From 907906466a871182e98f30954039e577e633312e Mon Sep 17 00:00:00 2001
From: Nicolas Patry <patry.nicolas@protonmail.com>
Date: Wed, 18 Sep 2024 17:01:36 +0200
Subject: [PATCH] Working loading state.

---
 .../text_generation_server/models/__init__.py |   18 +
 .../models/custom_modeling/mllama.py          | 1233 +++++++++++++++++
 .../text_generation_server/models/idefics.py  |   24 +-
 3 files changed, 1266 insertions(+), 9 deletions(-)
 create mode 100644 server/text_generation_server/models/custom_modeling/mllama.py

diff --git a/server/text_generation_server/models/__init__.py b/server/text_generation_server/models/__init__.py
index e5e5aabb..4c64d89e 100644
--- a/server/text_generation_server/models/__init__.py
+++ b/server/text_generation_server/models/__init__.py
@@ -308,6 +308,12 @@ class ModelType(enum.Enum):
         "url": "https://huggingface.co/HuggingFaceM4/idefics-9b",
         "multimodal": True,
     }
+    MLLAMA = {
+        "type": "mllama",
+        "name": "Mllama",
+        "url": "https://huggingface.co/xxx/xx",
+        "multimodal": True,
+    }
 
 
 __GLOBALS = locals()
@@ -1095,6 +1101,18 @@ def get_model(
             )
         else:
             raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
+    if model_type == MLLAMA:
+        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("Mllama"))
     if model_type == IDEFICS2:
         if FLASH_ATTENTION:
             return VlmCausalLM(
diff --git a/server/text_generation_server/models/custom_modeling/mllama.py b/server/text_generation_server/models/custom_modeling/mllama.py
new file mode 100644
index 00000000..66feb902
--- /dev/null
+++ b/server/text_generation_server/models/custom_modeling/mllama.py
@@ -0,0 +1,1233 @@
+# coding=utf-8
+# Copyright 2024 the HuggingFace Inc. team. All rights reserved.
+#
+# 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.
+"""PyTorch Mllama model."""
+
+from typing import List, Optional, Tuple
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+import math
+
+from transformers.activations import ACT2FN
+import torch.nn.functional as F
+from text_generation_server.models.custom_modeling.vlm import (
+    load_text_model,
+)
+from text_generation_server.layers.attention import Seqlen
+from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
+
+from text_generation_server.layers.layernorm import (
+    FastRMSNorm,
+)
+from text_generation_server.layers.rotary import PositionRotaryEmbedding
+from text_generation_server.layers import (
+    TensorParallelColumnLinear,
+    TensorParallelEmbedding,
+    TensorParallelRowLinear,
+    SpeculativeHead,
+    FastLinear,
+)
+from text_generation_server.utils.weights import DefaultWeightsLoader, UnquantizedWeight
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->MllamaVision
+class MllamaVisionMLP(nn.Module):
+    def __init__(self, *, prefix, config, weights):
+        super().__init__()
+        self.config = config
+        self.activation_fn = ACT2FN[config.hidden_act]
+        self.fc1 = TensorParallelColumnLinear.load(
+            prefix=f"{prefix}.fc1", weights=weights, config=config, bias=True
+        )
+        self.fc2 = TensorParallelRowLinear.load(
+            prefix=f"{prefix}.fc2", weights=weights, config=config, bias=True
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+class MllamaVisionSdpaAttention(nn.Module):
+    def __init__(self, *, prefix, config, weights):
+        super().__init__()
+
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.attention_heads
+        self.head_dim = config.hidden_size // config.attention_heads
+
+        self.qkv_proj = TensorParallelColumnLinear.load_multi(
+            config,
+            prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
+            dim=0,
+            weights=weights,
+            bias=False,
+        )
+        self.o_proj = TensorParallelRowLinear.load(
+            config,
+            prefix=f"{prefix}.o_proj",
+            weights=weights,
+            bias=False,
+        )
+
+    def forward(
+        self,
+        hidden_state: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        qkv = self.qkv_proj(hidden_state)
+        query, key, value = qkv.split(
+            [
+                self.head_size * self.num_heads,
+                self.head_size * self.num_heads,
+                self.head_size * self.num_heads,
