rename

server/text_generation_server/models/__init__.py

@@ -52,8 +52,8 @@ try:
     from text_generation_server.models.flash_llama import (
         FlashLlama,
     )
-    from text_generation_server.models.flash_golden_gate import (
+    from text_generation_server.models.flash_gemma import (
-        FlashGoldenGate,
+        FlashGemma,
     )
     from text_generation_server.models.flash_santacoder import (
         FlashSantacoderSharded,
@@ -315,9 +315,9 @@ def get_model(
                 dtype=dtype,
                 trust_remote_code=trust_remote_code,
             )
-    if model_type == "golden_gate":
+    if model_type == "gemma":
         if FLASH_ATTENTION:
-            return FlashGoldenGate(
+            return FlashGemma(
                 model_id,
                 revision,
                 quantize=quantize,
@@ -326,7 +326,9 @@ def get_model(
                 use_medusa=use_medusa,
             )
         elif sharded:
-            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Golden Gate"))
+            raise NotImplementedError(
+                FLASH_ATT_ERROR_MESSAGE.format("Sharded Golden Gate")
+            )
         else:
             return CausalLM(
                 model_id,

server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py

@@ -20,11 +20,16 @@
 
 import torch
 import torch.distributed
+import os
+from shutil import copyfile
 
 from torch import nn
 from transformers.activations import ACT2FN
 from transformers.configuration_utils import PretrainedConfig
 from typing import Optional, List, Tuple
+from tokenizers import processors
+from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
+from transformers.utils import logging
 
 from text_generation_server.utils import paged_attention, flash_attn
 from text_generation_server.utils.layers import (
@@ -37,8 +42,168 @@ from text_generation_server.utils.layers import (
     FastRMSNorm,
 )
 
+GemmaTokenizer = None
 
-class GoldenGateConfig(PretrainedConfig):
+logger = logging.get_logger(__name__)
+VOCAB_FILES_NAMES = {
+    "vocab_file": "tokenizer.model",
+    "tokenizer_file": "tokenizer.json",
+}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
+    },
+    "tokenizer_file": {
+        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
+    },
+}
+B_INST, E_INST = "[INST]", "[/INST]"
+B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
+
+# fmt: off
+DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
+answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
+ that your responses are socially unbiased and positive in nature.
+
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
+correct. If you don't know the answer to a question, please don't share false information."""
+
+
+# fmt: on
+
+
+class GemmaTokenizerFast(PreTrainedTokenizerFast):
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    slow_tokenizer_class = GemmaTokenizer
+    padding_side = "left"
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file=None,
+        tokenizer_file=None,
+        clean_up_tokenization_spaces=False,
+        unk_token="<unk>",
+        bos_token="<bos>",
+        eos_token="<eos>",
+        pad_token="<pad>",
+        add_bos_token=True,
+        add_eos_token=False,
+        use_default_system_prompt=False,
+        **kwargs,
+    ):
+        super().__init__(
+            vocab_file=vocab_file,
+            tokenizer_file=tokenizer_file,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            unk_token=unk_token,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            pad_token=pad_token,
+            add_bos_token=add_bos_token,
+            add_eos_token=add_eos_token,
+            use_default_system_prompt=use_default_system_prompt,
+            **kwargs,
+        )
+        self._add_bos_token = add_bos_token
+        self._add_eos_token = add_eos_token
+        self.update_post_processor()
+        self.use_default_system_prompt = use_default_system_prompt
+        self.vocab_file = vocab_file
+
+    @property
+    def can_save_slow_tokenizer(self) -> bool:
+        return os.path.isfile(self.vocab_file) if self.vocab_file else False
+
+    def update_post_processor(self):
+        """
+        Updates the underlying post processor with the current `bos_token` and `eos_token`.
+        """
+        bos = self.bos_token
+        bos_token_id = self.bos_token_id
+        if bos is None and self.add_bos_token:
+            raise ValueError("add_bos_token = True but bos_token = None")
+
+        eos = self.eos_token
+        eos_token_id = self.eos_token_id
+        if eos is None and self.add_eos_token:
+            raise ValueError("add_eos_token = True but eos_token = None")
+
+        single = f"{(bos + ':0 ') if self.add_bos_token else ''}$A:0{(' ' + eos + ':0') if self.add_eos_token else ''}"
+        pair = f"{single}{(' ' + bos + ':1') if self.add_bos_token else ''} $B:1{(' ' + eos + ':1') if self.add_eos_token else ''}"
+
+        special_tokens = []
+        if self.add_bos_token:
+            special_tokens.append((bos, bos_token_id))
+        if self.add_eos_token:
+            special_tokens.append((eos, eos_token_id))
+        self._tokenizer.post_processor = processors.TemplateProcessing(
+            single=single, pair=pair, special_tokens=special_tokens
+        )
+
+    @property
+    def add_eos_token(self):
+        return self._add_eos_token
+
+    @property
+    def add_bos_token(self):
+        return self._add_bos_token
+
+    @add_eos_token.setter
+    def add_eos_token(self, value):
+        self._add_eos_token = value
+        self.update_post_processor()
+
+    @add_bos_token.setter
+    def add_bos_token(self, value):
+        self._add_bos_token = value
+        self.update_post_processor()
+
+    def save_vocabulary(
+        self, save_directory: str, filename_prefix: Optional[str] = None
+    ) -> Tuple[str]:
+        if not self.can_save_slow_tokenizer:
+            raise ValueError(
+                "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
+                "tokenizer."
+            )
+
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        out_vocab_file = os.path.join(
+            save_directory,
+            (filename_prefix + "-" if filename_prefix else "")
+            + VOCAB_FILES_NAMES["vocab_file"],
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+
+        return (out_vocab_file,)
+
+    @property
+    # Copied from transformers.models.llama.tokenization_llama.GemmaTokenizer.default_chat_template
+    def default_chat_template(self):
+        raise NotImplementedError
+
+    # TODO ArthurZ let's rely on the template processor instead, refactor all fast tokenizers
+    # Copied from transformers.models.llama.tokenization_llama.GemmaTokenizer.build_inputs_with_special_tokens
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+        output = bos_token_id + token_ids_0 + eos_token_id
+
+        if token_ids_1 is not None:
+            output = output + bos_token_id + token_ids_1 + eos_token_id
+
+        return output
+
+
+class GemmaConfig(PretrainedConfig):
     def __init__(
         self,
         vocab_size=256128,
@@ -93,7 +258,8 @@ class GoldenGateConfig(PretrainedConfig):
             **kwargs,
         )
 
