional[str] = None,
         speculator: Optional[str] = None,
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        super(BLOOM, self).__init__(
-            model_id=model_id,
+
+        self.process_group, rank, world_size = initialize_torch_distributed()
+        if torch.cuda.is_available():
+            device = torch.device(f"cuda:{rank}")
+            dtype = torch.float16 if dtype is None else dtype
+        else:
+            device = torch.device("cpu")
+            dtype = torch.float32 if dtype is None else dtype
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_id,
             revision=revision,
-            speculator=speculator,
-            dtype=dtype,
+            padding_side="left",
+            truncation_side="left",
             trust_remote_code=trust_remote_code,
         )
 
+        config = AutoConfig.from_pretrained(
+            model_id,
+            revision=revision,
+            slow_but_exact=False,
+            tp_parallel=True,
+            trust_remote_code=trust_remote_code,
+        )
+        config.pad_token_id = 3
+        config.quantize = quantize
+        config.speculator = speculator
+
+        torch.distributed.barrier(group=self.process_group)
+        filenames = weight_files(model_id, revision=revision, extension=".safetensors")
+        weights = Weights(
+            filenames,
+            device=device,
+            dtype=dtype,
+            process_group=self.process_group,
+            prefix="transformer",
+        )
+        if config.quantize == "gptq":
+            weights._set_gptq_params(model_id, revision)
+
+        model = BloomForCausalLM(config, weights)
+
+        torch.distributed.barrier(group=self.process_group)
+        super(CausalLM, self).__init__(
+            model=model,
+            tokenizer=tokenizer,
+            requires_padding=True,
+            dtype=dtype,
+            device=device,
+            rank=rank,
+            world_size=world_size,
+        )
+
     @property
     def batch_type(self) -> Type[CausalLMBatch]:
         return BloomCausalLMBatch
+
+    def forward(
+        self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
+    ):
+        outputs, speculative_logits = self.model.forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=True,
+        )
+
+        logits = outputs.logits
+        return logits, speculative_logits, outputs.past_key_values
diff --git a/server/text_generation_server/models/causal_lm.py b/server/text_generation_server/models/causal_lm.py
index f814a8e2c..81a02163d 100644
--- a/server/text_generation_server/models/causal_lm.py
+++ b/server/text_generation_server/models/causal_lm.py
@@ -1,38 +1,13 @@
-# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
-import bisect
-from dataclasses import dataclass
-from functools import wraps
-import itertools
-import math
-import os
-import tempfile
-import time
-from typing import Dict, List, Optional, Tuple, Type
-
 import torch
-import torch._dynamo
-from loguru import logger
+import time
+
+from dataclasses import dataclass
 from opentelemetry import trace
+from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase
+from typing import Optional, Tuple, List, Type, Dict
 
-import text_generation_server.habana_quantization_env as hq_env
-import habana_frameworks.torch as htorch
-from optimum.habana.utils import HabanaProfile
-from optimum.habana.transformers.generation import MODELS_OPTIMIZED_WITH_STATIC_SHAPES
-from optimum.habana.checkpoint_utils import (
-    get_repo_root,
-    model_on_meta,
-    write_checkpoints_json,
-)
-from transformers import (
-    AutoTokenizer,
-    AutoModelForCausalLM,
-    PreTrainedTokenizerBase,
-    AutoConfig,
-)
-
-from text_generation_server.utils.tokens import batch_top_tokens
 from text_generation_server.models import Model
+from text_generation_server.utils.tokens import batch_top_tokens
 from text_generation_server.models.types import (
     Batch,
     Tokens,
@@ -40,419 +15,55 @@ from text_generation_server.models.types import (
     GeneratedText,
 )
 from text_generation_server.pb import generate_pb2
-from text_generation_server.utils import (
-    HeterogeneousNextTokenChooser,
-    StoppingCriteria,
-    make_tokenizer_optional,
-    is_tokenizer_transparent,
-    pad_next_token_chooser_parameters,
-)
-from text_generation_server.utils.debug import dbg_trace
-from text_generation_server.utils.speculate import get_speculate
+from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
 
 tracer = trace.get_tracer(__name__)
 
-MAX_TOTAL_TOKENS = int(os.environ.get('MAX_TOTAL_TOKENS', 2048))
-BATCH_BUCKET_SIZE = int(os.environ.get('BATCH_BUCKET_SIZE', 8))
-PAD_SEQUENCE_TO_MULTIPLE_OF = int(os.environ.get('PAD_SEQUENCE_TO_MULTIPLE_OF', 128))
-PREFILL_BATCH_BUCKET_SIZE = int(os.environ.get('PREFILL_BATCH_BUCKET_SIZE', 4))
-CHUNK_SIZES = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048]
-LAZY_MODE = int(os.environ.get('PT_HPU_LAZY_MODE', 1))
-
-
-def torch_compile_for_eager(func):
-    if LAZY_MODE == 1:
-        return func
-    return torch.compile(func, backend="hpu_backend", options={"keep_input_mutations": True})
-
-
-def round_up(number, k):
-    return (number + k - 1) // k * k
-
-
-def to_tensor_indices(indices, device):
-    return torch.tensor(indices, dtype=torch.long, device=device)
-
-
-def calculate_chunks(offset):
-    result = []
-    while offset != 0:
-        sign = 1 if offset > 0 else -1
-        best_chunk = min((abs(offset - sign * c), sign * c) for c in CHUNK_SIZES)[1]
-        result.append(best_chunk)
-        offset = offset - best_chunk
-    return result
-
-
-def biggest_single_chunk(offset):
-    if offset != 0:
-        idx = bisect.bisect(CHUNK_SIZES, abs(offset))
-        return int(math.copysign(CHUNK_SIZES[idx - 1], offset))
-    else:
-        return 0
-
-
-@torch_compile_for_eager
-def grouped_pad(tensor_groups, dims, values):
-    grouped_result = []
-    for tensors, dim, value in zip(tensor_groups, dims, values):
-        padding = MAX_TOTAL_TOKENS - tensors[0].size(dim) if dim is not None else 0
-        if padding > 0:
-            assert dim in [-1, -2], f'Only dims -1 and -2 are supported! {dim}'
-            pad_shape = (0, 0, 0, padding) if dim == -2 else (0, padding)
-            result = [torch.nn.functional.pad(t, pad_shape, value=value) for t in tensors]
-        else:
-            result = [t for t in tensors]
-        grouped_result.append(result)
-        htorch.core.mark_step()
-    return grouped_result
-
-
-@torch_compile_for_eager
-def roll(tensor, chunk, dim, merge_graphs):
-    if dim is None:
-        return tensor
-    tensor = torch.roll(tensor, chunk, dim)
-    if not merge_graphs:
-        htorch.core.mark_step()
-    return tensor
-
-
-def grouped_roll(tensor_groups, chunk, dims, merge_graphs):
-    tensor_groups = [[roll(t, chunk, dim, merge_graphs) for t in tensors] for tensors, dim in zip(tensor_groups, dims)]
-    if merge_graphs:
-        htorch.core.mark_step()
-    return tensor_groups
-
-
-@torch_compile_for_eager
-def grouped_shift(tensor_groups, dims, offset, merge_graphs):
-    chunks = calculate_chunks(offset)
-    for c in chunks:
-        tensor_groups = grouped_roll(tensor_groups, c, dims, merge_graphs)
-    return tensor_groups
-
-
-def move(dst_tensors, dst_indices, src_tensors):
-    bs_dim = 0
-    num_indices = dst_indices.size(0)
-    for i, (dst_t, src_t) in enumerate(zip(dst_tensors, src_tensors)):
-        if src_t.size(bs_dim) != num_indices:
-            src_t = torch.narrow(src_t, bs_dim, 0, num_indices)
-        dst_t.index_copy_(bs_dim, dst_indices, src_t)
-    htorch.core.mark_step()
-
-
-def grouped_move(dst_tensor_groups, dst_indices, src_tensor_groups):
-    for dst_tensors, src_tensors in zip(dst_tensor_groups, src_tensor_groups):
-        move(dst_tensors, dst_indices, src_tensors)
-
-
-@torch_compile_for_eager
-def extend_tensor(tensor, padding, dim):
-    result = torch.cat([tensor, padding], dim=dim)
-    htorch.core.mark_step()
-    return result
-
-
-@torch_compile_for_eager
-def extend_batch(tensors, target_bs, dim):
-    diff = target_bs - tensors[0].size(dim)
-    # TODO: add support for shrinking bs
-    if diff <= 0:
-        return tensors
-    shape = list(tensors[0].shape)
-    shape[dim] = diff
-    padding = torch.empty(shape, device=tensors[0].device, dtype=tensors[0].dtype)
-    tensors = [extend_tensor(t, padding, dim) for t in tensors]
-    return tensors
-
-
-def grouped_extend_batch(tensor_groups, target_bs, bs_dims):
-    tensor_groups = [extend_batch(tensors, target_bs, dim) for tensors, dim in zip(tensor_groups, bs_dims)]
-    return tensor_groups
-
-
-@torch_compile_for_eager
-def merge(tensor_group):
-    tensor_group = [torch.stack(tensor_group)]
-    htorch.core.mark_step()
-    return tensor_group
-
-
-@torch_compile_for_eager
-def split(tensor_group, clone_data):
-    tensor_group = [t.squeeze(0) for t in torch.split(tensor_group[0], 1)]
-    if clone_data:
-        tensor_group = [t.clone() for t in tensor_group]
-    htorch.core.mark_step()
-    return tensor_group
-
-
-def remove_kv_cache_from_output(module):
-    orig_fwd = module.forward
-
-    @wraps(orig_fwd)
-    def forward(*args, **kwargs):
-        if kwargs["past_key_values"] is not None:
-            kwargs["return_dict"] = False
-            output = orig_fwd(*args, **kwargs)
-            first_value, second_value, *_ = output
-            if first_value.nelement() < 2:
-                return second_value
-            else:
-                return first_value
-        else:
-            kwargs["return_dict"] = True
-            return orig_fwd(*args, **kwargs)
-
-    module.forward = forward
-    return module
-
-
-@dataclass
-class CausalLMRequest:
-    idx: int
-    data: generate_pb2.Request
-    input_length: int
-    prefix_offset: int
-    read_offset: int
-    stopping_criteria: StoppingCriteria
-
-    all_input_ids: torch.Tensor
-
-    @classmethod
-    def from_pb(cls, idx: int, data: generate_pb2.Request, tokenizer: PreTrainedTokenizerBase):
-        return cls(
-            idx=idx,
-            data=data,
-            input_length=None,
-            prefix_offset=None,
-            read_offset=None,
-            stopping_criteria=StoppingCriteria.from_pb(data.stopping_parameters, tokenizer),
-            all_input_ids=None,)
-
-    def update_idx(self, new_idx):
-        prev = self.idx
-        self.idx = new_idx
-        return (new_idx, prev)
-
 
 @dataclass
 class CausalLMBatch(Batch):
     batch_id: int
-    requests: List[CausalLMRequest]
+    requests: List[generate_pb2.Request]
+    requests_idx_mapping: Dict[int, int]
 
     # Decoder values
     input_ids: torch.Tensor
     attention_mask: torch.Tensor
     position_ids: torch.Tensor
     past_key_values: Optional[List[Tuple]]
-    merged_kv_cache: bool
+
+    # All tokens
+    all_input_ids: List[torch.Tensor]
+
+    # Lengths of all generations present in the batch
+    input_lengths: List[int]
+    prefix_offsets: List[int]
+    read_offsets: List[int]
 
     # Generation helpers
-    next_token_chooser: HeterogeneousNextTokenChooser
+    next_token_choosers: List[NextTokenChooser]
+    stopping_criterias: List[StoppingCriteria]
     top_n_tokens: List[int]
     top_n_tokens_tensor: torch.Tensor
 
-    input_length: int
+    # Metadata used for padding
+    max_input_length: int
+    padding_right_offset: int
 
-    logits = None
-    past = None
+    # Maximum number of tokens this batch will grow to
+    max_tokens: int
 
+    # Past metadata
     keys_head_dim_last: bool = True
 
     def to_pb(self) -> generate_pb2.CachedBatch:
         return generate_pb2.CachedBatch(
             id=self.batch_id,
-            request_ids=[r.data.id for r in self.requests],
+            request_ids=[r.id for r in self.requests],
             size=len(self),
             max_tokens=self.max_tokens,
         )
 
-    def detach_kv_cache(self):
-        past_keys = [past[0] for past in self.past_key_values]
-        past_values = [past[1] for past in self.past_key_values]
-        del self.past_key_values
-        return past_keys, past_values
-
-    def attach_kv_cache(self, past_keys, past_values):
-        # TODO: Add support for models that don't store kv_cache in a list
-        self.past_key_values = list(zip(past_keys, past_values))
-
-    def merge_kv_cache_if_needed(self, target_bs, offset):
-        pad_needed = self.seq_length < MAX_TOTAL_TOKENS
-        shift_needed = offset != 0
-        expand_needed = target_bs > self.batch_size
-        # Very simple heuristic to determine whether we should merge tensors
-        # this needs tuning for other models/scenarios
-        small_bs = len(self.past_key_values) > self.batch_size
-        if not self.merged_kv_cache and small_bs and (pad_needed or shift_needed or expand_needed):
-            past_keys, past_values = self.detach_kv_cache()
-            past_keys = merge(past_keys)
-            past_values = merge(past_values)
-            self.attach_kv_cache(past_keys, past_values)
-            self.merged_kv_cache = True
-
-    def split_kv_cache_if_needed(self, clone_data):
-        if self.merged_kv_cache:
-            past_keys, past_values = self.detach_kv_cache()
-            past_keys = split(past_keys, clone_data)
-            past_values = split(past_values, clone_data)
-            self.attach_kv_cache(past_keys, past_values)
-            self.merged_kv_cache = False
-
-    def get_tensor_groups(self):
-        past_keys, past_values = self.detach_kv_cache()
-        seq_dim = -1
-        key_dim = -2 if self.keys_head_dim_last else -1
-        value_dim = -2
-        tensors = [[self.input_ids], [self.attention_mask], [self.position_ids], past_keys, past_values]
-        # We don't need to align position_ids
-        seq_dims = [seq_dim, seq_dim, None, key_dim, value_dim]
-        bs_dims = [0, 0, 0] + ([1, 1] if self.merged_kv_cache else [0, 0])
-        return tensors, seq_dims, bs_dims
-
-    def set_tensor_groups(self, tensors):
-        self.input_ids = tensors.pop(0)[0]
-        self.attention_mask = tensors.pop(0)[0]
-        self.position_ids = tensors.pop(0)[0]
-        past_keys = tensors.pop(0)
-        past_values = tensors.pop(0)
-        self.attach_kv_cache(past_keys, past_values)
-
-    def realign(self, target_bs, offset, pad_token_id):
-        tensors, seq_dims, _ = self.get_tensor_groups()
-        tensors = grouped_pad(tensors, seq_dims, [pad_token_id, 0, 0, 0, 0])
-        tensors = grouped_shift(tensors, seq_dims, offset, self.merged_kv_cache)
-        self.set_tensor_groups(tensors)
-
-    def expand_bs(self, target_bs):
-        tensors, _, bs_dims = self.get_tensor_groups()
-        tensors = grouped_extend_batch(tensors, target_bs, bs_dims)
-        self.set_tensor_groups(tensors)
-
-    def used_indices(self):
-        return [req.idx for req in self.requests]
-
-    def update_indices(self, new_indices):
-        for req, new_idx in zip(self.requests, new_indices):
-            req.idx = new_idx
-        return self.used_indices()
-
-    def free_indices_generator(self):
-        used = set(req.idx for req in self.requests)
-        return (i for i in range(self.batch_size) if i not in used)
-
-    def move_data(self, src_batches):
-        dst_tensors, _, dst_dims = self.get_tensor_groups()
-        free_indices_gen = self.free_indices_generator()
-        for src_b in src_batches:
-            dst_indices = to_tensor_indices(src_b.update_indices(free_indices_gen), self.input_ids.device)
-            src_tensors, _, src_dims = src_b.get_tensor_groups()
-            grouped_move(dst_tensors, dst_indices, src_tensors)
-        self.set_tensor_groups(dst_tensors)
-
-    @classmethod
-    def recombine(cls, batches: List["CausalLMBatch"], pad_token_id: int) -> "CausalLMBatch":
-        if not all(b.past_key_values is not None for b in batches):
-            raise ValueError("KV cache not allocated! Cannot recombine before prefill!")
-
-        total_requests = sum(len(b) for b in batches)
-        new_bs = round_up(total_requests, BATCH_BUCKET_SIZE)
-        batch_id = batches[0].batch_id
-        device = batches[0].input_ids.device
-
-        input_lengths = [b.input_length for b in batches]
-        max_input_length = max(input_lengths)
-        offsets = [max_input_length - b.input_length for b in batches]
-
-        cur_padding = [b.right_padding for b in batches]
-        # For prefill there is a space allocated only for first token
-        # Need to add padding to the max total tokens before first decode
-
-        moves_needed = [total_requests - len(b) if b.batch_size == new_bs else total_requests for b in batches]
-        dst_batch_idx = min(enumerate(moves_needed), key=lambda idx_val: idx_val[1])[0]
-        reshape = (batches[dst_batch_idx].batch_size < new_bs)
-
-        # TODO: Add support for changing max seq len, i.e. due to output length bucketing
-        # FIXME: max_seq_len for non optimized code
-        if len(batches) > 1:
-            scenario = 'CONCAT'
-        elif reshape:
-            scenario = 'RESHAPE'
-        elif cur_padding[dst_batch_idx] <= 0:
-            scenario = 'SHIFT'
-            offsets = [biggest_single_chunk(b.max_input_length - max_input_length) for b in batches]
-            max_input_length = max_input_length + offsets[dst_batch_idx]
-        else:
-            # Nothing to do
-            return batches[0]
-
-        dbg_trace(
-            scenario, f'bs:{[b.batch_size for b in batches]}->{new_bs}'
-                      f' reqs:{[len(b) for b in batches]}'
-                      f' offsets:{offsets}'
-                      f' input_lengths:{input_lengths}'
-                      f' cur_padding:{cur_padding}'
-                      f' dst_batch:{dst_batch_idx}')
-
-        grouped_requests = [[req for req in batch.requests] for batch in batches]
-        flat_requests = list(itertools.chain(*grouped_requests))
-
-        for i in range(len(batches)):
-            target_bs = new_bs if i == dst_batch_idx else batches[i].batch_size
-            batches[i].merge_kv_cache_if_needed(target_bs, offsets[i])
-            batches[i].realign(target_bs, offsets[i], pad_token_id)
-            batches[i].split_kv_cache_if_needed(i == dst_batch_idx)
-        batches[dst_batch_idx].expand_bs(new_bs)
-        batches[dst_batch_idx].move_data([batches[i] for i in range(len(batches)) if i != dst_batch_idx])
-
-        top_n_tokens = [r.data.top_n_tokens for r in flat_requests]
-        top_n_tokens_tensor = torch.tensor(top_n_tokens, device=device, dtype=torch.int64)
-
-        parameters = [r.data.parameters for r in flat_requests]
-        # append the dummy parameters for dummy requests
-        batch_size = batches[dst_batch_idx].batch_size
-        parameters = pad_next_token_chooser_parameters(parameters, batch_size)
-
-        # update past grammar states
-        fsm_grammar_states = [0] * batch_size
-        for batch in batches:
-            for i, req in enumerate(batch.requests):
-                fsm_grammar_states[req.idx] = batch.next_token_chooser.fsm_grammar_states[i]
-
-        next_token_chooser = HeterogeneousNextTokenChooser.from_pb(
-            parameters,
-            batches[dst_batch_idx].next_token_chooser.dtype,
-            batches[dst_batch_idx].next_token_chooser.device,
-            batches[dst_batch_idx].next_token_chooser.tokenizer,
-            fsm_grammar_states,
-            quantization_enabled=hq_env.is_quantization_enabled,
-        )
-
-        input_ids = batches[dst_batch_idx].input_ids
-        attention_mask = batches[dst_batch_idx].attention_mask
-        position_ids = batches[dst_batch_idx].position_ids
-        past_key_values = batches[dst_batch_idx].past_key_values
-        input_length = max_input_length
-
-        htorch.core.mark_step()
-
-        return cls(
-            batch_id=batch_id,
-            requests=flat_requests,
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            merged_kv_cache=False,
-            next_token_chooser=next_token_chooser,
-            top_n_tokens=top_n_tokens,
-            top_n_tokens_tensor=top_n_tokens_tensor,
-            input_length=input_length,
-        )
-
     @classmethod
     def from_pb(
         cls,
@@ -461,152 +72,433 @@ class CausalLMBatch(Batch):
         dtype: torch.dtype,
         device: torch.device,
     ) -> "CausalLMBatch":
-        dbg_trace('FROM_PB', f'num_reqs:{len(pb.requests)}')
-        requests = [CausalLMRequest.from_pb(idx, req, tokenizer) for idx, req in enumerate(pb.requests)]
+        inputs = []
+        next_token_choosers = []
+        stopping_criterias = []
+        top_n_tokens = []
+        prefix_offsets = []
+        read_offsets = []
+        requests_idx_mapping = {}
 
-        max_input_length = max(r.data.truncate for r in requests)
-        max_new_tokens = max(r.stopping_criteria.max_new_tokens for r in requests)
+        # Parse batch
+        max_truncation = 0
+        padding_right_offset = 0
+        max_decode_tokens = 0
+        for i, r in enumerate(pb.requests):
+            requests_idx_mapping[r.id] = i
+            inputs.append(r.inputs)
+            next_token_choosers.append(
+                NextTokenChooser.from_pb(r.parameters, device, tokenizer)
+            )
+            stopping_criteria = StoppingCriteria.from_pb(
+                r.stopping_parameters, tokenizer
+            )
+            stopping_criterias.append(stopping_criteria)
+            top_n_tokens.append(r.top_n_tokens)
+            max_truncation = max(max_truncation, r.truncate)
+            max_decode_tokens += stopping_criteria.max_new_tokens
+            padding_right_offset = max(
+                padding_right_offset, stopping_criteria.max_new_tokens
+            )
 
-        # TODO: Add support for sparse batches
-        top_n_tokens = [r.top_n_tokens for r in pb.requests]
-        top_n_tokens_tensor = torch.tensor(top_n_tokens, device=device, dtype=torch.int64)
-
-        # TODO: by tokenizing all inputs at once we loose information on actual input lengths
-        # this means that we cannot shift inputs to the left after a long input sequence
-        # was filtered out
-        new_bs = round_up(len(requests), PREFILL_BATCH_BUCKET_SIZE)
-        missing_inputs = new_bs - len(requests)
-        dummy_inputs = ["?"] * missing_inputs
-        parameters = [r.parameters for r in pb.requests]
-        # append the dummy parameters for dummy request
-        parameters = pad_next_token_chooser_parameters(parameters, new_bs)
-
-        next_token_chooser = HeterogeneousNextTokenChooser.from_pb(
-            pb=parameters,
-            dtype=dtype,
-            device=device,
-            tokenizer=tokenizer,
-            quantization_enabled=hq_env.is_quantization_enabled,
-        )
         tokenized_inputs = tokenizer(
-            [r.data.inputs for r in requests] + dummy_inputs,
+            inputs,
             return_tensors="pt",
-            padding="longest",
+            padding=True,
             return_token_type_ids=False,
             truncation=True,
-            max_length=max_input_length,
-        )
+            max_length=max_truncation,
+        ).to(device)
+        for _ in pb.requests:
+            input_len = tokenized_inputs["input_ids"].shape[1]
+            prefix_offsets.append(input_len - 5)
+            read_offsets.append(input_len)
 