+            ],
+            dim=1,
+        )
+
+        batch_size, q_seq_len, _ = query.shape
+        _, kv_seq_len, _ = key.shape
+
+        query = query.view(batch_size, q_seq_len, self.num_heads, self.head_dim)
+        key = key.view(batch_size, kv_seq_len, self.num_heads, self.head_dim)
+        value = value.view(batch_size, kv_seq_len, self.num_heads, self.head_dim)
+
+        query = query.transpose(1, 2)
+        key = key.transpose(1, 2)
+        value = value.transpose(1, 2)
+
+        attn_output = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(batch_size, q_seq_len, -1)
+
+        output = self.o_proj(attn_output)
+        return output
+
+
+class MllamaVisionEncoderLayer(nn.Module):
+    def __init__(self, *, prefix, config, weights, is_gated: bool):
+        super().__init__()
+
+        self.hidden_size = config.hidden_size
+        self.num_attention_heads = config.attention_heads
+        self.is_gated = is_gated
+        self.intermediate_size = config.intermediate_size
+
+        self.self_attn = MllamaVisionSdpaAttention(
+            prefix=f"{prefix}.self_attn", config=config, weights=weights
+        )
+        self.mlp = MllamaVisionMLP(
+            prefix=f"{prefix}.mlp", config=config, weights=weights
+        )
+
+        self.input_layernorm = nn.LayerNorm.load(
+            prefix=f"{prefix}.input_layernorm", weights=weights, eps=1e-05
+        )
+        self.post_attention_layernorm = nn.LayerNorm.load(
+            prefix=f"{prefix}.post_attention_layernorm", weights=weights, eps=1e-05
+        )
+
+        # there used to be an if else here, no code path
+        if is_gated:
+            self.gate_attn = nn.Parameter(
+                weights.get_tensor(f"{prefix}.gate_attn"), requires_grad=False
+            )
+            self.gate_ffn = nn.Parameter(
+                weights.get_tensor(f"{prefix}.gate_attn"), requires_grad=False
+            )
+
+    def forward(
+        self,
+        hidden_state: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ):
+        # Self Attention
+        residual = hidden_state
+        hidden_state = self.input_layernorm(hidden_state)
+        hidden_state, attn_weights = self.self_attn(
+            hidden_state, attention_mask=attention_mask
+        )
+        gate_attn = 1 if not self.is_gated else self.gate_attn.tanh()
+        hidden_state = residual + gate_attn * hidden_state
+
+        # Feed forward
+        residual = hidden_state
+        hidden_state = self.post_attention_layernorm(hidden_state)
+        hidden_state = self.mlp(hidden_state)
+        gate_ffn = 1 if not self.is_gated else self.gate_ffn.tanh()
+        hidden_state = residual + gate_ffn * hidden_state
+        return hidden_state
+
+
+class MllamaVisionEncoder(nn.Module):
+    def __init__(self, *, prefix, config, weights, is_gated: bool, num_layers: int):
+        super().__init__()
+        self.config = config
+        self.layers = [
+            MllamaVisionEncoderLayer(
+                prefix=f"{prefix}.layers.{i}",
+                config=config,
+                weights=weights,
+                is_gated=is_gated,
+            )
+            for i in range(num_layers)
+        ]
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ):
+        for encoder_layer in self.layers:
+            layer_outputs = encoder_layer(
+                hidden_states,
+                attention_mask,
+            )
+
+            hidden_states = layer_outputs[0]
+
+        return hidden_states
+
+
+class MllamaPrecomputedAspectRatioEmbedding(nn.Module):
+    def __init__(self, *, prefix, config, weights):
+        super().__init__()
+        self.max_num_tiles = config.max_num_tiles
+        self.hidden_size = config.hidden_size
+        self.max_aspect_ratio_id = config.max_aspect_ratio_id
+
+        self.embedding = TensorParallelEmbedding(
+            prefix=f"{prefix}.embedding", weights=weights
+        )
+        self.gate = nn.Parameter(
+            weights.get_tensor(f"{prefix}.gate"), requires_grad=False
+        )
+
+    def forward(
+        self, hidden_state: torch.Tensor, aspect_ratio_ids: torch.Tensor
+    ) -> torch.Tensor:
+        embeddings = self.embedding(aspect_ratio_ids)
+        embeddings = embeddings.reshape(-1, self.max_num_tiles, 1, self.hidden_size)
+
+        # Always gated.