-class GoldenGateFastRMSNorm(FastRMSNorm):
+
+class GemmaFastRMSNorm(FastRMSNorm):
     @classmethod
     def load(cls, prefix, weights, eps=1e-6):
         weight = weights.get_tensor(f"{prefix}.weight") + 1
@@ -138,7 +304,7 @@ def _load_gqa(config, prefix: str, weights):
     )
 
 
-class FlashGoldenGateAttention(torch.nn.Module):
+class FlashGemmaAttention(torch.nn.Module):
     def __init__(
         self,
         prefix: str,
@@ -242,7 +408,7 @@ class FlashGoldenGateAttention(torch.nn.Module):
         return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
 
 
-class GoldenGateMLP(nn.Module):
+class GemmaMLP(nn.Module):
     def __init__(self, prefix, config, weights):
         super().__init__()
         act = config.hidden_act
@@ -251,9 +417,9 @@ class GoldenGateMLP(nn.Module):
             if "gelu" not in act
             else lambda x: torch.nn.functional.gelu(
                 x,
-                approximate="tanh"
+                approximate=(
-                if act in ["gelu_fast", "gelu_pytorch_tanh"]
+                    "tanh" if act in ["gelu_fast", "gelu_pytorch_tanh"] else "none"
-                else "none",
+                ),
             )
         )
         # Fuse gate and up proj
@@ -280,19 +446,19 @@ class GoldenGateMLP(nn.Module):
         return self.down_proj(self.act(gate_up_states[:, 0]) * gate_up_states[:, 1])
 