-        input_len = tokenized_inputs["input_ids"].shape[1]
-
-        bucket_size = max_input_length
-        left_padding = max_input_length - input_len
-        if input_len < max_input_length and PAD_SEQUENCE_TO_MULTIPLE_OF != 0:
-            assert PAD_SEQUENCE_TO_MULTIPLE_OF <= max_input_length, "PAD_SEQUENCE_TO_MULTIPLE_OF cannot be higher than max_input_length"
-            rounded_seq_len = round_up(input_len + 1, PAD_SEQUENCE_TO_MULTIPLE_OF)
-            if rounded_seq_len <= max_input_length:
-                bucket_size = rounded_seq_len - 1
-            else:
-                bucket_size = max_input_length - 1
-            left_padding = bucket_size - input_len
+        input_lengths = tokenized_inputs["attention_mask"].sum(1)
+        max_input_length = input_lengths.max()
 
         input_ids = tokenized_inputs["input_ids"]
-        attention_mask = tokenized_inputs["attention_mask"]
-
-        # Allocate space for first token
-        input_ids = torch.nn.functional.pad(
-            input_ids, (left_padding, 1), value=tokenizer.pad_token_id
+        # Allocate maximum attention_mask
+        attention_mask = input_ids.new_zeros(
+            (pb.size, max_input_length + padding_right_offset)
         )
-        attention_mask = torch.nn.functional.pad(
-            attention_mask, (left_padding, 1), value=0
+        # Copy tokenizer attention_mask into fully allocated attention_mask
+        attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
+
+        position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
+        position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
+        all_input_ids = tokenized_inputs["input_ids"].T.split(1, dim=1)
+        top_n_tokens_tensor = torch.tensor(
+            top_n_tokens, device=device, dtype=torch.int64
         )
-        all_input_ids = torch.nn.functional.pad(
-            input_ids, (0, max_new_tokens), value=tokenizer.pad_token_id
-        ).T.split(1, dim=1)
 
-        # New input length after left padding
-        input_len = bucket_size
-        for r in requests:
-            r.input_length = input_len
-            r.prefix_offset = input_len - 5
-            r.read_offset = input_len
-            r.all_input_ids = all_input_ids[r.idx]
-
-        input_ids = input_ids.to(device)
-        attention_mask = attention_mask.to(device)
-        position_ids = attention_mask.long().cumsum(-1) - 1
-        position_ids.masked_fill_(attention_mask == 0, 1)
-
-        htorch.core.mark_step()
+        max_tokens = len(inputs) * (max_input_length + max_decode_tokens)
 
         return cls(
             batch_id=pb.id,
-            requests=requests,
+            requests=pb.requests,
+            requests_idx_mapping=requests_idx_mapping,
             input_ids=input_ids,
             attention_mask=attention_mask,
             position_ids=position_ids,
             past_key_values=None,
-            merged_kv_cache=False,
-            next_token_chooser=next_token_chooser,
+            all_input_ids=list(all_input_ids),
+            input_lengths=input_lengths.tolist(),
+            prefix_offsets=prefix_offsets,
+            read_offsets=read_offsets,
+            next_token_choosers=next_token_choosers,
+            stopping_criterias=stopping_criterias,
             top_n_tokens=top_n_tokens,
             top_n_tokens_tensor=top_n_tokens_tensor,
-            input_length=input_len,
+            max_input_length=max_input_length.item(),
+            padding_right_offset=padding_right_offset,
+            max_tokens=max_tokens,
         )
 
     @tracer.start_as_current_span("filter")
     def filter(self, request_ids: List[int]) -> Optional["CausalLMBatch"]:
-        dbg_trace('FILTER', f'num_reqs:{len(self.requests)} -> {len(request_ids)}')
-        request_ids = set(request_ids)
-        self.requests = [req for req in self.requests if req.data.id in request_ids]
+        if len(request_ids) == 0:
+            raise ValueError("Batch must have at least one request")
+        if len(request_ids) == len(self):
+            return self
+
+        keep_indices = []
+
+        # New values after filtering
+        requests_idx_mapping = {}
+        requests = []
+        input_lengths = []
+        prefix_offsets = []
+        read_offsets = []
+        all_input_ids = []
+        max_input_length = 0
+
+        next_token_choosers = []
+        stopping_criterias = []
+        top_n_tokens = []
+
+        total_remaining_decode_tokens = 0
+        new_padding_right_offset = 0
+
+        for i, request_id in enumerate(request_ids):
+            idx = self.requests_idx_mapping[request_id]
+            requests_idx_mapping[request_id] = i
+            keep_indices.append(idx)
+
+            requests.append(self.requests[idx])
+            prefix_offsets.append(self.prefix_offsets[idx])
+            read_offsets.append(self.read_offsets[idx])
+            all_input_ids.append(self.all_input_ids[idx])
+
+            request_input_length = self.input_lengths[idx]
+            input_lengths.append(request_input_length)
+            max_input_length = max(max_input_length, request_input_length)
+
+            next_token_choosers.append(self.next_token_choosers[idx])
+            stopping_criteria = self.stopping_criterias[idx]
+            stopping_criterias.append(stopping_criteria)
+            top_n_tokens.append(self.top_n_tokens[idx])
+            remaining_decode_tokens = (
+                stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
+            )
+            total_remaining_decode_tokens += remaining_decode_tokens
+            new_padding_right_offset = max(
+                new_padding_right_offset, remaining_decode_tokens
+            )
+
+        # Apply indices to input_ids, attention mask, past key values and other items that need to be cached
+        input_ids = self.input_ids[keep_indices]
+        position_ids = self.position_ids[keep_indices]
+        self.attention_mask = self.attention_mask[
+            keep_indices,
+            -(self.padding_right_offset + max_input_length) : (
+                self.attention_mask.shape[1] - self.padding_right_offset
+            )
+            + new_padding_right_offset,
+        ]
+
+        # Ensure that past_key_values tensors can be updated in-place
+        if type(self.past_key_values[0]) == tuple:
+            self.past_key_values = [list(layer) for layer in self.past_key_values]
+
+        # Update tensors in-place to allow incremental garbage collection
+        past_kv_length = max_input_length - 1
+        for layer in self.past_key_values:
+            past_keys, past_values = layer
+            if len(past_keys.shape) == 3:
+                # Force past to be of dim [self_size, num_heads, ...] for easy indexing
+                past_keys = past_keys.view(len(self), -1, *past_keys.shape[-2:])
+                past_values = past_values.view(len(self), -1, *past_values.shape[-2:])
+            if self.keys_head_dim_last:
+                layer[0] = past_keys[keep_indices, :, -past_kv_length:, :]
+            else:
+                layer[0] = past_keys[keep_indices, :, :, -past_kv_length:]
+            del past_keys
+            layer[1] = past_values[keep_indices, :, -past_kv_length:, :]
+            del past_values
+
+        top_n_tokens_tensor = self.top_n_tokens_tensor[keep_indices]
+        max_tokens = len(request_ids) * max_input_length + total_remaining_decode_tokens
+
+        self.requests = requests
+        self.requests_idx_mapping = requests_idx_mapping
+        self.input_ids = input_ids
+        self.position_ids = position_ids
+        self.all_input_ids = all_input_ids
+        self.input_lengths = input_lengths
+        self.prefix_offsets = prefix_offsets
+        self.read_offsets = read_offsets
+        self.next_token_choosers = next_token_choosers
+        self.stopping_criterias = stopping_criterias
+        self.top_n_tokens = top_n_tokens
+        self.top_n_tokens_tensor = top_n_tokens_tensor
+        self.max_input_length = max_input_length
+        self.padding_right_offset = new_padding_right_offset
+        self.max_tokens = max_tokens
+
         return self
 
     @classmethod
     @tracer.start_as_current_span("concatenate")
-    def concatenate(cls, batches: List["CausalLMBatch"], pad_token_id: int = 0) -> "CausalLMBatch":
-        return cls.recombine(batches, pad_token_id)
+    def concatenate(cls, batches: List["CausalLMBatch"]) -> "CausalLMBatch":
+        # Used for padding
+        total_batch_size = 0
+        max_input_length = 0
+        padding_right_offset = 0
+        for batch in batches:
+            total_batch_size += len(batch)
+            max_input_length = max(max_input_length, batch.max_input_length)
+            padding_right_offset = max(padding_right_offset, batch.padding_right_offset)
+
+        # Batch attributes
+        requests = []
+        requests_idx_mapping = {}
+        input_lengths = []
+        prefix_offsets = []
+        read_offsets = []
+        all_input_ids = []
+        next_token_choosers = []
+        stopping_criterias = []
+        top_n_tokens = []
+        max_tokens = 0
+
+        # Batch tensors
+        input_ids = None
+        attention_mask = None
+        position_ids = None
+        past_key_values = []
+        top_n_tokens_tensor = None
+
+        # Used for slicing correctly inside the tensors
+        # Equivalent to a cumsum on batch sizes
+        start_index = 0
+        for i, batch in enumerate(batches):
+            requests.extend(batch.requests)
+            input_lengths.extend(batch.input_lengths)
+            prefix_offsets.extend(batch.prefix_offsets)
+            read_offsets.extend(batch.read_offsets)
+            all_input_ids.extend(batch.all_input_ids)
+            next_token_choosers.extend(batch.next_token_choosers)
+            stopping_criterias.extend(batch.stopping_criterias)
+            top_n_tokens.extend(batch.top_n_tokens)
+
+            if i == 0:
+                requests_idx_mapping = batch.requests_idx_mapping
+            else:
+                # We need to offset the mapping for each batch by the cumulative batch size
+                for k, v in batch.requests_idx_mapping.items():
+                    requests_idx_mapping[k] = v + start_index
+
+            # Slicing end index for this batch
+            end_index = start_index + len(batch)
+
+            # We only concatenate batches that did at least one step
+            if batch.past_key_values is None:
+                raise ValueError("only concatenate prefilled batches")
+
+            # Create empty tensor
+            # input_ids is always of shape [batch_size, 1]
+            # We do not need to pad it
+            if input_ids is None:
+                input_ids = batch.input_ids.new_empty((total_batch_size, 1))
+            # Copy to correct indices
+            input_ids[start_index:end_index] = batch.input_ids
+
+            # Create padded tensor
+            if attention_mask is None:
+                attention_mask = batch.attention_mask.new_zeros(
+                    (total_batch_size, max_input_length + padding_right_offset),
+                )
+
+            if top_n_tokens_tensor is None:
+                top_n_tokens_tensor = batches[0].top_n_tokens_tensor.new_zeros(
+                    total_batch_size,
+                )
+            top_n_tokens_tensor[start_index:end_index] = batch.top_n_tokens_tensor
+
+            # We need to slice the attention mask to remove padding from previous steps
+            # and to remove unused allocated space
+            left_offset = max_input_length - batch.max_input_length
+            batch_left_offset = (
+                batch.attention_mask.shape[1]
+                - batch.max_input_length
+                - batch.padding_right_offset
+            )
+            attention_mask[
+                start_index:end_index,
+                left_offset:-padding_right_offset,
+            ] = batch.attention_mask[
+                :,
+                batch_left_offset : -batch.padding_right_offset,
+            ]
+
+            # Create empty tensor
+            # position_ids is always of shape [batch_size, 1]
+            if position_ids is None:
+                position_ids = batch.position_ids.new_empty((total_batch_size, 1))
+            position_ids[start_index:end_index] = batch.position_ids
+
+            # Shenanigans to get dimensions because BLOOM outputs a past with a different shape
+            # BLOOM Keys:   [batch_size * num_heads, head_dim, seq_length]
+            # BLOOM Values: [batch_size * num_heads, seq_length, head_dim]
+            # And ensure that we can update tensors in-place
+            if type(batch.past_key_values[0]) == tuple:
+                batch.past_key_values = [
+                    [t.view(len(batch), -1, *t.shape[-2:]) for t in layer]
+                    for layer in batch.past_key_values
+                ]
+            elif len(batch.past_key_values[0][0].shape) == 3:
+                for layer in batch.past_key_values:
+                    for k, t in enumerate(layer):
+                        layer[k] = t.view(len(batch), -1, *t.shape[-2:])
+
+            # Add eventual padding tokens that were added while concatenating
+            max_tokens += batch.max_tokens + (
+                max_input_length - batch.max_input_length
+            ) * len(batch)
+
+            start_index = end_index
+
+        first_past_kvs = batches[0].past_key_values
+        _, num_heads, padded_sequence_length, head_dim = first_past_kvs[0][1].shape
+
+        padded_past_values_shape = (
+            total_batch_size,
+            num_heads,
+            max_input_length - 1,
+            head_dim,
+        )
+
+        if batches[0].keys_head_dim_last:
+            padded_past_keys_shape = padded_past_values_shape
+        else:
+            # seq_length is last for BLOOM
+            padded_past_keys_shape = (
+                total_batch_size,
+                num_heads,
+                head_dim,
+                max_input_length - 1,
+            )
+
+        # Iterate over attention layers
+        # Concatenate past key values layer by layer to allow incremental garbage collection
+        for j in range(len(first_past_kvs)):
+            padded_past_keys = first_past_kvs[j][0].new_zeros(padded_past_keys_shape)
+            start_index = 0
+            for batch in batches:
+                past_keys = batch.past_key_values[j][0]
+                # Clear reference to the original tensor
+                batch.past_key_values[j][0] = None
+
+                # Slicing end index for this batch
+                end_index = start_index + len(batch)
+                # We slice the keys to remove the padding from previous batches
+                past_seq_len = batch.max_input_length - 1
+                if batch.keys_head_dim_last:
+                    padded_past_keys[start_index:end_index, :, -past_seq_len:, :] = (
+                        past_keys[:, :, -past_seq_len:, :]
+                    )
+                else:
+                    # BLOOM case
+                    padded_past_keys[start_index:end_index, :, :, -past_seq_len:] = (
+                        past_keys[:, :, :, -past_seq_len:]
+                    )
+                del past_keys
+
+                start_index = end_index
+
+            padded_past_values = first_past_kvs[j][1].new_zeros(
+                padded_past_values_shape
+            )
+            start_index = 0
+            for batch in batches:
+                past_values = batch.past_key_values[j][1]
+                # Clear reference to the original tensor
+                batch.past_key_values[j][1] = None
+
+                # Slicing end index for this batch
+                end_index = start_index + len(batch)
+                # We slice the past values to remove the padding from previous batches
+                past_seq_len = batch.max_input_length - 1
+                padded_past_values[start_index:end_index, :, -past_seq_len:, :] = (
+                    past_values[:, :, -past_seq_len:, :]
+                )
+                del past_values
+
+                # Update values
+                start_index = end_index
+
+            past_key_values.append([padded_past_keys, padded_past_values])
+
+        return cls(
+            batch_id=batches[0].batch_id,
+            requests=requests,
+            requests_idx_mapping=requests_idx_mapping,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            all_input_ids=all_input_ids,
+            input_lengths=input_lengths,
+            prefix_offsets=prefix_offsets,
+            read_offsets=read_offsets,
+            next_token_choosers=next_token_choosers,
+            stopping_criterias=stopping_criterias,
+            top_n_tokens=top_n_tokens,
+            top_n_tokens_tensor=top_n_tokens_tensor,
+            max_input_length=max_input_length,
+            padding_right_offset=padding_right_offset,
+            keys_head_dim_last=batches[0].keys_head_dim_last,
+            max_tokens=max_tokens,
+        )
 
     def __len__(self):
         return len(self.requests)
 
-    @property
-    def max_input_length(self):
-        return max(req.input_length for req in self.requests)
-
-    @property
-    def batch_size(self):
-        return self.attention_mask.size(0)
-
-    @property
-    def seq_length(self):
-        return self.attention_mask.size(1)
-
-    @property
-    def right_padding(self):
-        return self.seq_length - self.input_length
-
-    # Maximum number of tokens this batch will grow to
-    @property
-    def max_tokens(self):
-        max_total_tokens = self.attention_mask.size(1)
-        return len(self.requests) * max_total_tokens
-
 
 class CausalLM(Model):
     def __init__(
         self,
         model_id: str,
         revision: Optional[str] = None,
+        quantize: Optional[str] = None,
         speculator: Optional[str] = None,
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-
         if speculator:
             raise RuntimeError("Speculator decoding is not enabled for AutoModel")
 
-        self.prev_bs = 0
+        if torch.cuda.is_available():
+            device = torch.device("cuda")
+            dtype = torch.float16 if dtype is None else dtype
+        else:
+            if quantize:
+                raise ValueError("quantization is not available on CPU")
+
+            device = torch.device("cpu")
+            dtype = torch.float32 if dtype is None else dtype
 
-        # Create tokenizer
         tokenizer = AutoTokenizer.from_pretrained(
             model_id,
             revision=revision,
@@ -614,444 +506,153 @@ class CausalLM(Model):
             truncation_side="left",
             trust_remote_code=trust_remote_code,
         )
-        make_tokenizer_optional(tokenizer)
-
-        # Create model
-        world_size = int(os.getenv("WORLD_SIZE", "1"))
-        rank = int(os.getenv("RANK", "0"))
-        dtype = torch.bfloat16 if dtype is None else dtype
-        device = torch.device("hpu")
-
-        if hq_env.is_quantization_enabled:
-            htorch.core.hpu_set_env()
-
-        if world_size > 1:
-            model = self.get_deepspeed_model(
-                model_id, dtype, revision
-            )
-            model = self.prepare_model_for_quantization(model)
-        else:
-            get_repo_root(model_id)
-
-            # Check support for rope scaling
-            model_kwargs = {}
-            config = AutoConfig.from_pretrained(
-                model_id
-            )
-            if hasattr(config, "rope_scaling"):
-                model_kwargs["rope_scaling"] = self.get_rope_scaling()
-
-            model = AutoModelForCausalLM.from_pretrained(
-                model_id,
-                revision=revision,
-                torch_dtype=dtype,
-                trust_remote_code=trust_remote_code,
-                **model_kwargs
-            )
-            model = self.prepare_model_for_quantization(model)
-            model = model.eval().to(device)
-
-        self.enable_hpu_graph = os.getenv("ENABLE_HPU_GRAPH", "true").lower() == "true" and LAZY_MODE == 1
-        self.limit_hpu_graph = os.getenv("LIMIT_HPU_GRAPH", "false").lower() == "true"
-        model = remove_kv_cache_from_output(model)
-        if self.enable_hpu_graph:
-            from habana_frameworks.torch.hpu import wrap_in_hpu_graph
-            model = wrap_in_hpu_graph(model, disable_tensor_cache=True)
-        else:
-            if LAZY_MODE == 0:
-                # It is said that "keep_input_mutations" is safe for inference to be done
-                dbg_trace(
-                    "TORCH COMPILE", f'Torch compiling of model')
-                model.model = torch.compile(model.model, backend="hpu_backend", options={"keep_input_mutations": True})
-
-        model = self.setup_quantization(model)
-
-        if model.config.model_type not in MODELS_OPTIMIZED_WITH_STATIC_SHAPES:
-            raise ValueError(f"Model type {model.config.model_type} is not supported!")
+        model = AutoModelForCausalLM.from_pretrained(
+            model_id,
+            revision=revision,
+            torch_dtype=dtype,
+            device_map=(
+                "auto"
+                if torch.cuda.is_available() and torch.cuda.device_count() > 1
+                else None
+            ),
+            load_in_8bit=quantize == "bitsandbytes",
+            trust_remote_code=trust_remote_code,
+        )
+        if (
+            torch.cuda.is_available()
+            and torch.cuda.device_count() == 1
+            and quantize != "bitsandbytes"
+        ):
+            model = model.cuda()
 
         if tokenizer.pad_token_id is None:
             if model.config.pad_token_id is not None:
                 tokenizer.pad_token_id = model.config.pad_token_id
             elif model.config.eos_token_id is not None:
-                if isinstance(model.config.eos_token_id, int):
-                    tokenizer.pad_token_id = model.config.eos_token_id
-                elif isinstance(model.config.eos_token_id, list):
-                    tokenizer.pad_token_id = model.config.eos_token_id[0]
-                else:
-                    raise ValueError(
-                        f"{type(model.config.eos_token_id)} type of eos_token_id in the model's config is not supported for tokenizer.pad_token_id"
-                    )
+                tokenizer.pad_token_id = model.config.eos_token_id
             elif tokenizer.eos_token_id is not None:
                 tokenizer.pad_token_id = tokenizer.eos_token_id
             else:
                 tokenizer.add_special_tokens({"pad_token": "[PAD]"})
 
-        kwargs = {
-            "use_cache": True,
-            "return_dict": True,
-        }
-
-        if model.config.model_type in ["llama", "mistral", "starcoder2", "qwen2"]:
-            
-            if model.config.model_type in ["llama", "mistral", "qwen2"]:
-                kwargs["attn_softmax_bf16"] = True
-                kwargs["trim_logits"] = True
-
-            if os.getenv("USE_FLASH_ATTENTION", "false").lower() == "true":
-                kwargs["use_flash_attention"] = True
-            if os.getenv("FLASH_ATTENTION_RECOMPUTE", "false").lower() == "true":
-                kwargs["flash_attention_recompute"] = True
-
-        self.speculate = get_speculate()
-
         super(CausalLM, self).__init__(
             model=model,
             tokenizer=tokenizer,
             requires_padding=True,
             dtype=dtype,
             device=device,
-            rank=rank,
-            kwargs=kwargs,
         )
 