+        embeddings = embeddings * self.gate.tanh()
+
+        hidden_state = hidden_state + embeddings
+        return hidden_state
+
+
+class MllamaPrecomputedPositionEmbedding(nn.Module):
+    def __init__(self, *, prefix, config, weights):
+        super().__init__()
+        self.max_num_tiles = config.max_num_tiles
+        self.max_aspect_ratio_id = config.max_aspect_ratio_id
+        self.num_patches = (config.image_size // config.patch_size) ** 2 + 1
+        self.hidden_size = config.hidden_size
+        self.scale = config.hidden_size**-0.5
+
+        self.gate = nn.Parameter(torch.zeros(1))
+
+        # position embedding
+        self.embedding = nn.Parameter(
+            weights.get_tensor(f"{prefix}.embedding"), requires_grad=False
+        )
+        self.tile_embedding = TensorParallelEmbedding(
+            prefix=f"{prefix}.tile_embedding", weights=weights
+        )
+
+    def forward(
+        self, hidden_state: torch.Tensor, aspect_ratio_ids: torch.Tensor
+    ) -> torch.Tensor:
+        # position embeddings
+        gated_position_embedding = (1 - self.gate.tanh()) * self.embedding
+        hidden_state = hidden_state + gated_position_embedding.view(
+            1, 1, self.num_patches, self.hidden_size
+        )
+
+        # precomputed tile position embeddings
+        tile_position_embedding = self.tile_embedding(aspect_ratio_ids)
+        batch_size = hidden_state.shape[0]
+        tile_position_embedding = tile_position_embedding.reshape(
+            batch_size, self.max_num_tiles, self.num_patches, self.hidden_size
+        )
+        gated_tile_position_embedding = self.gate.tanh() * tile_position_embedding
+        hidden_state = hidden_state + gated_tile_position_embedding
+
+        return hidden_state
+
+
+class MllamaVisionModel(nn.Module):
+    def __init__(self, *, prefix, config, weights):
+        super().__init__()
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+        self.max_num_tiles = config.max_num_tiles
+        self.hidden_size = config.hidden_size
+        self.in_channels = config.in_channels
+        self.intermediate_layers_indices = config.intermediate_layers_indices
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2 + 1
+        self.scale = config.hidden_size**-0.5
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=config.in_channels,
+            out_channels=self.hidden_size,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            padding="valid",
+            bias=False,
+        )
+        self.patch_embedding.weight = nn.Parameter(
+            weights.get_tensor(f"{prefix}.patch_embedding.weight"), requires_grad=False
+        )
+
+        self.class_embedding = nn.Parameter(
+            weights.get_tensor(f"{prefix}.class_embedding"), requires_grad=False
+        )
+
+        self.gated_positional_embedding = MllamaPrecomputedPositionEmbedding(
+            prefix=f"{prefix}.gated_positional_embedding",
+            config=config,
+            weights=weights,
+        )
+
+        self.pre_tile_positional_embedding = MllamaPrecomputedAspectRatioEmbedding(
+            prefix=f"{prefix}.pre_tile_positional_embedding",
+            config=config,
+            weights=weights,
+        )
+        self.post_tile_positional_embedding = MllamaPrecomputedAspectRatioEmbedding(
+            prefix=f"{prefix}.post_tile_positional_embedding",
+            config=config,
+            weights=weights,
+        )
+
+        ## layer norms
+        self.layernorm_pre = nn.LayerNorm.load(
+            prefix=f"{prefix}.layernorm_pre",
+            weights=weights,
+            # torch default
+            eps=1e-05,
+        )
+        self.layernorm_post = nn.LayerNorm.load(
+            prefix=f"{prefix}.layernorm_post",
+            weights=weights,
+            # torch default
+            eps=1e-05,
+        )
+
+        ## encoders
+        self.transformer = MllamaVisionEncoder(
+            prefix=f"{prefix}.transformer",
+            config=config,
+            weights=weights,
+            is_gated=False,
+            num_layers=config.num_hidden_layers,
+        )
+        self.global_transformer = MllamaVisionEncoder(
+            prefix=f"{prefix}.global_transformer",
+            config=config,
+            weights=weights,
+            is_gated=True,
+            num_layers=config.num_global_layers,
+        )
+
+    def apply_class_embedding(self, hidden_state: torch.Tensor) -> torch.Tensor:
+        batch_size, _, hidden_size = hidden_state.shape
+        class_embedding = self.class_embedding.expand(batch_size, 1, hidden_size)
+        hidden_state = torch.cat([class_embedding, hidden_state], dim=1)
+        return hidden_state
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        aspect_ratio_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+    ) -> torch.Tensor:
+        batch_size, num_concurrent_media, num_tiles, num_channels, height, width = (
+            pixel_values.shape
+        )
+
+        pixel_values = pixel_values.reshape(
+            batch_size * num_concurrent_media * num_tiles, num_channels, height, width
+        )
+        aspect_ratio_ids = aspect_ratio_ids.reshape(
+            batch_size * num_concurrent_media, -1
+        )
+
+        # patch embedding
+        patch_embeds = self.patch_embedding(pixel_values.to(self.dtype).to(self.device))
+        hidden_state = patch_embeds.flatten(2).transpose(1, 2)
+
+        # tile embeddings
+        _, num_patches, dim = hidden_state.shape
+        hidden_state = hidden_state.reshape(
+            batch_size * num_concurrent_media, num_tiles, -1, dim
+        )
+        hidden_state = self.pre_tile_positional_embedding(
+            hidden_state, aspect_ratio_ids
+        )
+
+        # apply cls token
+        hidden_state = hidden_state.reshape(
+            batch_size * num_concurrent_media * num_tiles, num_patches, dim
+        )
+        hidden_state = self.apply_class_embedding(hidden_state)
+        num_patches += 1
+
+        # apply position embeddings
+        hidden_state = hidden_state.reshape(