 
-class FlashGoldenGateLayer(nn.Module):
+class FlashGemmaLayer(nn.Module):
     def __init__(self, layer_id, config, weights):
         super().__init__()
         prefix = f"model.layers.{layer_id}"
-        self.self_attn = FlashGoldenGateAttention(
+        self.self_attn = FlashGemmaAttention(
             prefix=f"{prefix}.self_attn", config=config, weights=weights
         )
-        self.mlp = GoldenGateMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
+        self.mlp = GemmaMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
 
-        self.input_layernorm = GoldenGateFastRMSNorm.load(
+        self.input_layernorm = GemmaFastRMSNorm.load(
             prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
         )
-        self.post_attention_layernorm = GoldenGateFastRMSNorm.load(
+        self.post_attention_layernorm = GemmaFastRMSNorm.load(
             prefix=f"{prefix}.post_attention_layernorm",
             weights=weights,
             eps=config.rms_norm_eps,
@@ -336,14 +502,14 @@ class FlashGoldenGateLayer(nn.Module):
         return mlp_output, attn_res
 
 
-class FlashGoldenGateModel(torch.nn.Module):
+class FlashGemmaModel(torch.nn.Module):
     def __init__(self, config, weights):
         super().__init__()
 
         process_group = weights.process_group
         self.tp_rank = process_group.rank()
         self.tp_world_size = process_group.size()
-        embed_norm = config.hidden_size ** 0.5
+        embed_norm = config.hidden_size**0.5
         self.embed_tokens = TensorParallelEmbedding(
             prefix="model.embed_tokens", weights=weights
         )
@@ -351,7 +517,7 @@ class FlashGoldenGateModel(torch.nn.Module):
 
         self.layers = nn.ModuleList(
             [
-                FlashGoldenGateLayer(
+                FlashGemmaLayer(
                     layer_id,
                     config,
                     weights,
@@ -359,7 +525,7 @@ class FlashGoldenGateModel(torch.nn.Module):
                 for layer_id in range(config.num_hidden_layers)
             ]
         )
-        self.norm = GoldenGateFastRMSNorm.load(
+        self.norm = GemmaFastRMSNorm.load(
             prefix="model.norm", weights=weights, eps=config.rms_norm_eps
         )
 
@@ -408,11 +574,11 @@ class FlashGoldenGateModel(torch.nn.Module):
         return hidden_states
 
 
-class FlashGoldenGateForCausalLM(torch.nn.Module):
+class FlashGemmaForCausalLM(torch.nn.Module):
     def __init__(self, config, weights):
         super().__init__()
 
-        self.model = FlashGoldenGateModel(config, weights)
+        self.model = FlashGemmaModel(config, weights)
         self.lm_head = TensorParallelHead.load(
             config,
             prefix="model.embed_tokens" if config.tie_word_embeddings else "lm_head",