-        # Create profiler
-        ranks_to_profile = [int(val) for val in os.getenv("PROF_RANKS", "0").split(',')]
-        record_shapes = os.getenv("PROF_RECORD_SHAPES", "false").lower() == "true"
-        output_dir = os.getenv("PROF_PATH", "/tmp/hpu_profile")
-        self.profiling_warmup_steps = int(os.getenv("PROF_WARMUPSTEP", "0")) if rank in ranks_to_profile else 0
-        self.profiling_steps = int(os.getenv("PROF_STEP", "0")) if rank in ranks_to_profile else 0
-        self.profiling_wait_steps = int(os.getenv("PROF_WAITSTEP", "0"))
-        if self.profiling_steps > 0:
-            self.hb_profiler = HabanaProfile(
-                wait=self.profiling_wait_steps,
-                warmup=self.profiling_warmup_steps,
-                active=self.profiling_steps,
-                output_dir=output_dir,
-                record_shapes=record_shapes
-            )
-            self.hb_profiler.start()
-        else:
-            self.hb_profiler = None
-        self.step = 0
-
-    def get_deepspeed_model(
-        self,
-        model_id: str,
-        dtype: torch.dtype,
-        revision: Optional[str] = None
-    ) -> torch.nn.Module:
-        import deepspeed
-        from habana_frameworks.torch.distributed.hccl import initialize_distributed_hpu
-
-        world_size, rank, local_rank = initialize_distributed_hpu()
-        model_kwargs = {
-            "revision": revision
-        }
-
-        # Initialize process(es) for DeepSpeed
-        deepspeed.init_distributed(dist_backend="hccl")
-        logger.info(
-            "DeepSpeed is enabled. world_size {} rank {} local_rank {}".format(world_size, rank, local_rank)
-        )
-        config = AutoConfig.from_pretrained(model_id, **model_kwargs)
-        load_to_meta = model_on_meta(config)
-
-        # Check support for rope scaling
-        if hasattr(config, "rope_scaling"):
-            config.rope_scaling = self.get_rope_scaling()
-            model_kwargs["rope_scaling"] = self.get_rope_scaling()
-
-        if load_to_meta:
-            # Construct model with fake meta tensors, later will be replaced on devices during ds-inference ckpt load
-            with deepspeed.OnDevice(dtype=dtype, device="meta"):
-                model = AutoModelForCausalLM.from_config(config, torch_dtype=dtype)
-        else:
-            get_repo_root(model_id, local_rank=os.getenv("LOCAL_RANK"))
-            # TODO: revisit placement on CPU when auto-injection is possible
-            with deepspeed.OnDevice(dtype=dtype, device="cpu"):
-                model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=dtype, **model_kwargs)
-        model = model.eval()
-
-        # Initialize the model
-        ds_inference_kwargs = {"dtype": dtype}
-        ds_inference_kwargs["tensor_parallel"] = {"tp_size": world_size}
-        ds_inference_kwargs["enable_cuda_graph"] = False
-
-        if load_to_meta:
-            # model loaded to meta is managed differently
-            checkpoints_json = tempfile.NamedTemporaryFile(suffix=".json", mode="+w")
-            write_checkpoints_json(model_id, local_rank, checkpoints_json)
-            ds_inference_kwargs["checkpoint"] = checkpoints_json.name
-        model = deepspeed.init_inference(model, **ds_inference_kwargs)
-
-        return model.module
-
-    def get_rope_scaling(self) -> Optional[Dict]:
-        rope_scaling = os.getenv("ROPE_SCALING", None)
-        if rope_scaling is None:
-            return None
-
-        rope_factor = float(os.getenv("ROPE_FACTOR", 1.0))
-        return {
-            'type': rope_scaling, 'factor': float(rope_factor)
-        }
-
-    def setup_quantization(self, model):
-        if hq_env.is_quantization_enabled:
-            htorch.core.quantization._mark_params_as_const(model)
-            htorch.core.quantization._check_params_as_const(model)
-            htorch.core.hpu_initialize(model)
-        return model
-
-    def prepare_model_for_quantization(self, model):
-        if hq_env.is_quantization_enabled:
-            if model.config.model_type == "llama":
-                self.patch_scoped_linear_all_reduce(model)
-            model = hq_env.prepare_model_for_quantization(model)
-        return model
-
-    def patch_scoped_linear_all_reduce(self, model):
-        from deepspeed.module_inject.layers import LinearAllreduce
-        from optimum.habana.transformers.models.modeling_all_models import ScopedLinearAllReduce
-        for name, module in model.named_children():
-            if type(module) is LinearAllreduce:
-                SL = ScopedLinearAllReduce(mod=module)
-                setattr(model, name, SL)
-            self.patch_scoped_linear_all_reduce(module)
-
     @property
     def batch_type(self) -> Type[CausalLMBatch]:
         return CausalLMBatch
 
     def decode(self, generated_ids: List[int]) -> str:
-        return self.tokenizer.decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
-
-    def decode_token(
-        self,
-        all_input_ids: List[int],
-        prefix_offset: int = 0,
-        read_offset: int = 0,
-    ) -> Tuple[str, int, int]:
-        if is_tokenizer_transparent(self.tokenizer):
-            new_text = self.tokenizer.decode(all_input_ids[read_offset:], skip_special_tokens=False)
-            return new_text, read_offset, len(all_input_ids)
-        else:
-            return super().decode_token(all_input_ids, prefix_offset, read_offset)
+        return self.tokenizer.decode(
+            generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
 
     def forward(
-        self,
-        input_ids,
-        attention_mask,
-        position_ids,
-        token_idx,
-        past_key_values: Optional[List[Tuple]] = None,
-        bypass_hpu_graph: Optional[bool] = None,
-    ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
+        self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
+    ) -> Tuple[
+        torch.Tensor, Optional[torch.Tensor], List[Tuple[torch.Tensor, torch.Tensor]]
+    ]:
         # Model Forward
         kwargs = {
             "input_ids": input_ids,
             "attention_mask": attention_mask,
             "past_key_values": past_key_values,
-            "token_idx": token_idx,
+            "use_cache": True,
+            "return_dict": True,
         }
-
-        # Optimum Habana got "lazy_mode" key-val only supported for llama type of models
-        if self.model.config.model_type == "llama" :
-            kwargs["lazy_mode"] = LAZY_MODE == 1
-
         if self.has_position_ids:
             kwargs["position_ids"] = position_ids
 
-        if bypass_hpu_graph != None:
-            kwargs["bypass_hpu_graphs"] = bypass_hpu_graph
-
-        kwargs.update(self.kwargs)
-        if past_key_values is not None:
-            return self.model.forward(**kwargs)
+        outputs = self.model.forward(**kwargs)
+        if isinstance(outputs, tuple):
+            outputs, speculative_logits = outputs
         else:
-            outputs = self.model.forward(**kwargs)
-            return outputs.logits, outputs.past_key_values
+            speculative_logits = None
+        return outputs.logits, speculative_logits, outputs.past_key_values
 
     @tracer.start_as_current_span("generate_token")
     def generate_token(
-        self, batches: List[CausalLMBatch]
+        self, batch: CausalLMBatch
     ) -> Tuple[List[Generation], Optional[CausalLMBatch], Tuple[int, int]]:
         start = time.time_ns()
+        # slice the attention mask to the correct shape
+        attention_mask = batch.attention_mask[:, : -batch.padding_right_offset]
+
+        logits, speculative_logits, past = self.forward(
+            batch.input_ids,
+            attention_mask,
+            batch.position_ids,
+            batch.past_key_values,
+        )
+
         # Results
         generations: List[Generation] = []
-        prev_batches = []
-        requests_to_generate = []
-        # In order to pipeline any actions on CPU we perform the operation in 3 main stages:
-        # Stage 1. Collect next token ids of any previously started generations
-        for batch_id, batch in enumerate(batches):
-            if batch.logits is not None:
-                logits = batch.logits
-                past = batch.past
-                prefill = batch.past_key_values is None
-                if prefill:
-                    # no right padding for prefill
-                    token_idx_scalar = batch.attention_mask.shape[-1] - 1
-                    token_idx = torch.tensor(token_idx_scalar).to(self.device)
-                else:
-                    token_idx_scalar = batch.attention_mask.shape[-1] - batch.right_padding
-                    token_idx = torch.tensor(token_idx_scalar).to(self.device)
+        stopped = True
 
-                # Select next token
-                input_length = batch.input_length
-                if logits.shape[-2] > 1:
-                    next_token_ids, next_token_logprobs, logprobs, _, _ = batch.next_token_chooser(
-                        batch.input_ids, logits[:, input_length - 1: input_length, :].squeeze(-2), self.speculate
-                    )
-                else:
-                    next_token_ids, next_token_logprobs, logprobs, _, _ = batch.next_token_chooser(
-                        batch.input_ids, logits.squeeze(-2), self.speculate
-                    )
-                # Speculation is not active for causal
-                accepted_ids = torch.ones_like(batch.input_ids)[:, 0]
-                batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
-                    batch.top_n_tokens,
-                    batch.top_n_tokens_tensor,
-                    logprobs,
-                    accepted_ids,
-                )
-
-                prev_batches.append({
-                    'next_token_ids': next_token_ids,
-                    'next_token_logprobs': next_token_logprobs,
-                })
-
-                for req_idx, req in enumerate(batch.requests):
-                    requests_to_generate.append({
-                        'req': req,
-                        'prev_req_idx': req.idx,
-                        'batch_id': batch_id,
-                        'seed': batch.next_token_chooser.seeds[req_idx],
-                        'do_sample': batch.next_token_chooser.do_sample[req_idx],
-                        'top_n_tokens': batch.top_n_tokens[req_idx],
-                        'top_token_ids': batch_top_token_ids[req_idx],
-                        'top_token_logprobs': batch_top_token_logprobs[req_idx],
-                        'grammar_state': batch.next_token_chooser.fsm_grammar_states[req.idx],
-                    })
-
-                htorch.core.mark_step()
-
-                # Add new token into input_ids
-                batch.input_ids.index_copy_(1, token_idx, next_token_ids.unsqueeze(1))
-
-                # Update attention_mask as we added a new token to input_ids
-                batch.attention_mask.index_fill_(1, token_idx, 1)
-
-                # Adjust lengths
-                batch.input_length += 1
-
-                # Update position_ids
-                if prefill:
-                    batch.position_ids = torch.index_select(batch.position_ids, 1, token_idx - 1) + 1
-                else:
-                    batch.position_ids += 1
-                # Update past key values
-                if prefill:
-                    batch.past_key_values = past
-
-        htorch.core.mark_step()
-
-        # Stage 2. Prepare new batch for speculative scheduling
-        if len(batches) > 1:
-            batch = self.batch_type.concatenate(batches, self.tokenizer.pad_token_id)
-        else:
-            batch = batches[0]
-
-        prefill = batch.past_key_values is None
-
-        # Check if we need to do any bookkeeping first
-        if not prefill:
-            batch = batch.__class__.recombine([batch], self.tokenizer.pad_token_id)
-
-        scenario = 'PREFILL' if prefill else 'GENERATE'
-        if self.enable_hpu_graph and self.limit_hpu_graph and round_up(batch.batch_size, BATCH_BUCKET_SIZE) != self.prev_bs:
-            self.model.clear_cache()
-            self.prev_bs = round_up(batch.batch_size, BATCH_BUCKET_SIZE)
-        dbg_trace(
-            scenario, f'bs:{batch.batch_size} num_reqs:{len(batch.requests)} seq_len:{batch.seq_length} padding:{batch.right_padding}')
-        assert batch.right_padding > 0, 'No more room for next token!'
-
-        # Execute batch
-        if prefill:
-            # no right padding for prefill
-            token_idx = torch.tensor(batch.attention_mask.shape[-1] - 1).to(self.device)
-            batch.logits, batch.past = self.forward(
-                batch.input_ids,
-                batch.attention_mask,
-                batch.position_ids,
-                token_idx,
-                batch.past_key_values,
-                bypass_hpu_graph=prefill and self.limit_hpu_graph if self.enable_hpu_graph else None,
-            )
-        elif all([req.stopping_criteria.max_new_tokens == 1 for req in batch.requests]):
-            # Don't schedule next forward if max_new_tokens for all requests equals 1
-            # - we've already generated the first and only needed token in the prefill phase
-            pass
-        else:
-            token_idx = torch.tensor(batch.attention_mask.shape[-1] - batch.right_padding).to(self.device)
-            input_ids = torch.index_select(batch.input_ids, 1, token_idx - 1)
-            batch.logits = self.forward(
-                input_ids,
-                batch.attention_mask,
-                batch.position_ids,
-                token_idx,
-                batch.past_key_values,
-                bypass_hpu_graph=prefill and self.limit_hpu_graph if self.enable_hpu_graph else None,
-            )
-
-        htorch.core.mark_step()
+        # Speculation is not active for causal
+        accepted_ids = torch.ones_like(batch.input_ids)[:, 0]
+        batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
+            batch.top_n_tokens,
+            batch.top_n_tokens_tensor,
+            torch.log_softmax(logits[:, -1], -1),
+            accepted_ids,
+        )
 
         start_decode = time.time_ns()
 
-        # Stage 3. Finish and return previous generations
-        stopped = len(requests_to_generate) > 0
-        for prev_batch in prev_batches:
-            prev_batch['next_token_logprobs'] = prev_batch['next_token_logprobs'].tolist()
-            prev_batch['next_token_ids_cpu'] = prev_batch['next_token_ids'].cpu()
-        htorch.core.mark_step()
+        # Zipped iterator
+        iterator = zip(
+            batch.requests,
+            batch.input_lengths,
+            batch.prefix_offsets,
+            batch.read_offsets,
+            logits,
+            batch.next_token_choosers,
+            batch.stopping_criterias,
+            batch.all_input_ids,
+            batch.top_n_tokens,
+            batch_top_token_ids,
+            batch_top_token_logprobs,
+        )
 
-        for req_data in requests_to_generate:
-            req = req_data['req']
-            i = req_data['prev_req_idx']
-            prev_batch_id = req_data['batch_id']
-            assert len(prev_batches) > prev_batch_id
-            next_token_ids_cpu = prev_batches[prev_batch_id]['next_token_ids_cpu']
-            next_token_logprobs = prev_batches[prev_batch_id]['next_token_logprobs']
-
-            request = req.data
-            input_length = req.input_length
-            prefix_offset = req.prefix_offset
-            read_offset = req.read_offset
-            do_sample = req_data['do_sample']
-            seed = req_data['seed']
-            stopping_criteria = req.stopping_criteria
-            all_input_ids = req.all_input_ids
-            next_token_id = next_token_ids_cpu[i]
-            next_token_logprob = next_token_logprobs[i]
-            top_n_tokens = req_data['top_n_tokens']
-            top_token_ids = req_data['top_token_ids']
-            top_token_logprobs = req_data['top_token_logprobs']
-            grammar_state = req_data['grammar_state']
+        # For each member of the batch
+        for i, (
+            request,
+            input_length,
+            prefix_offset,
+            read_offset,
+            logits,
+            next_token_chooser,
+            stopping_criteria,
+            all_input_ids,
+            top_n_tokens,
+            top_token_ids,
+            top_token_logprobs,
+        ) in enumerate(iterator):
+            # Select next token
+            next_token_id, logprobs = next_token_chooser(
+                all_input_ids.view(1, -1), logits[-1:, :]
+            )
 
             # Append next token to all tokens
-            all_input_ids[input_length] = next_token_id
+            all_input_ids = torch.cat([all_input_ids, next_token_id])
             new_input_length = input_length + 1
 
             # Generated token
-            if is_tokenizer_transparent(self.tokenizer) and len(stopping_criteria.stop_sequence_criterias) == 0:
-                next_token_text = ''
-            else:
-                next_token_text, prefix_offset, read_offset = self.decode_token(
-                    all_input_ids[0:new_input_length, 0], prefix_offset, read_offset
-                )
+            next_token_logprob = logprobs[-1, next_token_id]
+            next_token_id_squeezed = next_token_id.squeeze()
+            next_token_text, prefix_offset, read_offset = self.decode_token(
+                all_input_ids[:, 0], prefix_offset, read_offset
+            )
 
             # Evaluate stopping criteria
             stop, reason = stopping_criteria(
-                next_token_id,
+                next_token_id_squeezed,
                 next_token_text,
             )
 
@@ -1063,17 +664,23 @@ class CausalLM(Model):
             if i % self.world_size == self.rank:
                 if stop:
                     # Decode generated tokens
-                    if is_tokenizer_transparent(self.tokenizer):
-                        output_text = None
+                    output_text, _, _ = self.decode_token(
+                        all_input_ids[:, 0],
+                        prefix_offset=len(all_input_ids)
+                        - stopping_criteria.current_tokens
+                        - 1,
+                        read_offset=len(all_input_ids)
+                        - stopping_criteria.current_tokens,
+                        skip_special_tokens=True,
+                    )
+                    # Get seed
+                    if isinstance(next_token_chooser.choice, Sampling):
+                        seed = next_token_chooser.choice.seed
                     else:
-                        output_text = self.decode(
-                            all_input_ids[new_input_length - stopping_criteria.current_tokens: new_input_length, 0]
-                        )
+                        seed = None
+
                     generated_text = GeneratedText(
-                        output_text,
-                        stopping_criteria.current_tokens,
-                        reason,
-                        seed if do_sample else None,
+                        output_text, stopping_criteria.current_tokens, reason, seed
                     )
                 else:
                     generated_text = None
@@ -1081,8 +688,12 @@ class CausalLM(Model):
                 # Prefill
                 if stopping_criteria.current_tokens == 1 and request.prefill_logprobs:
                     # Remove generated token to only have prefill and add nan for first prompt token
-                    prefill_logprobs = [float("nan")] + next_token_logprobs
-                    prefill_token_ids = all_input_ids[0: new_input_length - 1]
+                    prefill_logprobs = [float("nan")] + torch.log_softmax(
+                        logits, -1
+                    ).gather(1, all_input_ids[1:]).squeeze(1)[
+                        -new_input_length:-1
+                    ].tolist()
+                    prefill_token_ids = all_input_ids[-new_input_length:-1]
                     prefill_texts = self.tokenizer.batch_decode(
                         prefill_token_ids,
                         clean_up_tokenization_spaces=False,
@@ -1126,10 +737,10 @@ class CausalLM(Model):
                     request.id,
                     prefill_tokens,
                     Tokens(
-                        [next_token_id],
+                        [next_token_id_squeezed],
                         [next_token_logprob],
                         [next_token_text],
-                        [next_token_id in self.all_special_ids],
+                        [next_token_id_squeezed.item() in self.all_special_ids],
                     ),
                     generated_text,
                     top_tokens,
@@ -1137,58 +748,37 @@ class CausalLM(Model):
 
                 generations.append(generation)
 
-            batch.next_token_chooser = (
-                batch.next_token_chooser.advance_grammar_single_with_past_state(
-                    req.idx, next_token_id, grammar_state
-                )
+            # Update values
+            batch.next_token_choosers[i] = batch.next_token_choosers[i].advance_grammar(
+                next_token_id_squeezed.item()
             )
+            batch.input_ids[i, 0] = next_token_id
+            batch.all_input_ids[i] = all_input_ids
+            batch.input_lengths[i] = new_input_length
+            batch.prefix_offsets[i] = prefix_offset
+            batch.read_offsets[i] = read_offset
+            batch.max_input_length = max(batch.max_input_length, new_input_length)
 
-            req.all_input_ids = all_input_ids
-            req.input_length = new_input_length
-            req.prefix_offset = prefix_offset
-            req.read_offset = read_offset
+        # We finished all generations in the batch; there is no next batch
+        if stopped:
+            forward_ns = start_decode - start
+            decode_ns = time.time_ns() - start_decode
+            return generations, None, (forward_ns, decode_ns)
 
-        htorch.core.mark_step()
-        self.step = self.step + 1
-        if self.hb_profiler is not None:
-            if self.step > self.profiling_wait_steps + self.profiling_warmup_steps + self.profiling_steps:
-                self.hb_profiler.stop()
-            else:
-                self.hb_profiler.step()
+        # Slice unused values from prefill
+        batch.input_ids = batch.input_ids[:, :1]
+
+        # Update attention_mask as we added a new token to input_ids
+        batch.attention_mask[:, -batch.padding_right_offset] = 1
+        # Decrease right offset
+        batch.padding_right_offset -= 1
+
+        # Update position_ids
+        batch.position_ids = batch.position_ids[:, -1:] + 1
+
+        # Update past key values
+        batch.past_key_values = past
 
         forward_ns = start_decode - start
         decode_ns = time.time_ns() - start_decode
-        return generations, batch if not stopped else None, (forward_ns, decode_ns)
-
-    def warmup(self, batches: List[CausalLMBatch]) -> None:
-        def get_unfinished_requests(requests: List[CausalLMRequest]) -> List[int]:
-            return [
-                request.data.id for request in requests
-                if request.stopping_criteria.current_tokens < request.stopping_criteria.max_new_tokens
-            ]
-
-        # prefill
-        _, prefill_batch, _ = self.generate_token([batches.pop(0)])
-        # decode
-        _, decode_batch, _ = self.generate_token([prefill_batch])
-        # shifts
-        self.shifting_warmup(decode_batch)
-
-        while len(batches) > 0:
-            # prefill
-            _, prefill_batch, _ = self.generate_token([batches.pop(0)])
-            # concatenate and decode
-            _, decode_batch, _ = self.generate_token([decode_batch, prefill_batch])
-            # filter finished requests
-            request_ids = get_unfinished_requests(decode_batch.requests)
-            if len(request_ids) < len(decode_batch.requests):
-                decode_batch = decode_batch.filter(request_ids)
-
-    def shifting_warmup(self, batch: CausalLMBatch) -> None:
-        chunk_sizes = CHUNK_SIZES.copy()
-        chunk_sizes.extend([-chunk for chunk in chunk_sizes])
-
-        for chunk in chunk_sizes:
-            batch.merge_kv_cache_if_needed(batch.batch_size, chunk)
-            batch.realign(batch.batch_size, chunk, 0)
-            batch.split_kv_cache_if_needed(True)
+        return generations, batch, (forward_ns, decode_ns)
diff --git a/server/text_generation_server/models/custom_modeling/llava_next.py b/server/text_generation_server/models/custom_modeling/llava_next.py
index 1ef550190..de9673aa0 100644
--- a/server/text_generation_server/models/custom_modeling/llava_next.py
+++ b/server/text_generation_server/models/custom_modeling/llava_next.py
@@ -21,12 +21,18 @@ import torch.utils.checkpoint
 from torch import nn
 
 from transformers.activations import ACT2FN
-from transformers.models.llava_next.modeling_llava_next import (
-    unpad_image,
-)
-from optimum.habana.transformers.models import GaudiLlavaNextForConditionalGeneration 
 from transformers.image_processing_utils import select_best_resolution
 
+from text_generation_server.models.custom_modeling.vlm import (
+    load_text_model,
+    load_vision_model,
+)
+from text_generation_server.layers import (
+    TensorParallelColumnLinear,
+    TensorParallelRowLinear,
+)
+
+
 def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
     """
     Calculate the shape of the image patch grid after the preprocessing for images of any resolution.
@@ -50,13 +56,100 @@ def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
     return height // patch_size, width // patch_size
 
 
-class LlavaNextForConditionalGeneration(GaudiLlavaNextForConditionalGeneration):
-       
+def unpad_image(tensor, original_size):
+    """
+    Unpads a PyTorch tensor of a padded and resized image.
+
+    Args:
+        tensor (`torch.Tensor`):
+            The image tensor, assumed to be of shape (num_channels, height, width).
+        original_size (`tuple`):
+            The original size of the image (height, width).
+
+    Returns:
+        `torch.Tensor`: The unpadded image tensor.
+    """
+    original_height, original_width = original_size
+    current_height, current_width = tensor.shape[1:]
+
+    original_aspect_ratio = original_width / original_height
+    current_aspect_ratio = current_width / current_height
+
+    if original_aspect_ratio > current_aspect_ratio:
+        scale_factor = current_width / original_width
+        new_height = int(original_height * scale_factor)
+        padding = (current_height - new_height) // 2
+        unpadded_tensor = tensor[:, padding : current_height - padding, :]
+    else:
+        scale_factor = current_height / original_height
+        new_width = int(original_width * scale_factor)
+        padding = (current_width - new_width) // 2
+        unpadded_tensor = tensor[:, :, padding : current_width - padding]
+
+    return unpadded_tensor
+
+
+# Copied from transformers.models.llava.modeling_llava.LlavaMultiModalProjector with Llava->LlavaNext
+class LlavaNextMultiModalProjector(nn.Module):
+    def __init__(self, prefix, config, weights):
+        super().__init__()
+
+        self.linear_1 = TensorParallelColumnLinear.load(
+            prefix=f"{prefix}.linear_1", config=config, weights=weights, bias=True
+        )
+        self.act = ACT2FN[config.projector_hidden_act]
+        self.linear_2 = TensorParallelRowLinear.load(
+            prefix=f"{prefix}.linear_2", config=config, weights=weights, bias=True
+        )
+
+    def forward(self, image_features):
+        hidden_states = self.linear_1(image_features)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+
+
+class LlavaNextForConditionalGeneration(nn.Module):
+    def __init__(self, prefix, config, weights):
+        super().__init__()
+        config.vision_config.quantize = config.quantize
+        vision_config = config.vision_config
+        # Instead of selecting in hidden_states[-2].
+        # Instead compute only the n -2 + 1 layers and don't pool
+        if config.vision_feature_layer < 0:
+            vision_config.num_hidden_layers += config.vision_feature_layer + 1
+        else:
+            vision_config.num_hidden_layers = config.vision_feature_layer + 1
+        self.vision_tower = load_vision_model(
+            prefix="vision_tower" if not prefix else f"{prefix}.vision_tower",
+            config=config.vision_config,
+            weights=weights,
+        )
+
+        self.multi_modal_projector = LlavaNextMultiModalProjector(
+            prefix="multi_modal_projector", config=config, weights=weights
+        )
+
+        self.image_newline = weights.get_tensor("image_newline")
+
+        self.vocab_size = config.text_config.vocab_size
+        self.config = config
+        config.text_config.quantize = config.quantize
+        config.text_config.speculator = config.speculator
+        self.language_model = load_text_model(
+            prefix="language_model" if not prefix else f"{prefix}.language_model",
+            config=config.text_config,
+            weights=weights,
+        )
+        self.pad_token_id = (
+            config.pad_token_id if config.pad_token_id is not None else -1
+        )
+
     def _merge_input_ids_with_image_features(
         self,
+        input_ids: torch.Tensor,
         inputs_embeds: torch.Tensor,
         image_features: torch.Tensor,
-        input_ids: torch.Tensor,
     ):
         """In place merges in vision_embeddings with inputs_embeds."""
         mask = input_ids == self.config.image_token_index
@@ -71,226 +164,120 @@ class LlavaNextForConditionalGeneration(GaudiLlavaNextForConditionalGeneration):
 
     def forward(
         self,
-        input_ids: torch.LongTensor = None,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        cu_seqlen_prefill: Optional[torch.Tensor],
+        kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
+        block_tables: torch.Tensor,
+        slots: torch.Tensor,
+        input_lengths: torch.Tensor,
+        max_s: int,
+        prefill_cache_indices: Optional[torch.Tensor],
+        lm_head_indices: Optional[torch.Tensor] = None,
         pixel_values: torch.FloatTensor = None,
+        # Unused for this model
+        pixel_attention_mask=None,
         image_sizes: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        vision_feature_layer: Optional[int] = None,
-        vision_feature_select_strategy: Optional[str] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        token_idx: Optional[torch.Tensor] = None,
-        use_flash_attention: Optional[bool] = False,
-        flash_attention_recompute: Optional[bool] = False,
     ):
+        inputs_embeds = self.language_model.embed_tokens(input_ids)
+        if pixel_values is not None and len(pixel_values) > 0:
+            # num_special_image_tokens = (input_ids == self.config.image_token_index).sum()
+            # assert num_special_image_tokens == len(pixel_values), f"Received {num_special_image_tokens} for {len(pixel_values)} images, this is invalid"
+            # 1. Extract the input embeddings
 