+            batch_size * num_concurrent_media, num_tiles, num_patches, dim
+        )
+        hidden_state = self.gated_positional_embedding(hidden_state, aspect_ratio_ids)
+
+        # apply encoder
+        hidden_state = self.layernorm_pre(hidden_state)
+
+        # Compute the number of tokens to pad
+        num_padding_patches = (8 - (hidden_state.shape[-2] % 8)) % 8
+        # Compute padding tuple for pad function
+        padding = (
+            0,
+            0,
+            0,
+            num_padding_patches,
+        )  # (pad_left, pad_right, pad_left for dim -2, pad_right for dim -2)
+        # Pad the tensor
+        hidden_state = F.pad(hidden_state, padding, mode="constant", value=0)
+        slice_index = -num_padding_patches if num_padding_patches > 0 else None
+
+        if attention_mask is not None:
+            attention_mask = attention_mask.reshape(
+                batch_size * num_concurrent_media, -1
+            )
+            attention_mask = _prepare_aspect_ratio_attention_mask(
+                aspect_ratio_mask=attention_mask,
+                num_patches=self.num_patches,
+                target_length=hidden_state.shape[2],
+                dtype=self.dtype,
+            )
+
+        hidden_state = hidden_state.view(batch_size * num_concurrent_media, -1, dim)
+        output = self.transformer(
+            hidden_state,
+            attention_mask=attention_mask,
+            output_hidden_states=True,
+        )
+        hidden_state, all_intermediate_hidden_states = output[0], output[1]
+        intermediate_hidden_states = [
+            hidden_state
+            for idx, hidden_state in enumerate(all_intermediate_hidden_states)
+            if idx in self.intermediate_layers_indices
+        ]
+        intermediate_hidden_states = torch.stack(intermediate_hidden_states, dim=-1)
+
+        # apply global encoder
+        hidden_state = self.layernorm_post(hidden_state)
+        hidden_state = hidden_state.reshape(
+            batch_size * num_concurrent_media,
+            num_tiles,
+            num_patches + num_padding_patches,
+            dim,
+        )
+        hidden_state = self.post_tile_positional_embedding(
+            hidden_state, aspect_ratio_ids
+        )
+        hidden_state = hidden_state.reshape(
+            batch_size * num_concurrent_media,
+            num_tiles * (num_patches + num_padding_patches),
+            dim,
+        )
+        hidden_state = self.global_transformer(
+            hidden_state, attention_mask=attention_mask
+        )[0]
+        hidden_state = hidden_state.reshape(
+            batch_size * num_concurrent_media,
+            num_tiles,
+            num_patches + num_padding_patches,
+            dim,
+        )
+        hidden_state = hidden_state[:, :, :slice_index]
+
+        # adding intermediate layer outputs
+        hidden_state = hidden_state.reshape(
+            batch_size, num_concurrent_media, num_tiles, num_patches, dim
+        )
+        intermediate_hidden_states = intermediate_hidden_states.reshape(
+            batch_size * num_concurrent_media,
+            num_tiles,
+            num_patches + num_padding_patches,
+            -1,
+        )
+        intermediate_hidden_states = intermediate_hidden_states[:, :, :slice_index]
+        intermediate_hidden_states = intermediate_hidden_states.reshape(
+            batch_size, num_concurrent_media, num_tiles, num_patches, -1
+        )
+        hidden_state = torch.cat([hidden_state, intermediate_hidden_states], dim=-1)
+        return hidden_state
+
+
+class MllamaTextCrossAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, *, prefix, config, weights):
+        super().__init__()
+        self.config = config
+        self.num_heads = self.config.num_attention_heads
+        self.num_key_value_heads = self.config.num_key_value_heads
+        self.dropout = config.dropout
+        self.hidden_size = config.hidden_size
+        self.head_dim = config.hidden_size // self.num_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+
+        self.qkv_proj = TensorParallelColumnLinear.load_multi(
+            config,
+            prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
+            dim=0,
+            weights=weights,
+            bias=False,
+        )
+        self.o_proj = TensorParallelRowLinear.load(
+            config,
+            prefix=f"{prefix}.o_proj",
+            weights=weights,
+            bias=False,
+        )
+
+        self.q_norm = FastRMSNorm.load(
+            prefix=f"{prefix}.q_norm", weights=weights, eps=config.rms_norm_eps
+        )
+        self.k_norm = FastRMSNorm.load(
+            prefix=f"{prefix}.k_norm", weights=weights, eps=config.rms_norm_eps
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cross_attention_states: Optional[torch.Tensor] = None,
+        past_key_value=None,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+        use_cache: bool = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        """Input shape: Batch x Time x Channel"""
+        bsz, q_len, _ = hidden_states.size()
+        query_states = self.q_proj(hidden_states)
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        query_states = self.q_norm(query_states)
+
+        if cross_attention_states is not None:
+            key_states = self.k_proj(cross_attention_states)
+            value_states = self.v_proj(cross_attention_states)
+            key_states = key_states.view(
+                bsz, -1, self.num_key_value_heads, self.head_dim
+            ).transpose(1, 2)
+            value_states = value_states.view(
+                bsz, -1, self.num_key_value_heads, self.head_dim
+            ).transpose(1, 2)
+            key_states = repeat_kv(key_states, self.num_key_value_groups)
+            value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+            key_states = self.k_norm(key_states)
+            if past_key_value is not None:
+                # if we have a new image + new tokens, we only computed key_states on that new image
+                # we still update the cross key states, past_image, new_image. And use it!