server/text_generation_server/models/custom_modeling/temp_tok.py

@@ -1,216 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import os
-from shutil import copyfile
-from typing import Optional, Tuple
-
-from tokenizers import processors
-
-from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
-from transformers.utils import logging
-from transformers.utils.versions import require_version
-
-
-require_version("tokenizers>=0.13.3")
-
-GoldenGateTokenizer = None
-
-logger = logging.get_logger(__name__)
-VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model", "tokenizer_file": "tokenizer.json"}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
-    },
-    "tokenizer_file": {
-        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
-    },
-}
-B_INST, E_INST = "[INST]", "[/INST]"
-B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
-
-# fmt: off
-DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
-answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
- that your responses are socially unbiased and positive in nature.
-If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
-correct. If you don't know the answer to a question, please don't share false information."""
-# fmt: on
-
-
-class GoldenGateTokenizerFast(PreTrainedTokenizerFast):
-    """
-    Construct a GoldenGate tokenizer. Based on byte-level Byte-Pair-Encoding.
-    This uses notably ByteFallback and no normalization.
-    ```python
-    >>> from transformers import GoldenGateTokenizerFast
-    >>> tokenizer = GoldenGateTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer")
-    >>> tokenizer.encode("Hello this is a test")
-    [1, 15043, 445, 338, 263, 1243]
-    ```
-    If you want to change the `bos_token` or the `eos_token`, make sure to specify them when initializing the model, or
-    call `tokenizer.update_post_processor()` to make sure that the post-processing is correctly done (otherwise the
-    values of the first token and final token of an encoded sequence will not be correct). For more details, checkout
-    [post-processors] (https://huggingface.co/docs/tokenizers/api/post-processors) documentation.
-    This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
-    refer to this superclass for more information regarding those methods.
-    Args:
-        vocab_file (`str`, *optional*):
-            [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .model extension) that
-            contains the vocabulary necessary to instantiate a tokenizer.
-        tokenizer_file (`str`, *optional*):
-            [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
-            contains everything needed to load the tokenizer.
-        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
-            Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
-            extra spaces.
-        unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<s>"`):
-            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
-        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`):
-            The end of sequence token.
-        add_bos_token (`bool`, *optional*, defaults to `True`):
-            Whether or not to add an `bos_token` at the start of sequences.
-        add_eos_token (`bool`, *optional*, defaults to `False`):
-            Whether or not to add an `eos_token` at the end of sequences.
-        use_default_system_prompt (`bool`, *optional*, defaults to `False`):
-            Whether or not the default system prompt for GoldenGate should be used.
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    slow_tokenizer_class = GoldenGateTokenizer
-    padding_side = "left"
-    model_input_names = ["input_ids", "attention_mask"]
-
-    def __init__(
-        self,
-        vocab_file=None,
-        tokenizer_file=None,
-        clean_up_tokenization_spaces=False,
-        unk_token="<unk>",
-        bos_token="<bos>",
-        eos_token="<eos>",
-        pad_token="<pad>",
-        add_bos_token=True,
-        add_eos_token=False,
-        use_default_system_prompt=False,
-        **kwargs,
-    ):
-        super().__init__(
-            vocab_file=vocab_file,
-            tokenizer_file=tokenizer_file,
-            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-            unk_token=unk_token,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            pad_token=pad_token,
-            add_bos_token=add_bos_token,
-            add_eos_token=add_eos_token,
-            use_default_system_prompt=use_default_system_prompt,
-            **kwargs,
-        )
-        self._add_bos_token = add_bos_token
-        self._add_eos_token = add_eos_token
-        self.update_post_processor()
-        self.use_default_system_prompt = use_default_system_prompt
-        self.vocab_file = vocab_file
-
-    @property
-    def can_save_slow_tokenizer(self) -> bool:
-        return os.path.isfile(self.vocab_file) if self.vocab_file else False
-
-    def update_post_processor(self):
-        """
-        Updates the underlying post processor with the current `bos_token` and `eos_token`.
-        """
-        bos = self.bos_token
-        bos_token_id = self.bos_token_id
-        if bos is None and self.add_bos_token:
-            raise ValueError("add_bos_token = True but bos_token = None")
-
-        eos = self.eos_token
-        eos_token_id = self.eos_token_id
-        if eos is None and self.add_eos_token:
-            raise ValueError("add_eos_token = True but eos_token = None")
-
-        single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
-        pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
-
-        special_tokens = []
-        if self.add_bos_token:
-            special_tokens.append((bos, bos_token_id))
-        if self.add_eos_token:
-            special_tokens.append((eos, eos_token_id))
-        self._tokenizer.post_processor = processors.TemplateProcessing(
-            single=single, pair=pair, special_tokens=special_tokens
-        )
-
-    @property
-    def add_eos_token(self):
-        return self._add_eos_token
-
-    @property
-    def add_bos_token(self):
-        return self._add_bos_token
-
-    @add_eos_token.setter
-    def add_eos_token(self, value):
-        self._add_eos_token = value
-        self.update_post_processor()
-
-    @add_bos_token.setter
-    def add_bos_token(self, value):
-        self._add_bos_token = value
-        self.update_post_processor()
-
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        if not self.can_save_slow_tokenizer:
-            raise ValueError(
-                "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
-                "tokenizer."
-            )
-
-        if not os.path.isdir(save_directory):
-            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
-            return
-        out_vocab_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
-        )
-
-        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
-            copyfile(self.vocab_file, out_vocab_file)
-
-        return (out_vocab_file,)
-
-    @property
-    # Copied from transformers.models.llama.tokenization_llama.GoldenGateTokenizer.default_chat_template
-    def default_chat_template(self):
-        raise NotImplementedError
-
-    # TODO ArthurZ let's rely on the template processor instead, refactor all fast tokenizers
-    # Copied from transformers.models.llama.tokenization_llama.GoldenGateTokenizer.build_inputs_with_special_tokens
-    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
-        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
-        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
-
-        output = bos_token_id + token_ids_0 + eos_token_id
-
-        if token_ids_1 is not None:
-            output = output + bos_token_id + token_ids_1 + eos_token_id
-
-        return output