-        if token_idx is not None:
-            output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-            output_hidden_states = (
-                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+            # 2. Merge text and images
+            num_images, num_patches, channels, height, width = pixel_values.shape
+            pixel_values = pixel_values.view(
+                num_images * num_patches, channels, height, width
             )
-            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-            if inputs_embeds is None:
-                inputs_embeds = self.get_input_embeddings()(input_ids)
+            image_features = self.vision_tower(pixel_values)
 
-            outputs = self.language_model(
-                attention_mask=attention_mask,
-                position_ids=position_ids,
-                past_key_values=past_key_values,
-                inputs_embeds=inputs_embeds,
-                use_cache=use_cache,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-                token_idx=token_idx,
-                use_flash_attention=use_flash_attention,
-                flash_attention_recompute=flash_attention_recompute,
-            )
+            # selected_image_feature = image_features.hidden_states[self.config.vision_feature_layer]
+            # Already done within the clip model
+            selected_image_feature = image_features.last_hidden_state
 
-            logits = outputs[0]
-
-            if not return_dict:
-                output = (logits,) + outputs[1:]
-                return output
-
-        return outputs
-
-    def prepare_inputs_for_generation(
-            self,
-            input_ids,
-            past_key_values=None,
-            inputs_embeds=None,
-            pixel_values=None,
-            image_sizes=None,
-            attention_mask=None,
-            **kwargs,
-        ):
-            """
-            Inherits from LlavaForConditionalGeneration: https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llava_next/modeling_llava_next.py#L635
-            The only differences are:
-            - add new args token_idx
-            - add the process of merging images into inputs_embeds
-            """
-            token_idx = kwargs.get("token_idx", None)
-            if token_idx is None:
-                return super().prepare_inputs_for_generation(
-                    input_ids=input_ids,
-                    past_key_values=past_key_values,
-                    inputs_embeds=inputs_embeds,
-                    pixel_values=pixel_values,
-                    image_sizes=image_sizes,
-                    attention_mask=attention_mask,
-                    **kwargs,
-                )
+            if self.config.vision_feature_select_strategy == "default":
+                selected_image_feature = selected_image_feature[:, 1:]
+            elif self.config.vision_feature_select_strategy == "full":
+                selected_image_feature = selected_image_feature
             else:
-                use_flash_attention = kwargs.get("use_flash_attention", False)
-                flash_attention_recompute = kwargs.get("flash_attention_recompute", False)
-                
-                position_ids = kwargs.get("position_ids", None)
-                labels = kwargs.get("labels", None)
-                if past_key_values is None and pixel_values is not None and input_ids.shape[1] != 1:
-                    vision_feature_select_strategy = kwargs.get("vision_feature_select_strategy", None)
-                    vision_feature_layer = kwargs.get("vision_feature_layer", None)
-                    vision_feature_select_strategy = (
-                        vision_feature_select_strategy
-                        if vision_feature_select_strategy is not None
-                        else self.config.vision_feature_select_strategy
-                    )
-                    vision_feature_layer = (
-                        vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
-                    )
-
-                    # 1. Extract the input embeddings
-                    inputs_embeds = self.get_input_embeddings()(input_ids)
-                    # 2. Merge text and images
-                    batch_size, num_patches, num_channels, height, width = pixel_values.shape
-                    reshaped_pixel_values = pixel_values.view(batch_size * num_patches, num_channels, height, width)
-                    image_features = self.vision_tower(
-                        reshaped_pixel_values,
-                        output_hidden_states=True,
-                        use_flash_attention=use_flash_attention,
-                        flash_attention_recompute=flash_attention_recompute,
-                    )
-
-                    selected_image_feature = image_features.hidden_states[vision_feature_layer]
-
-                    if vision_feature_select_strategy == "default":
-                        selected_image_feature = selected_image_feature[:, 1:]
-                    elif vision_feature_select_strategy == "full":
-                        selected_image_feature = selected_image_feature
-
-                    image_features = self.multi_modal_projector(selected_image_feature)
-
-                    # split up image_features for each of the individual images
-                    # hence we get a list of image_features, each of shape (5, num_patches, hidden_size)
-                    # if we assume each image has 5 image features (base image + 4 patches)
-                    split_sizes = [image.shape[0] for image in pixel_values]
-                    image_features = torch.split(image_features, split_sizes, dim=0)
-
-                    # NOTE we only support multimodal_patch_merge_type == "spatial_unpad"
-                    height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
-
-                    new_image_features = []
-                    for image_idx, image_feature in enumerate(image_features):
-                        if image_feature.shape[0] > 1:
-                            base_image_feature = image_feature[0]
-                            image_feature = image_feature[1:]
-
-                            if height * width != base_image_feature.shape[0]:
-                                raise ValueError("The number of patches is not consistent with the image size.")
-
-                            num_patch_height, num_patch_width = get_anyres_image_grid_shape(
-                                image_sizes[image_idx].tolist(),
-                                self.config.image_grid_pinpoints,
-                                self.config.vision_config.image_size,
-                            )
-                            
-                            image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
-                            image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
-                            image_feature = image_feature.flatten(1, 2).flatten(2, 3)
-                            image_feature = unpad_image(image_feature, image_sizes[image_idx])
-                            image_feature = torch.cat(
-                                (
-                                    image_feature,
-                                    self.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1),
-                                ),
-                                dim=-1,
-                            )
-                            image_feature = image_feature.flatten(1, 2).transpose(0, 1)
-                            image_feature = torch.cat((base_image_feature, image_feature), dim=0)
-                        else:
-                            image_feature = image_feature[0]
-                            image_feature = torch.cat((image_feature, self.image_newline[None]), dim=0)
-                        new_image_features.append(image_feature)
-                    image_features = torch.stack(new_image_features, dim=0)
-                    inputs_embeds = self._merge_input_ids_with_image_features(inputs_embeds, image_features, input_ids)
-                    self.image_offset = image_features.shape[1] - 1  # image_token has occupied 1 token position.
-                # In case input_ids.shape[1] == 1 & pixel_values==None & past_key_values != None, we are in the case of
-                # generation with cache
-                elif past_key_values is not None:
-                    seq_len = input_ids.shape[1]
-                    pad_len = seq_len - token_idx.item()
-                    input_ids = torch.index_select(input_ids, 1, token_idx - 1)
-                    # Retrieve the first layer to inspect the logits and mask out the hidden states
-                    # that are set to 0
-                    first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]
-
-                    # Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
-                    batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0)
-
-                    # Get the target length
-                    past_length = first_layer_past_key_value.shape[-1]
-                    extended_attention_mask = torch.ones(
-                        (attention_mask.shape[0], past_length),
-                        dtype=attention_mask.dtype,
-                        device=attention_mask.device,
-                    )
-                    # Filter out only the tokens that can be un-attended, this can happen
-                    # if one uses Llava + Fused modules where the cache on the
-                    # first iteration is already big enough, or if one passes custom cache
-                    valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
-                    new_batch_index = batch_index[valid_indices]
-                    new_non_attended_tokens = non_attended_tokens[valid_indices]
-
-                    # Zero-out the places where we don't need to attend
-                    extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
-
-                    attention_mask = extended_attention_mask
-                    attention_mask[:, -pad_len:] = 0
-
-                if attention_mask is not None and position_ids is None:
-                    # create position_ids on the fly for batch generation
-                    position_ids = attention_mask.long().cumsum(-1) - 1
-                    position_ids.masked_fill_(attention_mask == 0, 1)
-                    if past_key_values:
-                        if token_idx is not None:
-                            position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
-                        else:
-                            position_ids = position_ids[:, -input_ids.shape[1] :]
-
-                # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-                if inputs_embeds is not None and past_key_values is None:
-                    model_inputs = {"inputs_embeds": inputs_embeds}
-                else:
-                    model_inputs = {"input_ids": input_ids}
-
-                model_inputs.update(
-                    {
-                        "position_ids": position_ids,
-                        "past_key_values": past_key_values,
-                        "use_cache": kwargs.get("use_cache"),
-                        "attention_mask": attention_mask,
-                        "token_idx": token_idx,
-                        "labels": labels,
-                        "use_flash_attention": use_flash_attention,
-                        "flash_attention_recompute": flash_attention_recompute,
-                    }
+                raise RuntimeError(
+                    f"Strategy `{self.config.vision_feature_select_strategy}` is not supported/valid."
                 )
 
-                return model_inputs
+            image_features = self.multi_modal_projector(selected_image_feature)
+
+            # split up image_features for each of the individual images
+            # hence we get a list of image_features, each of shape (5, num_patches, hidden_size)
+            # if we assume each image has 5 image features (base image + 4 patches)
+            split_sizes = [num_patches] * num_images
+            image_features = torch.split(image_features, split_sizes, dim=0)
+
+            # NOTE we only support multimodal_patch_merge_type == "spatial_unpad"
+            height = width = (
+                self.config.vision_config.image_size
+                // self.config.vision_config.patch_size
+            )
+
+            new_image_features = []
+            for image_idx, image_feature in enumerate(image_features):
+                if image_feature.shape[0] > 1:
+                    base_image_feature = image_feature[0]
+                    image_feature = image_feature[1:]
+
+                    if height * width != base_image_feature.shape[0]:
+                        raise ValueError(
+                            "The number of patches is not consistent with the image size."
+                        )
+                    num_patch_height, num_patch_width = get_anyres_image_grid_shape(
+                        image_sizes[image_idx],
+                        self.config.image_grid_pinpoints,
+                        self.config.vision_config.image_size,
+                    )
+                    image_feature = image_feature.view(
+                        num_patch_height, num_patch_width, height, width, -1
+                    )
+                    image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
+                    image_feature = image_feature.flatten(1, 2).flatten(2, 3)
+                    image_feature = unpad_image(image_feature, image_sizes[image_idx])
+                    image_feature = torch.cat(
+                        (
+                            image_feature,
+                            self.image_newline[:, None, None].expand(
+                                *image_feature.shape[:-1], 1
+                            ),
+                        ),
+                        dim=-1,
+                    )
+                    image_feature = image_feature.flatten(1, 2).transpose(0, 1)
+                    image_feature = torch.cat(
+                        (base_image_feature, image_feature), dim=0
+                    )
+                else:
+                    image_feature = image_feature[0]
+                    image_feature = torch.cat(
+                        (image_feature, self.image_newline[None]), dim=0
+                    )
+                new_image_features.append(image_feature)
+            image_features = torch.stack(new_image_features, dim=0)
+
+            inputs_embeds = self._merge_input_ids_with_image_features(
+                input_ids, inputs_embeds, image_features
+            )
+
+        hidden_states = self.language_model.model(
+            inputs_embeds=inputs_embeds,
+            position_ids=position_ids,
+            cu_seqlen_prefill=cu_seqlen_prefill,
+            kv_cache=kv_cache,
+            block_tables=block_tables,
+            slots=slots,
+            input_lengths=input_lengths,
+            max_s=max_s,
+            true_max_s=max_s,
+            prefill_cache_indices=None,
+        )
+        if lm_head_indices is not None:
+            hidden_states = hidden_states[lm_head_indices]
+        logits, speculative_logits = self.language_model.lm_head(hidden_states)
+        return logits, speculative_logits
diff --git a/server/text_generation_server/models/flash_mistral.py b/server/text_generation_server/models/flash_mistral.py
index 87e192e0c..e6125e294 100644
--- a/server/text_generation_server/models/flash_mistral.py
+++ b/server/text_generation_server/models/flash_mistral.py
@@ -10,7 +10,7 @@ from transformers import PreTrainedTokenizerBase, AutoTokenizer, AutoConfig
 from typing import Optional, Tuple, Type
 
 from text_generation_server.pb import generate_pb2
-from text_generation_server.models.flash_causal_lm import FlashCausalLM
+from text_generation_server.models import FlashCausalLM
 from text_generation_server.models.flash_causal_lm import FlashCausalLMBatch, BLOCK_SIZE
 from text_generation_server.models.cache_manager import (
     get_cache_manager,
diff --git a/server/text_generation_server/models/model.py b/server/text_generation_server/models/model.py
index 7546e70be..4f35b0aa4 100644
--- a/server/text_generation_server/models/model.py
+++ b/server/text_generation_server/models/model.py
@@ -2,8 +2,8 @@ import inspect
 import torch
 
 from abc import ABC, abstractmethod
-from typing import List, Optional, Tuple, Type, TypeVar
-from transformers import PreTrainedTokenizerBase
+from typing import List, Tuple, Optional, TypeVar, Type
+from transformers import PreTrainedTokenizerBase, PretrainedConfig
 
 from text_generation_server.models.types import Batch, Generation
 from text_generation_server.utils.speculate import get_speculate
@@ -22,10 +22,10 @@ class Model(ABC):
         device: torch.device,
         rank: int = 0,
         world_size: int = 1,
-        kwargs: dict = {},
+        sliding_window: Optional[int] = None,
         speculate: Optional[int] = None,
     ):
-        self.model = model
+        self.model = model.eval()
         self.tokenizer = tokenizer
 
         # all_special_ids is not set correctly if the rust tokenizer is unpacked
@@ -40,7 +40,8 @@ class Model(ABC):
         self.device = device
         self.rank = rank
         self.world_size = world_size
-        self.kwargs = kwargs
+        self.sliding_window = sliding_window if sliding_window != -1 else None
+
         if speculate is None:
             speculate = get_speculate()
         self.speculate = speculate
@@ -54,10 +55,14 @@ class Model(ABC):
 
     @property
     def info(self) -> InfoResponse:
+        if self.requires_padding and self.sliding_window is not None:
+            raise NotImplementedError("sliding_window is not implemented with padding")
+
         return InfoResponse(
             requires_padding=self.requires_padding,
             dtype=str(self.dtype),
             device_type=self.device.type,
+            window_size=self.sliding_window,
             speculate=self.speculate,
         )
 
@@ -72,21 +77,28 @@ class Model(ABC):
     ) -> Tuple[List[Generation], Optional[B], Tuple[int, int]]:
         raise NotImplementedError
 
-    def warmup(self, batch: B, max_total_tokens: int):
+    def warmup(self, batch: B) -> Optional[int]:
         self.generate_token(batch)
+        return None
 
     def decode_token(
         self,
         all_input_ids: List[int],
         prefix_offset: int = 0,
         read_offset: int = 0,
+        skip_special_tokens: bool = False,
     ) -> Tuple[str, int, int]:
         """Hack to hopefully support generate_stream for the maximum number of tokenizers"""
 
         # The prefix text is necessary only to defeat cleanup algorithms in the decode
         # which decide to add a space or not depending on the surrounding ids.
-        prefix_text = self.tokenizer.decode(all_input_ids[prefix_offset:read_offset], skip_special_tokens=False)
-        new_text = self.tokenizer.decode(all_input_ids[prefix_offset:], skip_special_tokens=False)
+        prefix_text = self.tokenizer.decode(
+            all_input_ids[prefix_offset:read_offset],
+            skip_special_tokens=skip_special_tokens,
+        )
+        new_text = self.tokenizer.decode(
+            all_input_ids[prefix_offset:], skip_special_tokens=skip_special_tokens
+        )
 
         if len(new_text) > len(prefix_text) and not new_text.endswith("�"):
             # utf-8 char at the end means it's a potential unfinished byte sequence
diff --git a/server/text_generation_server/models/santacoder.py b/server/text_generation_server/models/santacoder.py
index f07734c24..323e4324c 100644
--- a/server/text_generation_server/models/santacoder.py
+++ b/server/text_generation_server/models/santacoder.py
@@ -1,5 +1,8 @@
-from typing import Optional, List
 import torch
+import torch.distributed
+
+from typing import Optional, List
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
 from text_generation_server.models import CausalLM
 
@@ -15,19 +18,29 @@ class SantaCoder(CausalLM):
         self,
         model_id: str,
         revision: Optional[str] = None,
+        quantize: Optional[str] = None,
         speculator: Optional[str] = None,
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        super().__init__(
-            model_id=model_id,
+        if torch.cuda.is_available():
+            device = torch.device("cuda")
+            dtype = torch.float16 if dtype is None else dtype
+        else:
+            if quantize:
+                raise ValueError("quantization is not available on CPU")
+
+            device = torch.device("cpu")
+            dtype = torch.float32 if dtype is None else dtype
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_id,
             revision=revision,
-            speculator=speculator,
-            dtype=dtype,
+            padding_side="left",
+            truncation_side="left",
             trust_remote_code=trust_remote_code,
         )
-
-        self.tokenizer.add_special_tokens(
+        tokenizer.add_special_tokens(
             {
                 "additional_special_tokens": [
                     EOD,
@@ -39,7 +52,25 @@ class SantaCoder(CausalLM):
                 "pad_token": EOD,
             }
         )
+        with device:
+            model = AutoModelForCausalLM.from_pretrained(
+                model_id,
+                revision=revision,
+                torch_dtype=dtype,
+                load_in_8bit=quantize == "bitsandbytes",
+                trust_remote_code=trust_remote_code,
+            )
+
+        super(CausalLM, self).__init__(
+            model=model,
+            tokenizer=tokenizer,
+            requires_padding=True,
+            dtype=dtype,
+            device=device,
+        )
 
     def decode(self, generated_ids: List[int]) -> str:
         # Do not skip special tokens as they are used for custom parsing rules of the generated text
-        return self.tokenizer.decode(generated_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)
+        return self.tokenizer.decode(
+            generated_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False
+        )
diff --git a/server/text_generation_server/models/starcoder.py b/server/text_generation_server/models/starcoder.py
deleted file mode 100644
index 98e7939a6..000000000
--- a/server/text_generation_server/models/starcoder.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from loguru import logger
-import torch
-from dataclasses import dataclass
-import os
-from typing import List, Optional, Type
-
-from text_generation_server.models import CausalLM
-from text_generation_server.models.causal_lm import CausalLMBatch
-
-
-@dataclass
-class StarCoderCausalLMBatch(CausalLMBatch):
-    past_key_values: Optional[List[torch.Tensor]]
-
-    def detach_kv_cache(self):
-        past_keys = []
-        past_values = []
-        last_dim = int(self.past_key_values[0].size(dim=-1)/2)
-        for key_value in self.past_key_values:
-            past_keys.append(key_value.split((last_dim, last_dim), dim=-1)[0])
-            past_values.append(key_value.split((last_dim, last_dim), dim=-1)[1])
-        del self.past_key_values
-
-        return past_keys, past_values
-
-    def attach_kv_cache(self, past_keys, past_values):
-        self.past_key_values = [
-            torch.cat((key, value), dim=-1) for key, value in zip(past_keys, past_values)]
-
-
-class StarCoder(CausalLM):
-    def __init__(
-        self,
-        model_id: str,
-        revision: Optional[str] = None,
-        dtype: Optional[torch.dtype] = None,
-    ):
-
-        super(StarCoder, self).__init__(
-            model_id=model_id,
-            revision=revision,
-            dtype=dtype,
-        )
-
-    @property
-    def batch_type(self) -> Type[CausalLMBatch]:
-        return StarCoderCausalLMBatch
\ No newline at end of file
diff --git a/server/text_generation_server/models/vlm_causal_lm.py b/server/text_generation_server/models/vlm_causal_lm.py
index 93cb179ad..f0db89b29 100644
--- a/server/text_generation_server/models/vlm_causal_lm.py
+++ b/server/text_generation_server/models/vlm_causal_lm.py
@@ -1,93 +1,29 @@
 import re
 import torch
-import os
-import time
 import math
 from PIL import Image
 from io import BytesIO
 import base64
-import numpy
+
 from opentelemetry import trace
-from loguru import logger
 from typing import Optional, Tuple, List, Type, Dict
-import itertools
-import tempfile
-import copy
-from text_generation_server.models import Model
+
 from transformers import PreTrainedTokenizerBase
 from transformers.image_processing_utils import select_best_resolution
-from text_generation_server.utils.tokens import batch_top_tokens
 from text_generation_server.pb import generate_pb2
-from text_generation_server.models.causal_lm import (
-    CausalLMBatch,
-    CausalLMRequest,
-    remove_kv_cache_from_output,
-    biggest_single_chunk,
+from text_generation_server.models.flash_mistral import (
+    BaseFlashMistral,
+    FlashMistralBatch,
 )
-
-from transformers.models.llava_next.modeling_llava_next import (
-    get_anyres_image_grid_shape,
-)
-
-from transformers import AutoProcessor
-import text_generation_server.habana_quantization_env as hq_env
-from optimum.habana.transformers.modeling_utils import adapt_transformers_to_gaudi
+from text_generation_server.models.flash_causal_lm import FlashCausalLMBatch
 from text_generation_server.models.cache_manager import (
     get_cache_manager,
 )
-from text_generation_server.utils import (
-    HeterogeneousNextTokenChooser,
-    StoppingCriteria,
-    make_tokenizer_optional,
-    is_tokenizer_transparent,
-    pad_next_token_chooser_parameters,
-)
-import habana_frameworks.torch as htorch
-from optimum.habana.utils import HabanaProfile
-from optimum.habana.transformers.generation import MODELS_OPTIMIZED_WITH_STATIC_SHAPES
-from optimum.habana.utils import get_hpu_memory_stats
-from optimum.habana.checkpoint_utils import get_ds_injection_policy
-
-from transformers import (
-    AutoTokenizer,
-    AutoModel,
-    PreTrainedTokenizerBase,
-    AutoConfig,
-)
-from optimum.habana.checkpoint_utils import (
-    get_repo_root,
-    model_on_meta,
-    write_checkpoints_json,
-)
-
-from text_generation_server.utils.speculate import get_speculate
-from text_generation_server.models.types import (
-    Batch,
-    Tokens,
-    Generation,
-    GeneratedText,
-)
-from text_generation_server.utils.debug import dbg_trace
 
 tracer = trace.get_tracer(__name__)
 