+                key_states, value_states = past_key_value.update(
+                    key_states,
+                    value_states,
+                    self.layer_idx,
+                    {"cache_position": cache_position},
+                )
+        elif cache_position[0] != 0:
+            key_states, value_states = (
+                past_key_value.key_cache[self.layer_idx],
+                past_key_value.value_cache[self.layer_idx],
+            )
+        else:
+            raise ValueError(
+                "Cross attention layer can't find neither `cross_attn_states` nor cached values for key/values!"
+            )
+
+        attn_weights = torch.matmul(
+            query_states, key_states.transpose(2, 3)
+        ) / math.sqrt(self.head_dim)
+
+        if attention_mask is not None:  # no matter the length, we just slice it
+            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+            attn_weights = attn_weights + causal_mask
+
+        attn_weights = nn.functional.softmax(
+            attn_weights, dim=-1, dtype=torch.float32
+        ).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(
+            attn_weights, p=self.dropout, training=self.training
+        )
+        attn_output = torch.matmul(attn_weights, value_states)
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, -1)
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+# Copied from transformers.models.gemma2.modeling_gemma2.Gemma2MLP with Gemma2->MllamaText
+class MllamaTextMLP(nn.Module):
+    def __init__(self, *, prefix, config, weights):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_up_proj = TensorParallelColumnLinear.load_multi(
+            config,
+            prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
+            weights=weights,
+            dim=0,
+            bias=False,
+        )
+        self.down_proj = TensorParallelRowLinear.load(
+            config,
+            prefix=f"{prefix}.down_proj",
+            weights=weights,
+            bias=False,
+        )
+        self.act_fn = ACT2FN[config.hidden_activation]
+
+    def forward(self, x):
+        gate_up_states = self.gate_up_proj(x)
+        gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
+        return self.down_proj(self.act(gate_up_states[:, 0]) * gate_up_states[:, 1])
+
+
+class MllamaCrossAttentionDecoderLayer(torch.nn.Module):
+    """Cross-attention transformer block with tanh-gated attention and feedforward."""
+
+    def __init__(self, *, prefix, config, weights) -> None:
+        super().__init__()
+        self.cross_attn = MllamaTextCrossAttention(
+            prefix=f"{prefix}.cross_attn", config=config, weights=weights
+        )
+
+        self.input_layernorm = FastRMSNorm.load(
+            prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
+        )
+        self.cross_attn_attn_gate = torch.nn.Parameter(
+            weights.get_tensor(f"{prefix}.cross_attn_attn_gate"), requires_grad=False
+        )
+
+        self.mlp = MllamaTextMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
+        self.post_attention_layernorm = FastRMSNorm.load(
+            prefix=f"{prefix}.post_attention_layernorm",
+            weights=weights,
+            eps=config.rms_norm_eps,
+        )
+        self.cross_attn_mlp_gate = torch.nn.Parameter(
+            weights.get_tensor(f"{prefix}.cross_attn_mlp_gate"), requires_grad=False
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cross_attention_states: torch.Tensor,
+        cross_attention_mask: torch.Tensor,
+        attention_mask: torch.Tensor,
+        full_text_row_masked_out_mask: Tuple[torch.Tensor, torch.Tensor],
+        past_key_value=None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        hidden_states, attn_weights, past_key_value = self.cross_attn(
+            hidden_states=hidden_states,
+            attention_mask=cross_attention_mask,
+            cross_attention_states=cross_attention_states,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            cache_position=cache_position,
+        )
+        hidden_states = residual + self.cross_attn_attn_gate.tanh() * hidden_states
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        if full_text_row_masked_out_mask is not None:
+            hidden_states = full_text_row_masked_out_mask[:, 0] * hidden_states  # type: ignore
+        hidden_states = residual + self.cross_attn_mlp_gate.tanh() * hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (attn_weights,)
+
+        if use_cache:
+            outputs += (past_key_value,)
+
+        return outputs
+
+
+class MllamaTextSelfAttention(nn.Module):
+    def __init__(self, *, prefix, config, weights):
+        super().__init__()
+        self.config = config
+        self.num_heads = config.num_attention_heads
+        self.dropout = config.dropout
+        self.hidden_size = config.hidden_size
+        self.num_key_value_heads = config.num_key_value_heads
+        self.head_dim = config.hidden_size // self.num_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+
+        self.qkv_proj = TensorParallelColumnLinear.load_multi(
+            config,
+            prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
+            dim=0,
+            weights=weights,
+            bias=False,
+        )
+        self.o_proj = TensorParallelRowLinear.load(
+            config,
+            prefix=f"{prefix}.o_proj",
+            weights=weights,
+            bias=False,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        position_embeddings: torch.Tensor,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        past_key_value=None,
+        cache_position=None,
+        **kwargs,
+    ):
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin
+        )
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        causal_mask = attention_mask
+        if attention_mask is not None:
+            causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and causal_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+        # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+        is_causal = True if causal_mask is None and q_len > 1 else False
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=causal_mask,
+            dropout_p=self.dropout if self.training else 0.0,