server/text_generation_server/models/flash_gemma.py

@@ -3,12 +3,12 @@ import torch.distributed
 
 from opentelemetry import trace
 from typing import Optional
-from transformers import AutoTokenizer
 
 from text_generation_server.models import FlashCausalLM
-from text_generation_server.models.custom_modeling.flash_golden_gate_modeling import (
+from text_generation_server.models.custom_modeling.flash_gemma_modeling import (
-    FlashGoldenGateForCausalLM,
+    GemmaTokenizerFast,
-    GoldenGateConfig,
+    FlashGemmaForCausalLM,
+    GemmaConfig,
 )
 from text_generation_server.utils import (
     initialize_torch_distributed,
@@ -19,7 +19,7 @@ from text_generation_server.utils import (
 tracer = trace.get_tracer(__name__)
 
 
-class FlashGoldenGate(FlashCausalLM):
+class FlashGemma(FlashCausalLM):
     def __init__(
         self,
         model_id: str,
@@ -32,12 +32,11 @@ class FlashGoldenGate(FlashCausalLM):
         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
+            dtype = torch.bfloat16 if dtype is None else dtype
         else:
-            raise NotImplementedError("FlashGoldenGate is only available on GPU")
+            raise NotImplementedError("FlashGemma is only available on GPU")
 
-        from text_generation_server.models.custom_modeling.temp_tok import GoldenGateTokenizerFast
+        tokenizer = GemmaTokenizerFast.from_pretrained(
-        tokenizer = GoldenGateTokenizerFast.from_pretrained(
             model_id,
             revision=revision,
             padding_side="left",
@@ -47,7 +46,7 @@ class FlashGoldenGate(FlashCausalLM):
             from_slow=False,
         )
 
-        config = GoldenGateConfig.from_pretrained(
+        config = GemmaConfig.from_pretrained(
             model_id, revision=revision, trust_remote_code=trust_remote_code
         )
         config.quantize = quantize
@@ -59,18 +58,18 @@ class FlashGoldenGate(FlashCausalLM):
         if config.quantize in ["gptq", "awq"]:
             weights._set_gptq_params(model_id, revision)
 
-        model = FlashGoldenGateForCausalLM(config, weights)
+        model = FlashGemmaForCausalLM(config, weights)
         if use_medusa:
             from text_generation_server.utils.medusa import MedusaModel
             from huggingface_hub import hf_hub_download
             import json
             import os
             from pathlib import Path
-            
+
-            is_local_model = (Path(use_medusa).exists() and Path(use_medusa).is_dir()) or os.getenv(
+            is_local_model = (
-                "WEIGHTS_CACHE_OVERRIDE", None
+                Path(use_medusa).exists() and Path(use_medusa).is_dir()
-            ) is not None
+            ) or os.getenv("WEIGHTS_CACHE_OVERRIDE", None) is not None
-            
+
             if not is_local_model:
                 medusa_config = hf_hub_download(
                     use_medusa, revision=revision, filename="config.json"
@@ -81,7 +80,7 @@ class FlashGoldenGate(FlashCausalLM):
             else:
                 medusa_config = str(Path(use_medusa) / "config.json")
                 medusa_head = str(Path(use_medusa) / "medusa_lm_head.pt")
-                
+
             with open(medusa_config, "r") as f:
                 config = json.load(f)
             medusa_sf = medusa_head[: -len(".pt")] + ".safetensors"
@@ -92,7 +91,7 @@ class FlashGoldenGate(FlashCausalLM):
             model.lm_head = MedusaModel(config, weights, lm_head)
 
         torch.distributed.barrier(group=self.process_group)
-        super(FlashGoldenGate, self).__init__(
+        super(FlashGemma, self).__init__(
             model=model,
             tokenizer=tokenizer,
             num_layers=len(model.model.layers),