 IMAGES = re.compile(r"!\[[^\]]*\]\((.*?)\s*(\"(?:.*[^\"])\")?\s*\)")
-BASE_IMAGE_TOKENS = int(os.environ.get('BASE_IMAGE_TOKENS', 2048))
-MAX_TOTAL_TOKENS = int(os.environ.get('MAX_TOTAL_TOKENS', 8192))
-MAX_BATCH_TOTAL_TOKENS = int(os.environ.get('MAX_BATCH_TOTAL_TOKENS', 131072))
-PAD_SEQUENCE_TO_MULTIPLE_OF = int(os.environ.get('PAD_SEQUENCE_TO_MULTIPLE_OF', 256))
-CHUNK_SIZES = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048]
-LAZY_MODE = int(os.environ.get('PT_HPU_LAZY_MODE', 1))
 
-PREFILL_WARMUP_BATCH_SIZE_LIST = []
-PREFILL_WARMUP_SEQLEN_LIST = []
-DECODE_WARMUP_BATCH_SIZE_LIST = []
-def round_up(warmup_list:list, num) :
-    i = 0
-    for i in warmup_list:
-        if num <= i :
-            break
-    return i
 
 def split(string) -> List[Dict[str, str]]:
     parts = []
@@ -105,6 +41,30 @@ def split(string) -> List[Dict[str, str]]:
 
     return parts
 
+
+def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
+    """
+    Calculate the shape of the image patch grid after the preprocessing for images of any resolution.
+
+    Args:
+        image_size (`tuple`):
+            The size of the input image in the format (width, height).
+        grid_pinpoints (`List`):
+            A list containing possible resolutions. Each item in the list should be a tuple or list
+            of the form `(height, width)`.
+        patch_size (`int`):
+            The size of each image patch.
+
+    Returns:
+        tuple: The shape of the image patch grid in the format (width, height).
+    """
+    if not isinstance(grid_pinpoints, list):
+        raise ValueError("grid_pinpoints should be a list of tuples or lists")
+
+    height, width = select_best_resolution(image_size, grid_pinpoints)
+    return height // patch_size, width // patch_size
+
+
 def image_text_replacement(image_input, config, image_id) -> str:
     if config.model_type == "idefics2":
         # TODO technically depends on image splitting which is not implemented.
@@ -117,7 +77,9 @@ def image_text_replacement(image_input, config, image_id) -> str:
     elif config.model_type == "llava_next":
         height, width = image_input["image_sizes"][image_id]
         num_features = get_number_of_features(height, width, config)
+        from loguru import logger
 
+        logger.info(f"Found {num_features} in image of resolution {height}x{width}")
         return "<image>" * num_features
 
     elif config.model_type == "paligemma":
@@ -163,7 +125,6 @@ def get_number_of_features(height: int, width: int, config) -> int:
         image_grid_pinpoints,
         image_size,
     )
-
     unpadded_features, newline_features = get_unpadded_features(
         height, width, npatches, num_patch_height, num_patch_width
     )
@@ -178,104 +139,31 @@ def load_data_uri(image_uri: str) -> Image.Image:
     image = Image.open(BytesIO(content))
     return image
 
-class VlmCausalLMBatch(CausalLMBatch):
+
+class VlmCausalLMBatch(FlashMistralBatch):
     pixel_values: Optional[List[torch.Tensor]]
     pixel_attention_mask: Optional[List[torch.Tensor]]
     image_sizes: Optional[List[Tuple[int, int]]]
 
     @classmethod
-    def from_tokenized(
-        cls,
-        pb: generate_pb2.Batch,
-        tokenizer: PreTrainedTokenizerBase,
-        batch_tokenized_inputs,
-        dtype: torch.dtype,
-        device: torch.device,
-        is_warmup: bool = False,
-    ) -> "VlmCausalLMBatch":
-        
-        dbg_trace('FROM_PB', f'num_reqs:{len(pb.requests)}')
-        requests = [CausalLMRequest.from_pb(idx, req, tokenizer) for idx, req in enumerate(pb.requests)]
+    @tracer.start_as_current_span("concatenate")
+    def concatenate(cls, batches):
+        batch = super(VlmCausalLMBatch, cls).concatenate(batches)
+        batch.pixel_values = None
+        batch.pixel_attention_mask = None
+        batch.image_sizes = None
+        return batch
 
-        max_input_length = max(r.data.truncate for r in requests)
-        max_new_tokens = max(r.stopping_criteria.max_new_tokens for r in requests)
-        # TODO: Add support for sparse batches
-        top_n_tokens = [r.top_n_tokens for r in pb.requests]
-        top_n_tokens_tensor = torch.tensor(top_n_tokens, device=device, dtype=torch.int64)
-
-        # TODO: by tokenizing all inputs at once we loose information on actual input lengths
-        # this means that we cannot shift inputs to the left after a long input sequence
-        # was filtered out
-        new_bs = round_up(PREFILL_WARMUP_BATCH_SIZE_LIST, len(requests))
-        parameters = [r.parameters for r in pb.requests]
-        # append the dummy parameters for dummy request
-        parameters = pad_next_token_chooser_parameters(parameters, new_bs)
-
-        next_token_chooser = HeterogeneousNextTokenChooser.from_pb(
-            pb=parameters,
-            dtype=dtype,
-            device=device,
-            tokenizer=tokenizer,
-            quantization_enabled=hq_env.is_quantization_enabled,
-        )
-        tokenized_inputs = batch_tokenized_inputs
-        input_len = tokenized_inputs["input_ids"].shape[1]
-
-        bucket_size = max_input_length
-        left_padding = max_input_length - input_len
-        if is_warmup is False:
-            if input_len < max_input_length :
-                rounded_seq_len = round_up(PREFILL_WARMUP_SEQLEN_LIST, input_len + 1)
-                if rounded_seq_len <= max_input_length:
-                    bucket_size = rounded_seq_len - 1
-                else:
-                    bucket_size = max_input_length - 1
-                left_padding = bucket_size - input_len
-
-        input_ids = tokenized_inputs["input_ids"]
-        attention_mask = tokenized_inputs["attention_mask"]
-        # Allocate space for first token
-        if left_padding > 0:
-            input_ids = torch.nn.functional.pad(
-                input_ids, (left_padding, 1), value=tokenizer.pad_token_id
-            )
-            attention_mask = torch.nn.functional.pad(
-                attention_mask, (left_padding, 1), value=0
-            )
-        all_input_ids = torch.nn.functional.pad(
-            input_ids, (0, max_new_tokens), value=tokenizer.pad_token_id
-        ).T.split(1, dim=1)
-
-        # New input length after left padding
-        input_len = bucket_size
-        for r in requests:
-            r.input_length = input_len
-            r.prefix_offset = input_len - 5
-            r.read_offset = input_len
-            r.all_input_ids = all_input_ids[r.idx]
-        input_ids = input_ids.to(device)
-        attention_mask = attention_mask.to(device)
-        position_ids = attention_mask.long().cumsum(-1) - 1
-        position_ids.masked_fill_(attention_mask == 0, 1)
-
-        htorch.core.mark_step()
-
-        return cls(
-            batch_id=pb.id,
-            requests=requests,
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=None,
-            merged_kv_cache=False,
-            next_token_chooser=next_token_chooser,
-            top_n_tokens=top_n_tokens,
-            top_n_tokens_tensor=top_n_tokens_tensor,
-            input_length=input_len,
-        )
+    @tracer.start_as_current_span("filter")
+    def filter(self, request_ids: List[int]):
+        batch = super().filter(request_ids)
+        batch.pixel_values = None
+        batch.pixel_attention_mask = None
+        batch.image_sizes = None
+        return batch
 
     @classmethod
-    def batch_tokenized_inputs(cls, requests, tokenizer, processor, config, is_warmup):
+    def batch_tokenized_inputs(cls, requests, tokenizer, processor, config):
         batch_inputs = []
         image_inputs = []
         max_truncation = 0
@@ -304,28 +192,16 @@ class VlmCausalLMBatch(CausalLMBatch):
                     image_inputs.append(image_input)
                 else:
                     raise RuntimeError(f"Invalid chunk type {chunk['type']}")
+
             batch_inputs.append(full_text)
             max_truncation = max(max_truncation, r.truncate)
 
-        if is_warmup is False:
-            new_bs = round_up(PREFILL_WARMUP_BATCH_SIZE_LIST, len(requests))
-            missing_inputs = new_bs - len(requests)
-            dummy_images = []
-            dummy_inputs = []
-            if len(batch_inputs) > 0 and len(image_inputs) > 0:
-                dummy_inputs = [batch_inputs[0]] * missing_inputs
-                dummy_images = [image_inputs[0]] * missing_inputs
-            image_inputs += dummy_images
-            batch_inputs += dummy_inputs
-
         batch_tokenized_inputs = tokenizer(
             batch_inputs,
             truncation=True,
             max_length=max_truncation,
-            return_tensors="pt",
-            padding="longest",
-            return_token_type_ids=False,
-        )
+            add_special_tokens=not config.model_type == "paligemma",
+        )["input_ids"]
         if image_inputs:
             image_input = image_inputs[0]
             new_image_inputs = {
@@ -355,10 +231,9 @@ class VlmCausalLMBatch(CausalLMBatch):
         config,
         dtype: torch.dtype,
         device: torch.device,
-        is_warmup: bool = False,
     ) -> "VlmCausalLMBatch":
         batch_tokenized_inputs, image_inputs = cls.batch_tokenized_inputs(
-            pb.requests, tokenizer, processor, config, is_warmup
+            pb.requests, tokenizer, processor, config
         )
         batch = cls.from_tokenized(pb, tokenizer, batch_tokenized_inputs, dtype, device)
         if image_inputs is not None:
@@ -379,836 +254,127 @@ class VlmCausalLMBatch(CausalLMBatch):
             batch.image_sizes = None
         return batch
 
-    @classmethod
-    @tracer.start_as_current_span("concatenate")
-    def concatenate(cls, batches: List["CausalLMBatch"], pad_token_id: int = 0, is_warmup:bool = False) -> "CausalLMBatch":
-        return cls.recombine(batches, pad_token_id, is_warmup)
-
-
-
-    @classmethod
-    def recombine(cls, batches: List["VlmCausalLMBatch"], pad_token_id: int, is_warmup: bool =False) -> "VlmCausalLMBatch":
-        if not all(b.past_key_values is not None for b in batches):
-            raise ValueError("KV cache not allocated! Cannot recombine before prefill!")
-
-        total_requests = sum(len(b) for b in batches)
-        new_bs = total_requests
-        if is_warmup is False :
-            new_bs = round_up(DECODE_WARMUP_BATCH_SIZE_LIST, total_requests)
-        batch_id = batches[0].batch_id
-        device = batches[0].input_ids.device
-
-        input_lengths = [b.input_length for b in batches]
-        max_input_length = max(input_lengths)
-        offsets = [max_input_length - b.input_length for b in batches]
-
-        cur_padding = [b.right_padding for b in batches]
-        # For prefill there is a space allocated only for first token
-        # Need to add padding to the max total tokens before first decode
-
-        moves_needed = [total_requests - len(b) if b.batch_size == new_bs else total_requests for b in batches]
-        dst_batch_idx = min(enumerate(moves_needed), key=lambda idx_val: idx_val[1])[0]
-        reshape = (batches[dst_batch_idx].batch_size < new_bs)
-
-        # TODO: Add support for changing max seq len, i.e. due to output length bucketing
-        # FIXME: max_seq_len for non optimized code
-        if len(batches) > 1:
-            scenario = 'CONCAT'
-        elif reshape:
-            scenario = 'RESHAPE'
-        elif cur_padding[dst_batch_idx] <= 0:
-            scenario = 'SHIFT'
-            offsets = [biggest_single_chunk(b.max_input_length - max_input_length) for b in batches]
-            max_input_length = max_input_length + offsets[dst_batch_idx]
-        else:
-            # Nothing to do
-            return batches[0]
-
-        dbg_trace(
-            scenario, f'bs:{[b.batch_size for b in batches]}->{new_bs}'
-                      f' reqs:{[len(b) for b in batches]}'
-                      f' offsets:{offsets}'
-                      f' input_lengths:{input_lengths}'
-                      f' cur_padding:{cur_padding}'
-                      f' dst_batch:{dst_batch_idx}')
-
-        grouped_requests = [[req for req in batch.requests] for batch in batches]
-        flat_requests = list(itertools.chain(*grouped_requests))
-
-        for i in range(len(batches)):
-            target_bs = new_bs if i == dst_batch_idx else batches[i].batch_size
-            batches[i].merge_kv_cache_if_needed(target_bs, offsets[i])
-            batches[i].realign(target_bs, offsets[i], pad_token_id)
-            batches[i].split_kv_cache_if_needed(i == dst_batch_idx)
-        batches[dst_batch_idx].expand_bs(new_bs)
-        batches[dst_batch_idx].move_data([batches[i] for i in range(len(batches)) if i != dst_batch_idx])
-
-        top_n_tokens = [r.data.top_n_tokens for r in flat_requests]
-        top_n_tokens_tensor = torch.tensor(top_n_tokens, device=device, dtype=torch.int64)
-
-        parameters = [r.data.parameters for r in flat_requests]
-        # append the dummy parameters for dummy requests
-        batch_size = batches[dst_batch_idx].batch_size
-        parameters = pad_next_token_chooser_parameters(parameters, batch_size)
-
-        # update past grammar states
-        fsm_grammar_states = [0] * batch_size
-        for batch in batches:
-            for i, req in enumerate(batch.requests):
-                fsm_grammar_states[req.idx] = batch.next_token_chooser.fsm_grammar_states[i]
-
-        next_token_chooser = HeterogeneousNextTokenChooser.from_pb(
-            parameters,
-            batches[dst_batch_idx].next_token_chooser.dtype,
-            batches[dst_batch_idx].next_token_chooser.device,
-            batches[dst_batch_idx].next_token_chooser.tokenizer,
-            fsm_grammar_states,
-            quantization_enabled=hq_env.is_quantization_enabled,
-        )
-
-        input_ids = batches[dst_batch_idx].input_ids
-        attention_mask = batches[dst_batch_idx].attention_mask
-        position_ids = batches[dst_batch_idx].position_ids
-        past_key_values = batches[dst_batch_idx].past_key_values
-        input_length = max_input_length
-
-        htorch.core.mark_step()
-
-        return cls(
-            batch_id=batch_id,
-            requests=flat_requests,
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            merged_kv_cache=False,
-            next_token_chooser=next_token_chooser,
-            top_n_tokens=top_n_tokens,
-            top_n_tokens_tensor=top_n_tokens_tensor,
-            input_length=input_length,
-        )
-
-class VlmCausalLM(Model):
-    def __init__(
-        self,
-        model_class,
-        model_id: str,
-        *,
-        processor_class=AutoProcessor,
-        processor_kwargs=None,
-        batch_class=VlmCausalLMBatch,
-        revision,
-        dtype,
-        trust_remote_code: bool,
-        **kwargs,
-    ):
-        adapt_transformers_to_gaudi()
-        if processor_kwargs is None:
-            processor_kwargs = {}
-        self.processor = processor_class.from_pretrained(
-            model_id,
-            revision=revision,
-            trust_remote_code=trust_remote_code,
-            **processor_kwargs,
-        )
-        self.batch_class = batch_class
-        self.prev_bs = 0
-
-        # Create tokenizer
-        tokenizer = AutoTokenizer.from_pretrained(
-            model_id,
-            revision=revision,
-            padding_side="left",
-            truncation_side="left",
-            trust_remote_code=trust_remote_code,
-        )
-
-        # Create model
-        world_size = int(os.getenv("WORLD_SIZE", "1"))
-        rank = int(os.getenv("RANK", "0"))
-        dtype = torch.bfloat16 if dtype is None else dtype
-        device = torch.device("hpu")
-
-        if hq_env.is_quantization_enabled:
-            htorch.core.hpu_set_env()
-
-        if world_size > 1:
-            model = self.get_deepspeed_model(
-                model_class, model_id, dtype, revision
-            )
-            model = self.prepare_model_for_quantization(model)
-        else:
-            get_repo_root(model_id)
-
-            # Check support for rope scaling
-            model_kwargs = {}
-            config = AutoConfig.from_pretrained(
-                model_id
-            )
-            if hasattr(config, "rope_scaling"):
-                model_kwargs["rope_scaling"] = self.get_rope_scaling()
-
-            model = model_class.from_pretrained(
-                model_id,
-                revision=revision,
-                torch_dtype=dtype,
-                trust_remote_code=trust_remote_code,
-                **model_kwargs
-            )
-            model = self.prepare_model_for_quantization(model)
-            model = model.eval().to(device)
-
-        self.enable_hpu_graph = os.getenv("ENABLE_HPU_GRAPH", "true").lower() == "true" and LAZY_MODE == 1
-        self.limit_hpu_graph = os.getenv("LIMIT_HPU_GRAPH", "false").lower() == "true"
-        model = remove_kv_cache_from_output(model)
-        if self.enable_hpu_graph:
-            from habana_frameworks.torch.hpu import wrap_in_hpu_graph
-            model = wrap_in_hpu_graph(model, disable_tensor_cache=True)
-        else:
-            if LAZY_MODE == 0: 
-                # It is said that "keep_input_mutations" is safe for inference to be done
-                dbg_trace(
-                    "TORCH COMPILE", f'Torch compiling of model')
-                model.model = torch.compile(model.model, backend="hpu_backend", options={"keep_input_mutations": True})
-
-        model = self.setup_quantization(model)
-
-        if model.config.model_type not in MODELS_OPTIMIZED_WITH_STATIC_SHAPES:
-            raise ValueError(f"Model type {model.config.model_type} is not supported!")
-
-        if tokenizer.pad_token_id is None:
-            if model.config.pad_token_id is not None:
-                tokenizer.pad_token_id = model.config.pad_token_id
-            elif model.config.eos_token_id is not None:
-                tokenizer.pad_token_id = model.config.eos_token_id
-            elif tokenizer.eos_token_id is not None:
-                tokenizer.pad_token_id = tokenizer.eos_token_id
-            else:
-                tokenizer.add_special_tokens({"pad_token": "[PAD]"})
-
-        kwargs = {
-            "use_cache": True,
-            "return_dict": True,
-        }
-
-        if model.config.model_type in ["llama", "mistral", "llava_next"]:
-            kwargs["attn_softmax_bf16"] = True
-            kwargs["trim_logits"] = True
-
-            if os.getenv("USE_FLASH_ATTENTION", "false").lower() == "true":
-                kwargs["use_flash_attention"] = True
-            if os.getenv("FLASH_ATTENTION_RECOMPUTE", "false").lower() == "true":
-                kwargs["flash_attention_recompute"] = True
-
-        self.speculate = get_speculate()
-        super(VlmCausalLM, self).__init__(
-            model=model,
-            tokenizer=tokenizer,
-            requires_padding=True,
-            dtype=dtype,
-            device=device,
-            rank=rank,
-            kwargs=kwargs,
-        )
-
-        # Create profiler
-        ranks_to_profile = [int(val) for val in os.getenv("PROF_RANKS", "0").split(',')]
-        record_shapes = os.getenv("PROF_RECORD_SHAPES", "false").lower() == "true"
-        output_dir = os.getenv("PROF_PATH", "/tmp/hpu_profile")
-        self.profiling_warmup_steps = int(os.getenv("PROF_WARMUPSTEP", "0")) if rank in ranks_to_profile else 0
-        self.profiling_steps = int(os.getenv("PROF_STEP", "0")) if rank in ranks_to_profile else 0
-        self.profiling_wait_steps = int(os.getenv("PROF_WAITSTEP", "0"))
-        if self.profiling_steps > 0:
-            self.hb_profiler = HabanaProfile(
-                wait=self.profiling_wait_steps,
-                warmup=self.profiling_warmup_steps,
-                active=self.profiling_steps,
-                output_dir=output_dir,
-                record_shapes=record_shapes
-            )
-            self.hb_profiler.start()
-        else:
-            self.hb_profiler = None
-        self.step = 0
-
 
+class VlmCausalLM(BaseFlashMistral):
     @property
     def batch_type(self) -> Type[VlmCausalLMBatch]:
-        return self.batch_class
-
-    def max_past(self) -> Optional[int]:
-        return getattr(self.model.text_model, "max_past", None)
-
-    def get_deepspeed_model(
-        self,
-        model_class,
-        model_id: str,
-        dtype: torch.dtype,
-        revision: Optional[str] = None
-    ) -> torch.nn.Module:
-        import deepspeed
-        from habana_frameworks.torch.distributed.hccl import initialize_distributed_hpu
-
-        world_size, rank, local_rank = initialize_distributed_hpu()
-        model_kwargs = {
-            "revision": revision
-        }
-
-        # Initialize process(es) for DeepSpeed
-        deepspeed.init_distributed(dist_backend="hccl")
-        logger.info(
-            "DeepSpeed is enabled. world_size {} rank {} local_rank {}".format(world_size, rank, local_rank)
-        )
-        config = AutoConfig.from_pretrained(model_id, **model_kwargs)
-        load_to_meta = model_on_meta(config)
-
-        # Check support for rope scaling
-        if hasattr(config, "rope_scaling"):
-            config.rope_scaling = self.get_rope_scaling()
-            model_kwargs["rope_scaling"] = self.get_rope_scaling()
-
-        if load_to_meta:
-            # Construct model with fake meta tensors, later will be replaced on devices during ds-inference ckpt load
-            with deepspeed.OnDevice(dtype=dtype, device="meta"):
-                model = model_class.from_config(config, torch_dtype=dtype)
-        else:
-            get_repo_root(model_id, local_rank=os.getenv("LOCAL_RANK"))
-            # TODO: revisit placement on CPU when auto-injection is possible
-            with deepspeed.OnDevice(dtype=dtype, device="cpu"):
-                model = model_class.from_pretrained(model_id, torch_dtype=dtype, **model_kwargs)
-        model = model.eval()
-
-        # Initialize the model
-        ds_inference_kwargs = {"dtype": dtype}
-        ds_inference_kwargs["tensor_parallel"] = {"tp_size": world_size}
-        ds_inference_kwargs["enable_cuda_graph"] = False
-        ds_inference_kwargs["injection_policy"] = get_ds_injection_policy(model.language_model.config)
-
-        if load_to_meta:
-            # model loaded to meta is managed differently
-            checkpoints_json = tempfile.NamedTemporaryFile(suffix=".json", mode="+w")
-            write_checkpoints_json(model_id, local_rank, checkpoints_json)
-            ds_inference_kwargs["checkpoint"] = checkpoints_json.name
-        model = deepspeed.init_inference(model, **ds_inference_kwargs)
-
-        return model.module
-
-    def get_rope_scaling(self) -> Optional[Dict]:
-        rope_scaling = os.getenv("ROPE_SCALING", None)
-        if rope_scaling is None:
-            return None
-
-        rope_factor = float(os.getenv("ROPE_FACTOR", 1.0))
-        return {
-            'type': rope_scaling, 'factor': float(rope_factor)
-        }
-
-    def setup_quantization(self, model):
-        if hq_env.is_quantization_enabled:
-            htorch.core.quantization._mark_params_as_const(model)
-            htorch.core.quantization._check_params_as_const(model)
-            htorch.core.hpu_initialize(model)
-        return model
-
-    def prepare_model_for_quantization(self, model):
-        if hq_env.is_quantization_enabled:
-            if model.config.model_type == "llama":
-                self.patch_scoped_linear_all_reduce(model)
-            import habana_quantization_toolkit
-            habana_quantization_toolkit.prep_model(model)
-        return model
-
-    def finish_quantization_measurements(self, model):
-        if hq_env.is_quantization_enabled:
-            import habana_quantization_toolkit
-            habana_quantization_toolkit.finish_measurements(self.model)
-        return model
-
-    def patch_scoped_linear_all_reduce(self, model):
-        from deepspeed.module_inject.layers import LinearAllreduce
-        from optimum.habana.transformers.models.modeling_all_models import ScopedLinearAllReduce
-        for name, module in model.named_children():
-            if type(module) is LinearAllreduce:
-                SL = ScopedLinearAllReduce(mod=module)
-                setattr(model, name, SL)
-            self.patch_scoped_linear_all_reduce(module)
-
-    def decode(self, generated_ids: List[int]) -> str:
-        return self.tokenizer.decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
-
-    def decode_token(
-        self,
-        all_input_ids: List[int],
-        prefix_offset: int = 0,
-        read_offset: int = 0,
-    ) -> Tuple[str, int, int]:
-        if is_tokenizer_transparent(self.tokenizer):
-            new_text = self.tokenizer.decode(all_input_ids[read_offset:], skip_special_tokens=False)
-            return new_text, read_offset, len(all_input_ids)
-        else:
-            return super().decode_token(all_input_ids, prefix_offset, read_offset)
+        return VlmCausalLMBatch
 
     def forward(
-        self,
-        input_ids,
-        attention_mask,
-        position_ids,
-        token_idx,
-        past_key_values: Optional[List[Tuple]] = None,
-        pixel_values: Optional[List[torch.Tensor]] = None,
-        image_sizes: Optional[List[Tuple[int, int]]] = None,
-        bypass_hpu_graph: Optional[bool] = None,
-    ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
+        self, batch: VlmCausalLMBatch
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
         # Model Forward
-        kwargs = {
-            "input_ids": input_ids,
-            "attention_mask": attention_mask,
-            "past_key_values": past_key_values,
-            "token_idx": token_idx,
-            "pixel_values": pixel_values,
-            "image_sizes": image_sizes,
-        }
+        if batch.speculative_ids is not None:
+            input_ids = batch.input_ids
+            position_ids = batch.position_ids
+            cu_seqlen_prefill = batch.cu_seqlen_prefill
+            kv_cache = get_cache_manager().kv_cache
+            block_tables = batch.block_tables_tensor
+            slots = batch.slots[batch.slot_indices]
+            input_lengths = batch.input_lengths_tensor
+            max_s = batch.max_seqlen
+            lm_head_indices = batch.prefill_head_indices
 