+            is_causal=is_causal,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, -1)
+
+        attn_output = self.o_proj(attn_output)
+        return attn_output, None, past_key_value
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaDecoderLayer with LlamaDecoder->MllamaSelfAttentionDecoder, Llama->MllamaText, LLAMA->MLLAMA_TEXT
+class MllamaSelfAttentionDecoderLayer(nn.Module):
+    def __init__(self, *, prefix, config, weights):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = MllamaTextSelfAttention(
+            prefix=f"{prefix}.self_attn", config=config, weights=weights
+        )
+
+        self.mlp = MllamaTextMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
+        self.input_layernorm = FastRMSNorm.load(
+            prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
+        )
+        self.post_attention_layernorm = FastRMSNorm.load(
+            prefix=f"{prefix}.post_attention_layernorm",
+            weights=weights,
+            eps=config.rms_norm_eps,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value=None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[
+            Tuple[torch.Tensor, torch.Tensor]
+        ] = None,  # will become mandatory in v4.45
+        **kwargs,
+    ) -> Tuple[
+        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+    ]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+                Indices depicting the position of the input sequence tokens in the sequence
+            position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
+                Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
+                with `head_dim` being the embedding dimension of each attention head.
+            kwargs (`dict`, *optional*):
+                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+                into the model
+        """
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+class MllamaTextModel(nn.Module):
+    def __init__(self, *, prefix, config, weights):
+        super().__init__()
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.embed_tokens = TensorParallelEmbedding(
+            prefix=f"{prefix}.embed_tokens", weights=weights
+        )
+        self.cross_attention_layers = config.cross_attention_layers
+
+        self.layers = []
+        for layer_idx in range(config.num_hidden_layers):
+            if layer_idx in self.cross_attention_layers:
+                self.layers.append(
+                    MllamaCrossAttentionDecoderLayer(
+                        prefix=f"{prefix}.layers.{layer_idx}",
+                        config=config,
+                        weights=weights,
+                    )
+                )
+            else:
+                self.layers.append(
+                    MllamaSelfAttentionDecoderLayer(
+                        prefix=f"{prefix}.layers.{layer_idx}",
+                        config=config,
+                        weights=weights,
+                    )
+                )
+
+        # TODO Should we use this slow norm ?
+        # self.norm = MllamaTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.norm = FastRMSNorm.load(
+            prefix=f"{prefix}.norm",
+            weights=weights,
+            eps=config.rms_norm_eps,
+        )
+        # TODO Anything specific ?
+        head_size = config.hidden_size // config.num_attention_heads
+        self.rotary_emb = PositionRotaryEmbedding.static(
+            config=config,
+            dim=head_size,
+            base=config.rope_theta,
+            device=weights.device,
+        )
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        cross_attention_states: Optional[torch.FloatTensor] = None,
+        cross_attention_mask: Optional[torch.Tensor] = None,
+        full_text_row_masked_out_mask: Optional[
+            Tuple[torch.Tensor, torch.Tensor]
+        ] = None,
+        past_key_values=None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ):
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+            )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        hidden_states = inputs_embeds
+
+        if cache_position is None:
+            past_seen_tokens = (
+                past_key_values.get_seq_length() if past_key_values is not None else 0
+            )
+            cache_position = torch.arange(
+                past_seen_tokens,
+                past_seen_tokens + inputs_embeds.shape[1],
+                device=inputs_embeds.device,
+            )
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        # causal_mask = self._update_causal_mask(
+        #     attention_mask,
+        #     inputs_embeds,
+        #     cache_position,
+        #     past_key_values,
+        # )
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # decoder layers
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if (
+                idx in self.cross_attention_layers
+                and cross_attention_states is None
+                and (
+                    past_key_values is None
+                    or (
+                        past_key_values is not None
+                        and past_key_values.get_seq_length(idx) == 0
+                    )
+                )
+            ):
+                continue
+
+            layer_outputs = decoder_layer(
+                hidden_states,
+                cross_attention_states=cross_attention_states,
+                cross_attention_mask=cross_attention_mask,
+                attention_mask=causal_mask,
+                full_text_row_masked_out_mask=full_text_row_masked_out_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings,
+            )
+
+            hidden_states = layer_outputs
+
+        hidden_states = self.norm(hidden_states)
+
+        return hidden_states
+
+    # def _update_causal_mask(
+    #     self,
+    #     attention_mask: torch.Tensor,
+    #     input_tensor: torch.Tensor,
+    #     cache_position: torch.Tensor,
+    #     past_key_values,
+    # ):
+    #     if self.config._attn_implementation == "flash_attention_2":
+    #         if attention_mask is not None and 0.0 in attention_mask:
+    #             return attention_mask
+    #         return None
+
+    #     # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+    #     # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+    #     # to infer the attention mask.