-        hpu_kwargs = {}
-        # Optimum Habana got "lazy_mode" key-val only supported for llama type of models
-        if self.model.config.model_type == "llama" :
-            hpu_kwargs["lazy_mode"] = LAZY_MODE == 1
+            speculative_ids = batch.speculative_ids
 
-        if self.has_position_ids:
-            kwargs["position_ids"] = position_ids
+            B, speculative_length = speculative_ids.shape
+            new_length = speculative_length + 1
+            new_input_ids = torch.cat(
+                [input_ids.unsqueeze(-1), speculative_ids], dim=1
+            ).reshape(-1)
+            arange = torch.arange(new_length, device=position_ids.device).unsqueeze(0)
+            arange_int = arange.to(dtype=torch.int32)
+            new_position_ids = (
+                position_ids.unsqueeze(-1).expand(B, new_length) + arange
+            ).view(-1)
+            slots = (slots.unsqueeze(-1).expand(B, new_length) + arange_int).view(-1)
+            input_lengths = (
+                input_lengths.unsqueeze(-1).expand(B, new_length) + arange_int
+            ).view(-1)
 
-        if bypass_hpu_graph != None:
-            hpu_kwargs["bypass_hpu_graphs"] = bypass_hpu_graph
+            # Add Copy the block tables for all members
+            block_tables = (
+                block_tables.unsqueeze(1)
+                .expand(B, new_length, -1)
+                .reshape(B * new_length, -1)
+                .contiguous()
+            )
+            max_s = max_s + speculative_length
 
-        kwargs.update(self.kwargs)
-        model_inputs = self.model.prepare_inputs_for_generation(**kwargs)
-        if past_key_values is not None:
-            return self.model.forward(**model_inputs, **hpu_kwargs)
+            input_ids = new_input_ids
+            position_ids = new_position_ids
         else:
-            outputs = self.model.forward(**model_inputs, **hpu_kwargs)
-            return outputs.logits, outputs.past_key_values
+            input_ids = batch.input_ids
+            position_ids = batch.position_ids
+            cu_seqlen_prefill = batch.cu_seqlen_prefill
+            kv_cache = get_cache_manager().kv_cache
+            block_tables = batch.block_tables_tensor
+            slots = batch.slots[batch.slot_indices]
+            input_lengths = batch.input_lengths_tensor
+            max_s = batch.max_seqlen
+            lm_head_indices = batch.prefill_head_indices
 
-    @tracer.start_as_current_span("generate_token")
-    def generate_token(
-        self, batches: List[VlmCausalLMBatch], is_warmup: bool = False
-    ) -> Tuple[List[Generation], Optional[CausalLMBatch], Tuple[int, int]]:
-        start = time.time_ns()
-        # Results
-        generations: List[Generation] = []
-        prev_batches = []
-        requests_to_generate = []
-        # In order to pipeline any actions on CPU we perform the operation in 3 main stages:
-        # Stage 1. Collect next token ids of any previously started generations
-        for batch_id, batch in enumerate(batches):
-            if batch.logits is not None:
-                logits = batch.logits
-                past = batch.past
-                prefill = batch.past_key_values is None
-                if prefill:
-                    # no right padding for prefill
-                    token_idx_scalar = batch.attention_mask.shape[-1] - 1
-                    token_idx = torch.tensor(token_idx_scalar).to(self.device)
-                else:
-                    token_idx_scalar = batch.attention_mask.shape[-1] - batch.right_padding
-                    token_idx = torch.tensor(token_idx_scalar).to(self.device)
+        if cu_seqlen_prefill is None and self.max_past() is not None:
+            # In decode, not prefill, we're actually overwriting the KV-cache
+            # in a circular buffer mode.
+            # This makes sure the max_s for the decode pass is correct.
+            max_s = min(self.max_past(), max_s)
 
-                # Select next token
-                input_length = batch.input_length
-                if logits.shape[-2] > 1:
-                    next_token_ids, next_token_logprobs, logprobs, _, _ = batch.next_token_chooser(
-                        batch.input_ids, logits[:, input_length - 1: input_length, :].squeeze(-2), self.speculate
-                    )
-                else:
-                    next_token_ids, next_token_logprobs, logprobs, _, _ = batch.next_token_chooser(
-                        batch.input_ids, logits.squeeze(-2), self.speculate
-                    )
-                # Speculation is not active for causal
-                accepted_ids = torch.ones_like(batch.input_ids)[:, 0]
-                batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
-                    batch.top_n_tokens,
-                    batch.top_n_tokens_tensor,
-                    logprobs,
-                    accepted_ids,
-                )
-
-                prev_batches.append({
-                    'next_token_ids': next_token_ids,
-                    'next_token_logprobs': next_token_logprobs,
-                })
-
-                for req_idx, req in enumerate(batch.requests):
-                    requests_to_generate.append({
-                        'req': req,
-                        'prev_req_idx': req.idx,
-                        'batch_id': batch_id,
-                        'seed': batch.next_token_chooser.seeds[req_idx],
-                        'do_sample': batch.next_token_chooser.do_sample[req_idx],
-                        'top_n_tokens': batch.top_n_tokens[req_idx],
-                        'top_token_ids': batch_top_token_ids[req_idx],
-                        'top_token_logprobs': batch_top_token_logprobs[req_idx],
-                        'grammar_state': batch.next_token_chooser.fsm_grammar_states[req.idx],
-                    })
-
-                htorch.core.mark_step()
-
-                # Add new token into input_ids
-                batch.input_ids.index_copy_(1, token_idx, next_token_ids.unsqueeze(1))
-
-                # Update attention_mask as we added a new token to input_ids
-                batch.attention_mask.index_fill_(1, token_idx, 1)
-
-                # Adjust lengths
-                batch.input_length += 1
-
-                # Update position_ids
-                if prefill:
-                    batch.position_ids = torch.index_select(batch.position_ids, 1, token_idx - 1) + 1
-                else:
-                    batch.position_ids += 1
-                # Update past key values
-                if prefill:
-                    batch.past_key_values = past
-
-        htorch.core.mark_step()
-
-        # Stage 2. Prepare new batch for speculative scheduling
-        if len(batches) > 1:
-            batch = self.batch_type.concatenate(batches, self.tokenizer.pad_token_id, is_warmup)
+        bs = input_ids.shape[0]
+        # Try to find an associated cuda graph
+        bs = input_ids.shape[0]
+        sorted_padded_bs = sorted([k for k in self.cuda_graphs.keys() if k >= bs])
+        if sorted_padded_bs:
+            # Get associated cuda graph
+            cuda_graph = self.cuda_graphs[sorted_padded_bs[0]]
         else:
-            batch = batches[0]
-
-        prefill = batch.past_key_values is None
-
-        # Check if we need to do any bookkeeping first
-        if not prefill:
-            batch = batch.__class__.recombine([batch], self.tokenizer.pad_token_id, is_warmup)
-
-        scenario = 'PREFILL' if prefill else 'GENERATE'
-        if self.enable_hpu_graph and self.limit_hpu_graph and round_up(DECODE_WARMUP_BATCH_SIZE_LIST, batch.batch_size) != self.prev_bs:
-            self.model.clear_cache()
-            self.prev_bs = round_up(DECODE_WARMUP_BATCH_SIZE_LIST, batch.batch_size)
-        dbg_trace(
-            scenario, f'bs:{batch.batch_size} num_reqs:{len(batch.requests)} seq_len:{batch.seq_length} padding:{batch.right_padding}')
-        #assert batch.right_padding > 0, 'No more room for next token!'
-
-        # Execute batch
-        if prefill:
-            # no right padding for prefill
-            token_idx = torch.tensor(batch.attention_mask.shape[-1] - 1).to(self.device)
-            batch.logits, batch.past = self.forward(
-                batch.input_ids,
-                batch.attention_mask,
-                batch.position_ids,
-                token_idx,
-                batch.past_key_values,
-                batch.pixel_values,
-                batch.image_sizes,
-                bypass_hpu_graph=prefill and self.limit_hpu_graph if self.enable_hpu_graph else None,
-            )
-        elif all([req.stopping_criteria.max_new_tokens == 1 for req in batch.requests]):
-            # Don't schedule next forward if max_new_tokens for all requests equals 1 
-            # - we've already generated the first and only needed token in the prefill phase
-            pass
-        else:
-            token_idx = torch.tensor(batch.attention_mask.shape[-1] - batch.right_padding).to(self.device)
-            batch.logits = self.forward(
-                batch.input_ids,
-                batch.attention_mask,
-                batch.position_ids,
-                token_idx,
-                batch.past_key_values,
-                bypass_hpu_graph=prefill and self.limit_hpu_graph if self.enable_hpu_graph else None,
+            cuda_graph = None
+        if cu_seqlen_prefill is not None or cuda_graph is None:
+            logits, speculative_logits = self.model.forward(
+                input_ids=input_ids,
+                position_ids=position_ids,
+                cu_seqlen_prefill=cu_seqlen_prefill,
+                kv_cache=kv_cache,
+                block_tables=block_tables,
+                slots=slots,
+                input_lengths=input_lengths,
+                max_s=max_s,
+                prefill_cache_indices=batch.prefill_cache_indices,
+                lm_head_indices=lm_head_indices,
+                pixel_values=batch.pixel_values,
+                pixel_attention_mask=batch.pixel_attention_mask,
+                image_sizes=batch.image_sizes,
             )
+            if batch.prefill_cache_indices is not None:
+                batch.prefill_cache_indices = None
+            if batch.pixel_values is not None:
+                batch.pixel_values = None
+            if batch.pixel_attention_mask is not None:
+                batch.pixel_attention_mask = None
+            if batch.image_sizes is not None:
+                batch.image_sizes = None
+            return logits, speculative_logits
 
-        htorch.core.mark_step()
+        # Copy inputs to the static inputs of the cuda graph
+        # Static inputs are potentially padded
+        cuda_graph["input_ids"][: input_ids.shape[0]] = input_ids
+        cuda_graph["position_ids"][: position_ids.shape[0]] = position_ids
+        cuda_graph["block_tables"][
+            : block_tables.shape[0], : block_tables.shape[1]
+        ] = block_tables
+        cuda_graph["slots"].fill_(-1)
+        cuda_graph["slots"][: slots.shape[0]] = slots
+        cuda_graph["input_lengths"].zero_()
+        cuda_graph["input_lengths"][: input_lengths.shape[0]] = input_lengths
 
-        start_decode = time.time_ns()
+        # Replay the graph
+        cuda_graph["graph"].replay()
 
-        # Stage 3. Finish and return previous generations
-        stopped = len(requests_to_generate) > 0
-        for prev_batch in prev_batches:
-            prev_batch['next_token_logprobs'] = prev_batch['next_token_logprobs'].tolist()
-            prev_batch['next_token_ids_cpu'] = prev_batch['next_token_ids'].cpu()
-        htorch.core.mark_step()
-
-        for req_data in requests_to_generate:
-            req = req_data['req']
-            i = req_data['prev_req_idx']
-            prev_batch_id = req_data['batch_id']
-            assert len(prev_batches) > prev_batch_id
-            next_token_ids_cpu = prev_batches[prev_batch_id]['next_token_ids_cpu']
-            next_token_logprobs = prev_batches[prev_batch_id]['next_token_logprobs']
-
-            request = req.data
-            input_length = req.input_length
-            prefix_offset = req.prefix_offset
-            read_offset = req.read_offset
-            do_sample = req_data['do_sample']
-            seed = req_data['seed']
-            stopping_criteria = req.stopping_criteria
-            all_input_ids = req.all_input_ids
-            next_token_id = next_token_ids_cpu[i]
-            next_token_logprob = next_token_logprobs[i]
-            top_n_tokens = req_data['top_n_tokens']
-            top_token_ids = req_data['top_token_ids']
-            top_token_logprobs = req_data['top_token_logprobs']
-            grammar_state = req_data['grammar_state']
-
-            # Append next token to all tokens
-            all_input_ids[input_length] = next_token_id
-            new_input_length = input_length + 1
-
-            # Generated token
-            if is_tokenizer_transparent(self.tokenizer) and len(stopping_criteria.stop_sequence_criterias) == 0:
-                next_token_text = ''
-            else:
-                next_token_text, prefix_offset, read_offset = self.decode_token(
-                    all_input_ids[0:new_input_length, 0], prefix_offset, read_offset
-                )
-
-            # Evaluate stopping criteria
-            stop, reason = stopping_criteria(
-                next_token_id,
-                next_token_text,
-            )
-
-            if not stop:
-                stopped = False
-
-            # Shard generations
-            # All generations will be appended in the rust sharded client
-            if i % self.world_size == self.rank:
-                if stop:
-                    # Decode generated tokens
-                    if is_tokenizer_transparent(self.tokenizer):
-                        output_text = None
-                    else:
-                        output_text = self.decode(
-                            all_input_ids[new_input_length - stopping_criteria.current_tokens: new_input_length, 0]
-                        )
-                    generated_text = GeneratedText(
-                        output_text,
-                        stopping_criteria.current_tokens,
-                        reason,
-                        seed if do_sample else None,
-                    )
-                else:
-                    generated_text = None
-
-                # Prefill
-                if stopping_criteria.current_tokens == 1 and request.prefill_logprobs:
-                    # Remove generated token to only have prefill and add nan for first prompt token
-                    prefill_logprobs = [float("nan")] + next_token_logprobs
-                    prefill_token_ids = all_input_ids[0: new_input_length - 1]
-                    prefill_texts = self.tokenizer.batch_decode(
-                        prefill_token_ids,
-                        clean_up_tokenization_spaces=False,
-                        skip_special_tokens=False,
-                    )
-                    prefill_tokens = Tokens(
-                        prefill_token_ids,
-                        prefill_logprobs,
-                        prefill_texts,
-                        is_special=[],
-                    )
-                else:
-                    prefill_tokens = None
-
-                if top_n_tokens > 0:
-                    all_top_tokens = []
-                    for top_token_ids, top_token_logprobs in zip(
-                        top_token_ids, top_token_logprobs
-                    ):
-                        toptoken_texts = self.tokenizer.batch_decode(
-                            top_token_ids,
-                            clean_up_tokenization_spaces=False,
-                            skip_special_tokens=False,
-                        )
-                        special_toptokens = [
-                            token_id in self.all_special_ids
-                            for token_id in top_token_ids
-                        ]
-                        top_tokens = Tokens(
-                            top_token_ids,
-                            top_token_logprobs,
-                            toptoken_texts,
-                            special_toptokens,
-                        )
-                        all_top_tokens.append(top_tokens)
-                    top_tokens = all_top_tokens
-                else:
-                    top_tokens = None
-
-                generation = Generation(
-                    request.id,
-                    prefill_tokens,
-                    Tokens(
-                        [next_token_id],
-                        [next_token_logprob],
-                        [next_token_text],
-                        [next_token_id in self.all_special_ids],
-                    ),
-                    generated_text,
-                    top_tokens,
-                )
-
-                generations.append(generation)
-
-            batch.next_token_chooser = (
-                batch.next_token_chooser.advance_grammar_single_with_past_state(
-                    req.idx, next_token_id, grammar_state
-                )
-            )
-
-            req.all_input_ids = all_input_ids
-            req.input_length = new_input_length
-            req.prefix_offset = prefix_offset
-            req.read_offset = read_offset
-
-        htorch.core.mark_step()
-        self.step = self.step + 1
-        if self.hb_profiler is not None:
-            if self.step > self.profiling_wait_steps + self.profiling_warmup_steps + self.profiling_steps:
-                self.hb_profiler.stop()
-            else:
-                self.hb_profiler.step()
-
-        forward_ns = start_decode - start
-        decode_ns = time.time_ns() - start_decode
-        return generations, batch if not stopped else None, (forward_ns, decode_ns)
-
-    def batch_from_pb(self, batch, is_warmup):
-        return VlmCausalLMBatch.from_pb_processor(
-            batch,
-            self.tokenizer,
-            self.processor,
-            self.model.config,
-            self.dtype,
-            self.device,
-            is_warmup
+        # Slice output to the correct shape
+        speculative_logits = (
+            cuda_graph["speculative_logits"][:bs]
+            if cuda_graph["speculative_logits"] is not None
+            else None
         )
-
-    def generate_warmup_batch(self, request, seq_len, batch_size, is_warmup):
-        batch = copy.deepcopy(request.batches[0])
-        for req in batch.requests:
-            req.truncate = seq_len
-
-        for i in range(len(batch.requests) - batch_size):
-            batch.requests.pop()
-
-        return self.batch_from_pb(batch, is_warmup)
-
-    def warmup(self, request) -> None:
-        is_warmup = True
-        batches = [self.batch_from_pb(batch, is_warmup) for batch in request.batches]
-
-        try:
-            # max prefill batch size warmup
-            _, prefill_batch, _ = self.generate_token([batches[0]], is_warmup)
-        except:
-            raise RuntimeError(
-                f"Not enough memory to handle {len(batches[0].input_ids)} prefill tokens. "
-                f"You need to decrease `--max-batch-prefill-tokens`"
-            )
-
-        global BASE_IMAGE_TOKENS, MAX_TOTAL_TOKENS, MAX_BATCH_TOTAL_TOKENS, PREFILL_WARMUP_BATCH_SIZE_LIST, PREFILL_WARMUP_SEQLEN_LIST, DECODE_WARMUP_BATCH_SIZE_LIST
-        max_input_length =  batches[0].input_ids.shape[1]
-        max_prefill_batch_size = batches[0].input_ids.shape[0]
-        PREFILL_WARMUP_BATCH_SIZE_LIST = []
-        batch_size = 1
-        while batch_size <= max_prefill_batch_size:
-            PREFILL_WARMUP_BATCH_SIZE_LIST.append(batch_size)
-            batch_size = batch_size * 2
-        if PREFILL_WARMUP_BATCH_SIZE_LIST[-1] < max_prefill_batch_size :
-            PREFILL_WARMUP_BATCH_SIZE_LIST.append(max_prefill_batch_size)
-
-        seq_len = BASE_IMAGE_TOKENS
-        PREFILL_WARMUP_SEQLEN_LIST = []
-        i = 0
-        while seq_len <= max_input_length:
-            PREFILL_WARMUP_SEQLEN_LIST.append(seq_len)
-            seq_len += PAD_SEQUENCE_TO_MULTIPLE_OF*(2**i)
-            i += 1
-        if PREFILL_WARMUP_SEQLEN_LIST[-1] < max_input_length:
-            PREFILL_WARMUP_SEQLEN_LIST.append(max_input_length)
-
-        #Prefill and decode warmup
-        DECODE_WARMUP_BATCH_SIZE_LIST = []
-        prefill_batch = None
-        decode_batch = None
-        try:
-            for batch_size in PREFILL_WARMUP_BATCH_SIZE_LIST :
-                for seq_len in PREFILL_WARMUP_SEQLEN_LIST :
-                    batch = self.generate_warmup_batch(request, seq_len, batch_size, is_warmup)
-                    _, prefill_batch, _ = self.generate_token([batch], is_warmup)
-                    _, decode_batch, _ = self.generate_token([prefill_batch], is_warmup)
-
-                DECODE_WARMUP_BATCH_SIZE_LIST.append(batch_size)
-
-        except:
-            raise RuntimeError(
-                f"Not enough memory to handle following prefill and decode warmup."
-                f"Prefill batch size list:{PREFILL_WARMUP_BATCH_SIZE_LIST}"
-                f"Prefill sequence length list:{PREFILL_WARMUP_SEQLEN_LIST}"
-                f"Decode batch size list:{DECODE_WARMUP_BATCH_SIZE_LIST}"
-                f"You need to decrease `--max-batch-prefill-tokens`"
-            )
-
-        mem_stats = get_hpu_memory_stats(self.device)
-        logger.info(
-                f"\nFollowing prefill and decode warmup successfully.\n"
-                f"Prefill batch size list:{PREFILL_WARMUP_BATCH_SIZE_LIST}\n"
-                f"Prefill sequence length list:{PREFILL_WARMUP_SEQLEN_LIST}\n"
-                f"Decode batch size list:{DECODE_WARMUP_BATCH_SIZE_LIST}\n"
-                f"Memory stats: {mem_stats} "
-            )
-
-        max_decode_batch_size = math.floor(MAX_BATCH_TOTAL_TOKENS / MAX_TOTAL_TOKENS)
-        batch_size = max_prefill_batch_size * 2
-        # Decode warmup with bigger batch_size
-        try:
-            if DECODE_WARMUP_BATCH_SIZE_LIST[-1] < max_decode_batch_size and batch_size <= max_decode_batch_size:
-                batches = []
-                for i in range(int(batch_size/max_prefill_batch_size)) :
-                    batch = self.generate_warmup_batch(request, PREFILL_WARMUP_SEQLEN_LIST[0], DECODE_WARMUP_BATCH_SIZE_LIST[-1], is_warmup)
-                    _, prefill_batch, _ = self.generate_token([batch], is_warmup)
-                    batches.append(prefill_batch)
-                while batch_size <= max_decode_batch_size:
-                    _, decode_batch, _ = self.generate_token(batches, is_warmup)
-                    DECODE_WARMUP_BATCH_SIZE_LIST.append(batch_size)
-                    batch_size = batch_size * 2
-                    batches.clear()
-
-                    for i in range(int(batch_size/max_prefill_batch_size)) :
-                        batch = self.generate_warmup_batch(request, PREFILL_WARMUP_SEQLEN_LIST[0], DECODE_WARMUP_BATCH_SIZE_LIST[-1], is_warmup)
-                        _, prefill_batch, _ = self.generate_token([batch], is_warmup)
-                        batches.append(prefill_batch)
-
-                batches.clear()
-                if DECODE_WARMUP_BATCH_SIZE_LIST[-1] < max_decode_batch_size:
-                    max_decode_batch_size = math.floor( max_decode_batch_size / 2) * 2
-                    batch_size = max_decode_batch_size
-                    for i in range(int(max_decode_batch_size / 2)) :
-                        batch = self.generate_warmup_batch(request, PREFILL_WARMUP_SEQLEN_LIST[0], 2, is_warmup)
-                        _, prefill_batch, _ = self.generate_token([batch], is_warmup)
-                        batches.append(prefill_batch)
-                    _, decode_batch, _ = self.generate_token(batches, is_warmup)
-                    DECODE_WARMUP_BATCH_SIZE_LIST.append(max_decode_batch_size)
-                max_batch_total_tokens = max_decode_batch_size * MAX_TOTAL_TOKENS
-                MAX_BATCH_TOTAL_TOKENS = max_batch_total_tokens
-        except :
-            raise RuntimeError(
-                f"Not enough memory to handle batch_size({batch_size}) decode warmup."
-                f"Decode batch size list:{DECODE_WARMUP_BATCH_SIZE_LIST}"
-                f"max_decode_batch_size is {max_decode_batch_size}"
-                f"You need to decrease env `MAX_BATCH_TOTAL_TOKENS` or '--max_batch_total_tokens'"
-            )
-
-        mem_stats = get_hpu_memory_stats(self.device)
-        logger.info(
-                f"\nFollowing decode warmup successfully.\n"
-                f"Decode batch size list:{DECODE_WARMUP_BATCH_SIZE_LIST}\n"
-                f"Memory stats: {mem_stats}"
-            )
-
-        return MAX_BATCH_TOTAL_TOKENS
\ No newline at end of file
+        logits = cuda_graph["logits"][:bs]
+        return logits, speculative_logits
diff --git a/server/text_generation_server/server.py b/server/text_generation_server/server.py
index a4dc4a4c8..37c460329 100644
--- a/server/text_generation_server/server.py
+++ b/server/text_generation_server/server.py
@@ -1,8 +1,5 @@
-# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
 import asyncio
 import os
-import sys
 import torch
 import time
 import signal
@@ -17,9 +14,6 @@ from typing import List, Optional
 from text_generation_server.cache import Cache
 from text_generation_server.interceptor import ExceptionInterceptor
 from text_generation_server.models import Model, get_model
-from text_generation_server.pb import generate_pb2_grpc, generate_pb2
-from text_generation_server.tracing import UDSOpenTelemetryAioServerInterceptor
-from text_generation_server.models.globals import set_model_id
 
 try:
     from text_generation_server.models.pali_gemma import PaliGemmaBatch
@@ -32,7 +26,10 @@ try:
 except (ImportError, NotImplementedError):
     # These imports can fail on CPU/Non flash.
     VLM_BATCH_TYPES = set()
-from text_generation_server.utils.version import is_driver_compatible, MIN_TGI_GAUDI_SYNAPSE_VERSION
+
+from text_generation_server.pb import generate_pb2_grpc, generate_pb2
+from text_generation_server.tracing import UDSOpenTelemetryAioServerInterceptor
+from text_generation_server.models.globals import set_model_id
 
 
 class SignalHandler:
@@ -52,25 +49,24 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
         self,
         model: Model,
         cache: Cache,
+        quantize: Optional[str],
         server_urls: List[str],
     ):
         self.cache = cache
         self.model = model
+        self.quantize = quantize
         self.server_urls = server_urls
         # For some reason, inference_mode does not work well with GLOO which we use on CPU
-        # TODO: The inferecemode set messes up the autograd op dispatch. And results in aten::matmul
-        # op not optimized issue. Will investigate further.
-        # if model.device.type == "hpu":
-        # Force inference mode for the lifetime of TextGenerationService
-        # self._inference_mode_raii_guard = torch._C._InferenceMode(True)
-
+        if model.device.type == "cuda":
+            # Force inference mode for the lifetime of TextGenerationService
+            self._inference_mode_raii_guard = torch._C._InferenceMode(True)
 
     async def Info(self, request, context):
         return self.model.info
 
     async def Health(self, request, context):
-        if self.model.device.type == "hpu":
-            torch.zeros((2, 2)).to("hpu")
+        if self.model.device.type == "cuda":
+            torch.zeros((2, 2)).cuda()
         return generate_pb2.HealthResponse()
 
     async def ServiceDiscovery(self, request, context):
@@ -93,19 +89,41 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
         return generate_pb2.FilterBatchResponse(batch=filtered_batch.to_pb())
 
     async def Warmup(self, request, context):
-        def batch_from_pb(batch):
-            return self.model.batch_type.from_pb(
-                batch, self.model.tokenizer, self.model.dtype, self.model.device
+        if self.quantize == "gptq":
+            try:
+                # When using GPTQ, Exllama kernels need some global kernels
+                # For which we have the finale shapes only after the model has loaded
+                # This will allocate those buffers.
+                from text_generation_server.layers.gptq import (
+                    create_exllama_buffers,
+                    set_device,
+                )
+
+                set_device(self.model.device)
+                create_exllama_buffers(request.max_prefill_tokens)
+            except ImportError:
+                pass
+
+        if (
+            self.model.batch_type in VLM_BATCH_TYPES
+        ):  # Hack, i would rather use kwargs in the `from_pb` call
+            batch = self.model.batch_type.from_pb_processor(
+                request.batch,
+                self.model.tokenizer,
+                self.model.processor,
+                self.model.model.config,
+                self.model.dtype,
+                self.model.device,
             )
-
-        if self.model.batch_type in VLM_BATCH_TYPES :
-            max_supported_total_tokens = self.model.warmup(request)
-            return generate_pb2.WarmupResponse(max_supported_total_tokens=max_supported_total_tokens)
         else:
-            batches = [batch_from_pb(batch) for batch in request.batches]
-            self.model.warmup(batches)
-            return generate_pb2.WarmupResponse()
+            batch = self.model.batch_type.from_pb(
+                request.batch, self.model.tokenizer, self.model.dtype, self.model.device
+            )
+        max_supported_total_tokens = self.model.warmup(batch)
 
+        return generate_pb2.WarmupResponse(
+            max_supported_total_tokens=max_supported_total_tokens
+        )
 
     async def Prefill(self, request, context):
         start = time.time_ns()
@@ -124,8 +142,8 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
             batch = self.model.batch_type.from_pb(
                 request.batch, self.model.tokenizer, self.model.dtype, self.model.device
             )
-     
-        generations, next_batch, timings = self.model.generate_token([batch])
+
+        generations, next_batch, timings = self.model.generate_token(batch)
         self.cache.set(next_batch)
 
         return generate_pb2.PrefillResponse(
@@ -151,13 +169,21 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
         if len(batches) == 0:
             raise ValueError("All batches are empty")
 
-        generations, next_batch, timings = self.model.generate_token(batches)
+        if len(batches) > 1:
+            start_concat = time.time_ns()
+            batch = self.model.batch_type.concatenate(batches)
+            concat_ns = time.time_ns() - start_concat
+        else:
+            batch = batches[0]
+            concat_ns = None
+
+        generations, next_batch, timings = self.model.generate_token(batch)
         self.cache.set(next_batch)
 
         return generate_pb2.DecodeResponse(
             generations=[generation.to_pb() for generation in generations],
             batch=next_batch.to_pb() if next_batch else None,
-            concat_ns=None, # TODO: measure concat time
+            concat_ns=concat_ns,
             forward_ns=timings[0],
             decode_ns=timings[1],
             total_ns=time.time_ns() - start,
@@ -168,62 +194,41 @@ def serve(
     model_id: str,
     revision: Optional[str],
     sharded: bool,
+    quantize: Optional[str],
     speculate: Optional[int],
     dtype: Optional[str],
     trust_remote_code: bool,
     uds_path: Path,
 ):
-    # Remove default handler
-    #logger.remove()
-    logger.add(
-        sys.stdout,
-        format="{message}",
-        filter="text_generation_server",
-        level="INFO",
-        serialize=False,
-        backtrace=True,
-        diagnose=False,
-    )
-
     async def serve_inner(
         model_id: str,
         revision: Optional[str],
         sharded: bool = False,
+        quantize: Optional[str] = None,
         speculate: Optional[int] = None,
         dtype: Optional[str] = None,
         trust_remote_code: bool = False,
     ):
-        if not is_driver_compatible():
-            logger.warning(f"Current Synapse version is lower than the minimum version supported: {MIN_TGI_GAUDI_SYNAPSE_VERSION}, this could result in failures")
-
         unix_socket_template = "unix://{}-{}"
-        logger.info("Server:server_inner: sharded ={}".format(sharded))
-
         if sharded:
-            rank = int(os.environ["RANK"])
-            logger.info("Server:server_inner: rank ={}".format(rank))
             server_urls = [
-                unix_socket_template.format(uds_path, rank) for rank in range(int(os.environ["WORLD_SIZE"]))
+                unix_socket_template.format(uds_path, rank)
+                for rank in range(int(os.environ["WORLD_SIZE"]))
             ]
             local_url = server_urls[int(os.environ["RANK"])]
         else:
             local_url = unix_socket_template.format(uds_path, 0)
             server_urls = [local_url]
 
-        logger.info("Server:server_inner: data type = {}, local_url = {}".format(dtype, local_url))
-        if dtype == "bfloat16" or None:
-            data_type = torch.bfloat16
-        else:
-            data_type = torch.float
-        if revision == "None":
-            revision = None
         try:
             model = get_model(
                 model_id,
                 revision,
+                sharded,
+                quantize,
                 speculate,
-                data_type,
-                trust_remote_code
+                dtype,
+                trust_remote_code,
             )
         except Exception:
             logger.exception("Error when initializing model")
@@ -236,7 +241,7 @@ def serve(
             ]
         )
         generate_pb2_grpc.add_TextGenerationServiceServicer_to_server(
-            TextGenerationService(model, Cache(), server_urls), server
+            TextGenerationService(model, Cache(), quantize, server_urls), server
         )
         SERVICE_NAMES = (
             generate_pb2.DESCRIPTOR.services_by_name["TextGenerationService"].full_name,
@@ -255,6 +260,6 @@ def serve(
     set_model_id(model_id)
     asyncio.run(
         serve_inner(
-            model_id, revision, sharded, speculate, dtype, trust_remote_code
+            model_id, revision, sharded, quantize, speculate, dtype, trust_remote_code
         )
     )
diff --git a/server/text_generation_server/tgi_service.py b/server/text_generation_server/tgi_service.py
deleted file mode 100644
index f88c8c8ba..000000000
--- a/server/text_generation_server/tgi_service.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import os
-from pathlib import Path
-from loguru import logger
-import sys
-from text_generation_server import server
-import argparse
-
-
-def main(args):
-    logger.info("TGIService: starting tgi service .... ")
-    logger.info(
-        "TGIService: --model_id {}, --revision {}, --sharded {}, --speculate {}, --dtype {}, --trust_remote_code {}, --uds_path {} ".format(
-            args.model_id, args.revision, args.sharded, args.speculate, args.dtype, args.trust_remote_code, args.uds_path
-        )
-    )
-    server.serve(
-        model_id=args.model_id,
-        revision=args.revision,
-        sharded=args.sharded,
-        speculate=args.speculate,
-        dtype=args.dtype,
-        trust_remote_code=args.trust_remote_code,
-        uds_path=args.uds_path,
-    )
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model_id", type=str)
-    parser.add_argument("--revision", type=str)
-    parser.add_argument("--sharded", type=bool)
-    parser.add_argument("--speculate", type=int, default=None)
-    parser.add_argument("--dtype", type=str)
-    parser.add_argument("--trust_remote_code", type=bool)
-    parser.add_argument("--uds_path", type=Path)
-    args = parser.parse_args()
-    main(args)
diff --git a/server/text_generation_server/utils/__init__.py b/server/text_generation_server/utils/__init__.py
index 565a7c3ca..08ba808d1 100644
--- a/server/text_generation_server/utils/__init__.py
+++ b/server/text_generation_server/utils/__init__.py
@@ -1,6 +1,3 @@
-# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
-import text_generation_server.habana_quantization_env
 from text_generation_server.utils.convert import convert_file, convert_files
 from text_generation_server.utils.dist import initialize_torch_distributed
 from text_generation_server.utils.weights import Weights
@@ -21,9 +18,6 @@ from text_generation_server.utils.tokens import (
     FinishReason,
     Sampling,
     Greedy,
-    make_tokenizer_optional,
-    is_tokenizer_transparent,
-    pad_next_token_chooser_parameters,
 )
 
 __all__ = [
diff --git a/server/text_generation_server/utils/debug.py b/server/text_generation_server/utils/debug.py
deleted file mode 100644
index ef8d437b7..000000000
--- a/server/text_generation_server/utils/debug.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
-import os
-import glob
-import time
-
-from optimum.habana.utils import to_gb_rounded
-import habana_frameworks.torch as htorch
-
-START_TS = None
-DBG_TRACE_FILENAME = os.environ.get('DBG_TRACE_FILENAME')
-if 'GRAPH_VISUALIZATION' in os.environ:
-    for f in glob.glob('.graph_dumps/*'):
-        os.remove(f)
-
-
-def count_hpu_graphs():
-    return len(glob.glob('.graph_dumps/*PreGraph*'))
-
-
-def dbg_trace(tag, txt):
-    global START_TS
-    if DBG_TRACE_FILENAME is not None and int(os.getenv("RANK", 0)) == 0:
-        if START_TS is None:
-            START_TS = time.perf_counter()
-        time_offset = time.perf_counter() - START_TS
-        mem_stats = htorch.hpu.memory.memory_stats()
-        mem_used = to_gb_rounded(mem_stats['InUse'])
-        max_mem_used = to_gb_rounded(mem_stats['MaxInUse'])
-        print(f'ts:{time_offset:.3f}s g:{count_hpu_graphs()} mu:{mem_used:.1f}GB '
-              f'mmu:{max_mem_used:.1f}GB | {tag} | {txt}', flush=True, file=open(DBG_TRACE_FILENAME, 'a'))
diff --git a/server/text_generation_server/utils/dist.py b/server/text_generation_server/utils/dist.py
index d370a3d5c..3625e6f2c 100644
--- a/server/text_generation_server/utils/dist.py
+++ b/server/text_generation_server/utils/dist.py
@@ -44,12 +44,6 @@ class FakeGroup:
 
 
 def initialize_torch_distributed():
-    import habana_frameworks.torch.core as htcore
-
-    rank = int(os.getenv("RANK", "0"))
-    world_size = int(os.getenv("WORLD_SIZE", "1"))
-
-    options = None
     if torch.cuda.is_available():
         from torch.distributed import ProcessGroupNCCL
 
@@ -62,11 +56,6 @@ def initialize_torch_distributed():
         options = ProcessGroupNCCL.Options()
         options.is_high_priority_stream = True
         options._timeout = timedelta(seconds=60)
-    elif torch.hpu.is_available():
-        backend = "hccl"
-        n_hpus = torch.hpu.device_count()
-        if world_size > n_hpus:
-            raise ValueError(f"WORLD_SIZE ({world_size}) is higher than the number of available HPUs ({n_hpus}).")
     else:
         try:
             import oneccl_bindings_for_pytorch
diff --git a/server/text_generation_server/utils/gptq/quant_linear.py b/server/text_generation_server/utils/gptq/quant_linear.py
deleted file mode 100644
index a832f7555..000000000
--- a/server/text_generation_server/utils/gptq/quant_linear.py
+++ /dev/null
@@ -1,359 +0,0 @@
-import math
-import numpy as np
-import torch
-import torch.nn as nn
-from torch.cuda.amp import custom_bwd, custom_fwd
-
-try:
-    import triton
-    import triton.language as tl
-    from . import custom_autotune
-
-    # code based https://github.com/fpgaminer/GPTQ-triton
-    @custom_autotune.autotune(
-        configs=[
-            triton.Config(
-                {
-                    "BLOCK_SIZE_M": 64,
-                    "BLOCK_SIZE_N": 256,
-                    "BLOCK_SIZE_K": 32,
-                    "GROUP_SIZE_M": 8,
-                },
-                num_stages=4,
-                num_warps=4,
-            ),
-            triton.Config(
-                {
-                    "BLOCK_SIZE_M": 128,
-                    "BLOCK_SIZE_N": 128,
-                    "BLOCK_SIZE_K": 32,
-                    "GROUP_SIZE_M": 8,
-                },
-                num_stages=4,
-                num_warps=4,
-            ),
-            triton.Config(
-                {
-                    "BLOCK_SIZE_M": 64,
-                    "BLOCK_SIZE_N": 128,
-                    "BLOCK_SIZE_K": 32,
-                    "GROUP_SIZE_M": 8,
-                },
-                num_stages=4,
-                num_warps=4,
-            ),
-            triton.Config(
-                {
-                    "BLOCK_SIZE_M": 128,
-                    "BLOCK_SIZE_N": 32,
-                    "BLOCK_SIZE_K": 32,
-                    "GROUP_SIZE_M": 8,
-                },
-                num_stages=4,
-                num_warps=4,
-            ),
-            triton.Config(
-                {
-                    "BLOCK_SIZE_M": 64,
-                    "BLOCK_SIZE_N": 64,
-                    "BLOCK_SIZE_K": 32,
-                    "GROUP_SIZE_M": 8,
-                },
-                num_stages=4,
-                num_warps=4,
-            ),
-            triton.Config(
-                {
-                    "BLOCK_SIZE_M": 64,
-                    "BLOCK_SIZE_N": 128,
-                    "BLOCK_SIZE_K": 32,
-                    "GROUP_SIZE_M": 8,
-                },
-                num_stages=2,
-                num_warps=8,
-            ),
-            triton.Config(
-                {
-                    "BLOCK_SIZE_M": 64,
-                    "BLOCK_SIZE_N": 64,
-                    "BLOCK_SIZE_K": 64,
-                    "GROUP_SIZE_M": 8,
-                },
-                num_stages=3,
-                num_warps=8,
-            ),
-            triton.Config(
-                {
-                    "BLOCK_SIZE_M": 32,
-                    "BLOCK_SIZE_N": 32,
-                    "BLOCK_SIZE_K": 128,
-                    "GROUP_SIZE_M": 8,
-                },
-                num_stages=2,
-                num_warps=4,
-            ),
-        ],
-        key=["M", "N", "K"],
-        nearest_power_of_two=True,
-        prune_configs_by={
-            "early_config_prune": custom_autotune.matmul248_kernel_config_pruner,
-            "perf_model": None,
-            "top_k": None,
-        },
-    )
-    @triton.jit
-    def matmul_248_kernel(
-        a_ptr,
-        b_ptr,
-        c_ptr,
-        scales_ptr,
-        zeros_ptr,
-        g_ptr,
-        M,
-        N,
-        K,
-        bits,
-        maxq,
-        stride_am,
-        stride_ak,
-        stride_bk,
-        stride_bn,
-        stride_cm,
-        stride_cn,
-        stride_scales,
-        stride_zeros,
-        BLOCK_SIZE_M: tl.constexpr,
-        BLOCK_SIZE_N: tl.constexpr,
-        BLOCK_SIZE_K: tl.constexpr,
-        GROUP_SIZE_M: tl.constexpr,
-    ):
-        """
-        Compute the matrix multiplication C = A x B.
-        A is of shape (M, K) float16
-        B is of shape (K//8, N) int32
-        C is of shape (M, N) float16
-        scales is of shape (G, N) float16
-        zeros is of shape (G, N) float16
-        g_ptr is of shape (K) int32
-        """
-        infearure_per_bits = 32 // bits
-
-        pid = tl.program_id(axis=0)
-        num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
-        num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
-        num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
-        num_pid_in_group = GROUP_SIZE_M * num_pid_n
-        group_id = pid // num_pid_in_group
-        first_pid_m = group_id * GROUP_SIZE_M
-        group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
-        pid_m = first_pid_m + (pid % group_size_m)
-        pid_n = (pid % num_pid_in_group) // group_size_m
-
-        offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
-        offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
-        offs_k = tl.arange(0, BLOCK_SIZE_K)
-        a_ptrs = a_ptr + (
-            offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
-        )  # (BLOCK_SIZE_M, BLOCK_SIZE_K)
-        a_mask = offs_am[:, None] < M
-        # b_ptrs is set up such that it repeats elements along the K axis 8 times
-        b_ptrs = b_ptr + (
-            (offs_k[:, None] // infearure_per_bits) * stride_bk
-            + offs_bn[None, :] * stride_bn
-        )  # (BLOCK_SIZE_K, BLOCK_SIZE_N)
-        g_ptrs = g_ptr + offs_k
-        # shifter is used to extract the N bits of each element in the 32-bit word from B
-        scales_ptrs = scales_ptr + offs_bn[None, :]
-        zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits)
-
-        shifter = (offs_k % infearure_per_bits) * bits
-        zeros_shifter = (offs_bn % infearure_per_bits) * bits
-        accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
-
-        for k in range(0, num_pid_k):
-            g_idx = tl.load(g_ptrs)
-
-            # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
-            scales = tl.load(
-                scales_ptrs + g_idx[:, None] * stride_scales
-            )  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
-            zeros = tl.load(
-                zeros_ptrs + g_idx[:, None] * stride_zeros
-            )  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
-
-            zeros = (zeros >> zeros_shifter[None, :]) & maxq
-            zeros = (zeros + 1) & maxq  # eventually avoid overflow
-
-            a = tl.load(a_ptrs, mask=a_mask, other=0.0)  # (BLOCK_SIZE_M, BLOCK_SIZE_K)
-            b = tl.load(b_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
-
-            # Now we need to unpack b (which is N-bit values) into 32-bit values
-            b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
-            b = (b - zeros) * scales  # Scale and shift
-
-            accumulator += tl.dot(a, b)
-            a_ptrs += BLOCK_SIZE_K
-            b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
-            g_ptrs += BLOCK_SIZE_K
-
-        c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
-        c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
-        tl.store(c_ptrs, accumulator, mask=c_mask)
-
-except:
-    print("triton not installed.")
-
-
-def matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq):
-    with torch.cuda.device(input.device):
-        output = torch.empty(
-            (input.shape[0], qweight.shape[1]), device=input.device, dtype=torch.float16
-        )
-        grid = lambda META: (
-            triton.cdiv(input.shape[0], META["BLOCK_SIZE_M"])
-            * triton.cdiv(qweight.shape[1], META["BLOCK_SIZE_N"]),
-        )
-        matmul_248_kernel[grid](
-            input,
-            qweight,
-            output,
-            scales,
-            qzeros,
-            g_idx,
-            input.shape[0],
-            qweight.shape[1],
-            input.shape[1],
-            bits,
-            maxq,
-            input.stride(0),
-            input.stride(1),
-            qweight.stride(0),
-            qweight.stride(1),
-            output.stride(0),
-            output.stride(1),
-            scales.stride(0),
-            qzeros.stride(0),
-        )
-        return output
-
-
-class QuantLinearFunction(torch.autograd.Function):
-    @staticmethod
-    @custom_fwd(cast_inputs=torch.float16)
-    def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
-        output = matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq)
-        return output
-
-
-class QuantLinear(nn.Module):
-    def __init__(self, qweight, qzeros, scales, g_idx, bias, bits, groupsize):
-        super().__init__()
-        self.register_buffer("qweight", qweight)
-        self.register_buffer("qzeros", qzeros)
-        self.register_buffer("scales", scales)
-        self.register_buffer("g_idx", g_idx)
-        if bias is not None:
-            self.register_buffer("bias", bias)
-        else:
-            self.bias = None
-        if bits not in [2, 4, 8]:
-            raise NotImplementedError("Only 2,4,8 bits are supported.")
-        self.bits = bits
-        self.maxq = 2**self.bits - 1
-        self.groupsize = groupsize
-
-        self.outfeatures = qweight.shape[1]
-        self.infeatures = qweight.shape[0] * 32 // bits
-
-    @classmethod
-    def new(cls, bits, groupsize, infeatures, outfeatures, bias):
-        if bits not in [2, 4, 8]:
-            raise NotImplementedError("Only 2,4,8 bits are supported.")
-
-        qweight = torch.zeros((infeatures // 32 * bits, outfeatures), dtype=torch.int32)
-        qzeros = torch.zeros(
-            (math.ceil(infeatures / groupsize), outfeatures // 32 * bits),
-            dtype=torch.int32,
-        )
-        scales = torch.zeros(
-            (math.ceil(infeatures / groupsize), outfeatures), dtype=torch.float16
-        )
-        g_idx = torch.tensor(
-            [i // groupsize for i in range(infeatures)], dtype=torch.int32
-        )
-        if bias:
-            bias = torch.zeros((outfeatures), dtype=torch.float16)
-        else:
-            bias = None
-        return cls(qweight, qzeros, scales, g_idx, bias, bits, groupsize)
-
-    def pack(self, linear, scales, zeros, g_idx=None):
-        self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx
-
-        scales = scales.t().contiguous()
-        zeros = zeros.t().contiguous()
-        scale_zeros = zeros * scales
-        self.scales = scales.clone().half()
-        if linear.bias is not None:
-            self.bias = linear.bias.clone().half()
-
-        intweight = []
-        for idx in range(self.infeatures):
-            intweight.append(
-                torch.round(
-                    (linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]])
-                    / self.scales[self.g_idx[idx]]
-                ).to(torch.int)[:, None]
-            )
-        intweight = torch.cat(intweight, dim=1)
-        intweight = intweight.t().contiguous()
-        intweight = intweight.numpy().astype(np.uint32)
-        qweight = np.zeros(
-            (intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32
-        )
-        i = 0
-        row = 0
-        while row < qweight.shape[0]:
-            if self.bits in [2, 4, 8]:
-                for j in range(i, i + (32 // self.bits)):
-                    qweight[row] |= intweight[j] << (self.bits * (j - i))
-                i += 32 // self.bits
-                row += 1
-            else:
-                raise NotImplementedError("Only 2,4,8 bits are supported.")
-
-        qweight = qweight.astype(np.int32)
-        self.qweight = torch.from_numpy(qweight)
-
-        zeros -= 1
-        zeros = zeros.numpy().astype(np.uint32)
-        qzeros = np.zeros(
-            (zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32
-        )
-        i = 0
-        col = 0
-        while col < qzeros.shape[1]:
-            if self.bits in [2, 4, 8]:
-                for j in range(i, i + (32 // self.bits)):
-                    qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
-                i += 32 // self.bits
-                col += 1
-            else:
-                raise NotImplementedError("Only 2,4,8 bits are supported.")
-
-        qzeros = qzeros.astype(np.int32)
-        self.qzeros = torch.from_numpy(qzeros)
-
-    def forward(self, x):
-        out_shape = x.shape[:-1] + (self.outfeatures,)
-        out = QuantLinearFunction.apply(
-            x.reshape(-1, x.shape[-1]),
-            self.qweight,
-            self.scales,
-            self.qzeros,
-            self.g_idx,
-            self.bits,
-            self.maxq,
-        )
-        out = out + self.bias if self.bias is not None else out
-        return out.reshape(out_shape)
diff --git a/server/text_generation_server/utils/logits_process.py b/server/text_generation_server/utils/logits_process.py
index 104fc2f09..6b9154370 100644
--- a/server/text_generation_server/utils/logits_process.py
+++ b/server/text_generation_server/utils/logits_process.py
@@ -1,6 +1,5 @@
 import math
 import torch
-import habana_frameworks.torch.core as htcore
 