+    #     past_seen_tokens = (
+    #         past_key_values.get_seq_length() if past_key_values is not None else 0
+    #     )
+    #     using_static_cache = isinstance(past_key_values, StaticCache)
+
+    #     # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+    #     # TODO: we have only SDPA currently and there's a bug when attn-bias is passed. Need to add eager attn and return the line
+    #     # self.config._attn_implementation == "sdpa" and
+    #     if (
+    #         self.config._attn_implementation == "sdpa"
+    #         and not using_static_cache
+    #         and not output_attentions
+    #     ):
+    #         if AttentionMaskConverter._ignore_causal_mask_sdpa(
+    #             attention_mask,
+    #             inputs_embeds=input_tensor,
+    #             past_key_values_length=past_seen_tokens,
+    #             is_training=self.training,
+    #         ):
+    #             return None
+
+    #     dtype, device = input_tensor.dtype, input_tensor.device
+    #     min_dtype = torch.finfo(dtype).min
+    #     sequence_length = input_tensor.shape[1]
+    #     if using_static_cache:
+    #         target_length = past_key_values.get_max_length()
+    #     else:
+    #         target_length = (
+    #             attention_mask.shape[-1]
+    #             if isinstance(attention_mask, torch.Tensor)
+    #             else past_seen_tokens + sequence_length + 1
+    #         )
+
+    #     # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+    #     causal_mask = _prepare_4d_causal_attention_mask_with_cache_position(
+    #         attention_mask,
+    #         sequence_length=sequence_length,
+    #         target_length=target_length,
+    #         dtype=dtype,
+    #         device=device,
+    #         min_dtype=min_dtype,
+    #         cache_position=cache_position,
+    #         batch_size=input_tensor.shape[0],
+    #     )
+
+    #     if (
+    #         self.config._attn_implementation == "sdpa"
+    #         and attention_mask is not None
+    #         and attention_mask.device.type == "cuda"
+    #         and not output_attentions
+    #     ):
+    #         # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+    #         # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+    #         # Details: https://github.com/pytorch/pytorch/issues/110213
+    #         causal_mask = AttentionMaskConverter._unmask_unattended(
+    #             causal_mask, min_dtype
+    #         )
+
+    #     return causal_mask
+
+
+class MllamaForCausalLM(nn.Module):
+    def __init__(self, *, prefix, config, weights):
+        super().__init__()
+        self.vocab_size = config.vocab_size
+        self.model = MllamaTextModel(
+            prefix=f"{prefix}.model", config=config, weights=weights
+        )
+        self.lm_head = SpeculativeHead.load(
+            prefix=f"{prefix}.lm_head",
+            config=config,
+            weights=weights,
+        )
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        cross_attention_states: Optional[torch.LongTensor] = None,
+        cross_attention_mask: Optional[torch.LongTensor] = None,
+        full_text_row_masked_out_mask: Optional[
+            Tuple[torch.Tensor, torch.Tensor]
+        ] = None,
+        past_key_values=None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        num_logits_to_keep: int = 0,
+    ):
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            cross_attention_states=cross_attention_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            cross_attention_mask=cross_attention_mask,
+            full_text_row_masked_out_mask=full_text_row_masked_out_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            cache_position=cache_position,
+        )
+
+        hidden_states = outputs
+        # if lm_head_indices is not None:
+        #     hidden_states = hidden_states[lm_head_indices]
+        logits, speculative_logits = self.lm_head(hidden_states)
+        return logits
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        cache_position=None,
+        position_ids=None,
+        use_cache=True,
+        num_logits_to_keep=None,
+        **kwargs,
+    ):
+        # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
+        # Exception 1: when passing input_embeds, input_ids may be missing entries
+        # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
+        if past_key_values is not None:
+            if inputs_embeds is not None:  # Exception 1
+                input_ids = input_ids[:, -cache_position.shape[0] :]
+            elif (
+                input_ids.shape[1] != cache_position.shape[0]
+            ):  # Default case (the "else", a no op, is Exception 2)
+                input_ids = input_ids[:, cache_position]
+
+        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:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+                # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
+                position_ids = position_ids.clone(memory_format=torch.contiguous_format)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and cache_position[0] == 0:
+            model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
+        else:
+            # The clone here is for the same reason as for `position_ids`.