 from loguru import logger
 from typing import Dict, Union
@@ -44,31 +43,37 @@ class StaticWarper:
         if typical_p is not None and typical_p < 1.0:
             self.warpers.append(TypicalLogitsWarper(mass=typical_p))
 
-        self.hpu_graph = None
+        self.cuda_graph = None
         self.static_scores = None
         self.static_warped_scores = None
         self.static_next_logprob = None
 
     def __call__(self, scores):
-        if self.hpu_graph is None:
-            self.static_scores = scores.clone().contiguous()
-            self.static_warped_scores = scores.clone().contiguous()
-            self.static_next_logprob = scores.clone().contiguous()
-            self.hpu_graph = htcore.hpu.HPUGraph()
+        if torch.cuda.is_available():
+            if self.cuda_graph is None:
+                self.static_scores = scores
+                self.cuda_graph = torch.cuda.CUDAGraph()
 
-            with htcore.hpu.graph(self.hpu_graph):
-                local_scores = self.static_scores
-                for warper in self.warpers:
-                    local_scores = warper(None, local_scores)
+                with torch.cuda.graph(self.cuda_graph, pool=mempool):
+                    local_scores = self.static_scores
+                    for warper in self.warpers:
+                        local_scores = warper(None, local_scores)
 
-                self.static_warped_scores.copy_(local_scores)
-                # Compute logprobs
-                self.static_next_logprob.copy_(torch.log_softmax(self.static_warped_scores, -1))
+                    self.static_warped_scores = local_scores
+                    # Compute logprobs
+                    self.static_next_logprob = torch.log_softmax(
+                        self.static_warped_scores, -1
+                    )
 
-        self.static_scores.copy_(scores)
-        self.hpu_graph.replay()
+            self.static_scores.copy_(scores)
+            self.cuda_graph.replay()
 
-        return self.static_warped_scores, self.static_next_logprob
+            return self.static_warped_scores, self.static_next_logprob
+
+        # CPU branch
+        for warper in self.warpers:
+            scores = warper(None, scores)
+        return scores, torch.log_softmax(scores, -1)
 
 
 @lru_cache(10)
@@ -78,7 +83,9 @@ def static_warper(
     top_p: Optional[float],
     typical_p: Optional[float],
 ) -> StaticWarper:
-    return StaticWarper(temperature=temperature, top_k=top_k, top_p=top_p, typical_p=typical_p)
+    return StaticWarper(
+        temperature=temperature, top_k=top_k, top_p=top_p, typical_p=typical_p
+    )
 
 
 class HeterogeneousRepetitionPenaltyLogitsProcessor(LogitsProcessor):
@@ -95,13 +102,17 @@ class HeterogeneousRepetitionPenaltyLogitsProcessor(LogitsProcessor):
 
     def __init__(self, penalty: List[float], dtype: torch.dtype, device: torch.device):
         self.penalty = penalty
-        self.penalty_tensor = torch.tensor(penalty, dtype=dtype, device=device).unsqueeze(1)
+        self.penalty_tensor = torch.tensor(
+            penalty, dtype=dtype, device=device
+        ).unsqueeze(1)
 
     def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
         score = torch.gather(scores, 1, input_ids)
 
         # if score < 0 then repetition penalty has to be multiplied to reduce the previous token probability
-        score = torch.where(score < 0, score * self.penalty_tensor, score / self.penalty_tensor)
+        score = torch.where(
+            score < 0, score * self.penalty_tensor, score / self.penalty_tensor
+        )
 
         scores.scatter_(1, input_ids, score)
         return scores
@@ -159,11 +170,9 @@ class HeterogeneousFrequencyPenaltyLogitsProcessor(LogitsProcessor):
         vocab_size = scores.size(1)
 
         # Calculate the frequency for each token so far
-        token_freq = torch.zeros(
-            batch_size, vocab_size, dtype=scores.dtype, device=scores.device
-        )
+        token_freq = torch.zeros(batch_size, vocab_size, device=input_ids.device)
         token_freq.scatter_add_(
-            1, input_ids, torch.ones_like(input_ids, dtype=scores.dtype, device=scores.device)
+            1, input_ids, torch.ones_like(input_ids, dtype=torch.float)
         )
         token_freq /= input_size
 
@@ -190,9 +199,13 @@ class HeterogeneousTemperatureLogitsWarper:
             The value used to module the logits distribution.
     """
 
-    def __init__(self, temperature: List[float], dtype: torch.dtype, device: torch.device):
+    def __init__(
+        self, temperature: List[float], dtype: torch.dtype, device: torch.device
+    ):
         self.temperature = temperature
-        self.temperature_tensor = torch.tensor(temperature, dtype=dtype, device=device).unsqueeze(1)
+        self.temperature_tensor = torch.tensor(
+            temperature, dtype=dtype, device=device
+        ).unsqueeze(1)
 
     def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
         scores.div_(self.temperature_tensor)
@@ -231,7 +244,9 @@ class HeterogeneousTopPLogitsWarper(LogitsWarper):
         min_tokens_to_keep: int = 1,
     ):
         self.top_p = top_p
-        self.top_p_opposite = 1 - torch.tensor(top_p, dtype=dtype, device=device).unsqueeze(1)
+        self.top_p_opposite = 1 - torch.tensor(
+            top_p, dtype=dtype, device=device
+        ).unsqueeze(1)
         self.filter_value = filter_value
         self.min_tokens_to_keep = min_tokens_to_keep
 
@@ -248,7 +263,9 @@ class HeterogeneousTopPLogitsWarper(LogitsWarper):
         sorted_indices_to_remove[..., -self.min_tokens_to_keep :] = 0
 
         # scatter sorted tensors to original indexing
-        indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+        indices_to_remove = sorted_indices_to_remove.scatter(
+            1, sorted_indices, sorted_indices_to_remove
+        )
         warped_scores = scores.masked_fill_(indices_to_remove, self.filter_value)
 
         return warped_scores
@@ -296,7 +313,9 @@ class HeterogeneousTopKLogitsWarper(LogitsWarper):
         disabled = [x == 0 for x in top_k]
 
         if any(disabled):
-            self.top_k_disabled_mask = torch.tensor(disabled, dtype=torch.bool, device=device).view(-1, 1)
+            self.top_k_disabled_mask = torch.tensor(
+                disabled, dtype=torch.bool, device=device
+            ).view(-1, 1)
         else:
             self.top_k_disabled_mask = None
 
@@ -332,7 +351,9 @@ class HeterogeneousTopKLogitsWarper(LogitsWarper):
             self.max_top_k = max(self.top_k)
 
             if self.top_k_disabled_mask is not None:
-                self.top_k_disabled_mask = self.top_k_disabled_mask[indices] if any(disabled) else None
+                self.top_k_disabled_mask = (
+                    self.top_k_disabled_mask[indices] if any(disabled) else None
+                )
 
             return self
         return None
@@ -398,11 +419,15 @@ class HeterogeneousTypicalLogitsWarper(LogitsWarper):
         if self.disabled_mask is not None:
             last_ind.masked_fill_(self.disabled_mask, scores.shape[-1] - 1)
 
-        sorted_indices_to_remove = sorted_scores > sorted_scores.gather(1, last_ind.view(-1, 1))
+        sorted_indices_to_remove = sorted_scores > sorted_scores.gather(
+            1, last_ind.view(-1, 1)
+        )
         if self.min_tokens_to_keep > 1:
             # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
             sorted_indices_to_remove[..., : self.min_tokens_to_keep] = 0
-        indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+        indices_to_remove = sorted_indices_to_remove.scatter(
+            1, sorted_indices, sorted_indices_to_remove
+        )
 
         warped_scores = scores.masked_fill_(indices_to_remove, self.filter_value)
 
@@ -416,7 +441,9 @@ class HeterogeneousTypicalLogitsWarper(LogitsWarper):
             self.mass_tensor = self.mass_tensor[indices]
 
             if self.disabled_mask is not None:
-                self.disabled_mask = self.disabled_mask[indices] if any(disabled) else None
+                self.disabled_mask = (
+                    self.disabled_mask[indices] if any(disabled) else None
+                )
 
             return self
         return None
@@ -553,7 +580,7 @@ class HeterogeneousGrammarLogitProcessor(LogitsProcessor):
         mask = torch.full_like(logits, -math.inf)
         for i in range(logits.shape[0]):
             fsm = self.fsms[i]
-            if fsm is None:
+            if fsm_grammar_states[i] == -1 or fsm is None:
                 continue
             allowed_tokens = fsm.allowed_token_ids(fsm_grammar_states[i])
             mask[i, allowed_tokens] = 0
diff --git a/server/text_generation_server/utils/tokens.py b/server/text_generation_server/utils/tokens.py
index 267ea0683..22f86b60c 100644
--- a/server/text_generation_server/utils/tokens.py
+++ b/server/text_generation_server/utils/tokens.py
@@ -1,5 +1,3 @@
-# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
-
 import re
 from typing import List, Optional, Tuple, Set, Union
 
@@ -22,22 +20,21 @@ from text_generation_server.utils.logits_process import (
 )
 from text_generation_server.utils.watermark import WatermarkLogitsProcessor
 from transformers import PreTrainedTokenizerBase, RepetitionPenaltyLogitsProcessor
-from transformers.utils import to_py_obj
 
 
 class NextTokenChooser:
     def __init__(
         self,
-        watermark=False,
-        temperature=1.0,
-        repetition_penalty=1.0,
-        frequency_penalty=0.0,
-        top_k=None,
-        top_p=None,
-        typical_p=None,
-        do_sample=False,
-        seed=0,
-        device="cpu",
+        watermark: bool = False,
+        temperature: float = 1.0,
+        repetition_penalty: float = 1.0,
+        frequency_penalty: float = 0.0,
+        top_k: Optional[int] = None,
+        top_p: Optional[float] = None,
+        typical_p: Optional[float] = None,
+        do_sample: bool = False,
+        seed: int = 0,
+        device: str = "cpu",
         tokenizer: Optional[PreTrainedTokenizerBase] = None,
         grammar: str = "",
         grammar_type: GrammarType = GrammarType.GRAMMAR_TYPE_NONE,
@@ -70,7 +67,9 @@ class NextTokenChooser:
             or (typical_p is not None and typical_p < 1.0)
         )
         if has_warpers:
-            self.static_warper = static_warper(temperature=temperature, top_k=top_k, top_p=top_p, typical_p=typical_p)
+            self.static_warper = static_warper(
+                temperature=temperature, top_k=top_k, top_p=top_p, typical_p=typical_p
+            )
         else:
             self.static_warper = None
 
@@ -249,12 +248,10 @@ class HeterogeneousNextTokenChooser:
         tokenizer: PreTrainedTokenizerBase,
         grammars: List[str],
         grammar_types: List[int],
-        fsm_grammar_states: List[int],
-        quantization_enabled: bool,
+        fsm_grammar_states=List[int],
     ):
         warpers = []
 
-        # TODO: enable watermark with FP8 quantization
         self.watermark_processor = (
             HeterogeneousProcessorWrapper(
                 {
@@ -263,7 +260,7 @@ class HeterogeneousNextTokenChooser:
                     if do_watermark
                 }
             )
-            if any(watermark) and not quantization_enabled
+            if any(watermark)
             else None
         )
 
@@ -435,18 +432,6 @@ class HeterogeneousNextTokenChooser:
             )
         return self
 
-    def advance_grammar_single_with_past_state(
-        self, grammar_state_index: int, next_id: torch.Tensor, past_state: int
-    ):
-        if self.grammar_processor is not None:
-            next_id = next_id.item()
-            self.fsm_grammar_states[grammar_state_index] = (
-                self.grammar_processor.advance_at_index(
-                    next_id, past_state, grammar_state_index,
-                )
-            )
-        return self
-
     def filter(self, indices):
         if self.watermark_processor is not None:
             self.watermark_processor = self.watermark_processor.filter(indices)
@@ -497,7 +482,6 @@ class HeterogeneousNextTokenChooser:
         device: torch.device,
         tokenizer: PreTrainedTokenizerBase,
         fsm_grammar_states: Optional[List[int]] = None,
-        quantization_enabled: bool = False,
     ) -> "HeterogeneousNextTokenChooser":
         return HeterogeneousNextTokenChooser(
             watermark=[pb_.watermark for pb_ in pb],
@@ -517,37 +501,12 @@ class HeterogeneousNextTokenChooser:
             fsm_grammar_states=(
                 fsm_grammar_states if fsm_grammar_states else [0] * len(pb)
             ),
-            quantization_enabled=quantization_enabled,
         )
 
 
-def pad_next_token_chooser_parameters(
-    parameters: List[generate_pb2.NextTokenChooserParameters],
-    expected_size: int,
-) -> List[generate_pb2.NextTokenChooserParameters]:
-    # disable all logits processors to minimize padding overhead
-    empty_parameters = generate_pb2.NextTokenChooserParameters(
-        temperature=1.0,
-        top_k=0,
-        top_p=1.0,
-        typical_p=1.0,
-        do_sample=False,
-        seed=0,
-        repetition_penalty=1.0,
-        frequency_penalty=0.0,
-        watermark=False,
-        grammar="",
-        grammar_type=0,
-    )
-    parameters.extend(
-        [empty_parameters] * (expected_size - len(parameters))
-    )
-    return parameters
-
-
 class Sampling:
     def __init__(self, seed: int, device: str = "cpu"):
-        self.generator = torch.Generator("cpu")
+        self.generator = torch.Generator(device)
         self.generator.manual_seed(seed)
         self.seed = seed
 
@@ -583,7 +542,7 @@ class HeterogeneousSampling:
         self.greedy = Greedy()
 
     def __call__(self, logits):
-        out = torch.zeros(logits.shape[0], dtype=torch.int64, device=logits.device)
+        out = torch.empty(logits.shape[0], dtype=torch.int64, device=logits.device)
         if self.greedy_indices:
             # Computing for all indices is faster than slicing
             torch.argmax(logits, -1, out=out)
@@ -645,8 +604,9 @@ def batch_top_tokens(
     top_n_indices = (logprobs >= nth_highest).nonzero()
     _, top_n_ishes = torch.unique_consecutive(top_n_indices[:, 0], return_counts=True)
 
+    k = 1 if top_n_ishes.numel() == 0 else top_n_ishes.max()
     # Take a new topk for these new max n values
-    top_k = torch.topk(logprobs, k=top_n_ishes.max(), dim=1, sorted=True)
+    top_k = torch.topk(logprobs, k=k, dim=1, sorted=True)
 
     top_n_ishes = top_n_ishes.tolist()
     top_indices = top_k.indices.tolist()
@@ -684,50 +644,3 @@ def batch_top_tokens(
         batch_top_token_logprobs.append(row_top_token_logprobs)
 
     return batch_top_token_ids, batch_top_token_logprobs
-
-
-def make_tokenizer_optional(tokenizer):
-    class _(type(tokenizer)):
-        def __call__(
-            self,
-            text,
-            return_tensors,
-            padding,
-            return_token_type_ids,
-            truncation,
-            max_length
-        ):
-            assert return_tensors == "pt", "inccorrect input arguments when calling TransparentTokenizer"
-            assert padding == "max_length" or padding == "longest", "inccorrect input arguments when calling TransparentTokenizer"
-            assert return_token_type_ids == False, "inccorrect input arguments when calling TransparentTokenizer"
-            assert truncation == True, "inccorrect input arguments when calling TransparentTokenizer"
-
-            def str_token_to_int(i):
-                if i == '?':
-                    return tokenizer.pad_token_id
-                else:
-                    return int(i)
-            all_tokens = [[str_token_to_int(i.strip()) for i in inner_text.split(',')]
-                          for inner_text in text]
-            if padding == "longest":
-                max_length = max(len(tokens) for tokens in all_tokens)
-            return {"input_ids": torch.tensor([[tokenizer.pad_token_id] * (max_length - len(tokens)) + tokens for tokens in all_tokens]),
-                    "attention_mask": torch.tensor([[0] * (max_length - len(tokens)) + [1] * len(tokens) for tokens in all_tokens])}
-
-        def decode(
-            self,
-            token_ids,
-            skip_special_tokens: bool = False,
-            clean_up_tokenization_spaces: bool = None,
-            **kwargs,
-        ) -> str:
-            return ','.join(str(i) for i in to_py_obj(token_ids))
-
-    import os
-    if os.getenv("SKIP_TOKENIZER_IN_TGI", "false").lower() == "true":
-        tokenizer.__class__ = _
-        tokenizer.is_transparent = True
-
-
-def is_tokenizer_transparent(tokenizer):
-    return hasattr(tokenizer, "is_transparent") and tokenizer.is_transparent is True
diff --git a/server/text_generation_server/utils/version.py b/server/text_generation_server/utils/version.py
deleted file mode 100644
index a72a9ea7b..000000000
--- a/server/text_generation_server/utils/version.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from optimum.habana.utils import get_driver_version
-from packaging.version import Version
-
-MIN_TGI_GAUDI_SYNAPSE_VERSION=Version("1.16.0")
-
-
-def is_driver_compatible():
-    driver_version = get_driver_version()
-    if driver_version is not None:
-        if driver_version < MIN_TGI_GAUDI_SYNAPSE_VERSION:
-            return False
-    return True
\ No newline at end of file
diff --git a/server/text_generation_server/utils/watermark.py b/server/text_generation_server/utils/watermark.py
index 7f4bf3676..5d8f53123 100644
--- a/server/text_generation_server/utils/watermark.py
+++ b/server/text_generation_server/utils/watermark.py
@@ -34,17 +34,21 @@ class WatermarkLogitsProcessor(LogitsProcessor):
         # watermarking parameters
         self.gamma = gamma
         self.delta = delta
-        self.rng = torch.Generator(device="cpu")
+        self.rng = torch.Generator(device=device)
         self.hash_key = hash_key
 
     def _seed_rng(self, input_ids: Union[List[int], torch.LongTensor]):
         if isinstance(input_ids, list):
-            assert len(input_ids) >= 1, "requires at least a 1 token prefix sequence to seed rng"
+            assert (
+                len(input_ids) >= 1
+            ), "requires at least a 1 token prefix sequence to seed rng"
             prev_token = input_ids[-1]
         else:
             assert len(input_ids) == 1
             input_ids = input_ids[0]
-            assert input_ids.shape[-1] >= 1, "requires at least a 1 token prefix sequence to seed rng"
+            assert (
+                input_ids.shape[-1] >= 1
+            ), "requires at least a 1 token prefix sequence to seed rng"
             prev_token = input_ids[-1].item()
         self.rng.manual_seed(self.hash_key * prev_token)
 
@@ -63,7 +67,9 @@ class WatermarkLogitsProcessor(LogitsProcessor):
         return greenlist_ids
 
     @staticmethod
-    def _calc_greenlist_mask(scores: torch.FloatTensor, greenlist_token_ids) -> torch.BoolTensor:
+    def _calc_greenlist_mask(
+        scores: torch.FloatTensor, greenlist_token_ids
+    ) -> torch.BoolTensor:
         green_tokens_mask = torch.zeros_like(scores)
         green_tokens_mask[-1, greenlist_token_ids] = 1
         final_mask = green_tokens_mask.bool()
@@ -76,9 +82,15 @@ class WatermarkLogitsProcessor(LogitsProcessor):
         scores[greenlist_mask] = scores[greenlist_mask] + greenlist_bias
         return scores
 
-    def __call__(self, input_ids: Union[List[int], torch.LongTensor], scores: torch.FloatTensor) -> torch.FloatTensor:
-        greenlist_ids = self._get_greenlist_ids(input_ids, scores.shape[-1], scores.device)
-        green_tokens_mask = self._calc_greenlist_mask(scores=scores, greenlist_token_ids=greenlist_ids)
+    def __call__(
+        self, input_ids: Union[List[int], torch.LongTensor], scores: torch.FloatTensor
+    ) -> torch.FloatTensor:
+        greenlist_ids = self._get_greenlist_ids(
+            input_ids, scores.shape[-1], scores.device
+        )
+        green_tokens_mask = self._calc_greenlist_mask(
+            scores=scores, greenlist_token_ids=greenlist_ids
+        )
 
         scores = self._bias_greenlist_logits(
             scores=scores, greenlist_mask=green_tokens_mask, greenlist_bias=self.delta