+            model_inputs = {
+                "input_ids": input_ids.clone(memory_format=torch.contiguous_format),
+                "inputs_embeds": None,
+            }
+
+        if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
+            if model_inputs["inputs_embeds"] is not None:
+                batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
+                device = model_inputs["inputs_embeds"].device
+            else:
+                batch_size, sequence_length = model_inputs["input_ids"].shape
+                device = model_inputs["input_ids"].device
+
+            dtype = self.lm_head.weight.dtype
+            min_dtype = torch.finfo(dtype).min
+
+            attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
+                attention_mask,
+                sequence_length=sequence_length,
+                target_length=past_key_values.get_max_length(),
+                dtype=dtype,
+                device=device,
+                min_dtype=min_dtype,
+                cache_position=cache_position,
+                batch_size=batch_size,
+            )
+
+        if num_logits_to_keep is not None:
+            model_inputs["num_logits_to_keep"] = num_logits_to_keep
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "use_cache": use_cache,
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+
+class MllamaForConditionalGeneration(nn.Module):
+    def __init__(self, prefix, config, weights):
+        super().__init__()
+        config.vision_config.quantize = None
+        config.vision_config.speculator = config.speculator
+        config.text_config.quantize = config.quantize
+        config.text_config.speculator = config.speculator
+        self.vision_model = MllamaVisionModel(
+            prefix="vision_model", config=config.vision_config, weights=weights
+        )
+        self.language_model = MllamaForCausalLM(
+            prefix="language_model", config=config.text_config, weights=weights
+        )
+        self.multi_modal_projector = FastLinear.load(
+            prefix="multi_modal_projector", config=config, weights=weights, bias=True
+        )
+        self.config = config
diff --git a/server/text_generation_server/models/idefics.py b/server/text_generation_server/models/idefics.py
index 9058cb96..06cd501e 100644
--- a/server/text_generation_server/models/idefics.py
+++ b/server/text_generation_server/models/idefics.py
@@ -4,14 +4,13 @@ import torch.distributed
 from typing import Optional
 
 
-from text_generation_server.models.custom_modeling.idefics_config import IdeficsConfig
-from text_generation_server.models.custom_modeling.idefics_processing import (
-    IdeficsProcessor,
-)
-from transformers import LlamaTokenizerFast
+from transformers import AutoConfig, AutoProcessor, AutoTokenizer
 from text_generation_server.models.custom_modeling.idefics_modeling import (
     IdeficsForVisionText2Text,
 )
+from text_generation_server.models.custom_modeling.mllama import (
+    MllamaForConditionalGeneration,
+)
 from text_generation_server.models.idefics_causal_lm import IdeficsCausalLM
 from text_generation_server.utils import (
     initialize_torch_distributed,
@@ -53,7 +52,7 @@ class IDEFICSSharded(IdeficsCausalLM):
             dtype = torch.float32 if dtype is None else dtype
         self.device, self.dtype = device, dtype
 
-        config = IdeficsConfig.from_pretrained(
+        config = AutoConfig.from_pretrained(
             model_id,
             revision=revision,
             trust_remote_code=trust_remote_code,
@@ -62,14 +61,14 @@ class IDEFICSSharded(IdeficsCausalLM):
         config.speculator = speculator
         config.vision_config.quantize = quantize
 
-        tokenizer = LlamaTokenizerFast.from_pretrained(
+        tokenizer = AutoTokenizer.from_pretrained(
             model_id,
             revision=revision,
             padding_side="left",
             truncation_side="left",
             trust_remote_code=trust_remote_code,
         )
-        self.processor = IdeficsProcessor.from_pretrained(
+        self.processor = AutoProcessor.from_pretrained(
             model_id,
             revision=revision,
             padding_side="left",
@@ -90,7 +89,14 @@ class IDEFICSSharded(IdeficsCausalLM):
             weights_loader=weights_loader,
         )
 
-        model = IdeficsForVisionText2Text(config, weights)
+        if config.model_type == "idefics":
+            model = IdeficsForVisionText2Text(config, weights)
+        elif config.model_type == "mllama":
+            model = MllamaForConditionalGeneration(
+                prefix="", config=config, weights=weights
+            )
+        else:
+            raise RuntimeError(f"Unsupported model type {config.model_type}")
 
         torch.distributed.barrier(group=self.process_group)
         super(IdeficsCausalLM, self).__init__(