Full fine-tuning on JDocQA - 2 epochs

README.md

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+---
+library_name: transformers
+tags:
+- trl
+- sft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
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+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
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+## How to Get Started with the Model
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+## Training Details
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+#### Preprocessing [optional]
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+
+#### Training Hyperparameters
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+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+## Evaluation
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+### Testing Data, Factors & Metrics
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+#### Summary
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+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
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config.json

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+{
+  "architectures": [
+    "Lfm2VlForConditionalGeneration"
+  ],
+  "auto_map": {
+    "AutoConfig": "modeling_lfm2_vl.Lfm2VlConfig",
+    "AutoModelForImageTextToText": "modeling_lfm2_vl.Lfm2VlForConditionalGeneration"
+  },
+  "do_image_splitting": true,
+  "downsample_factor": 2,
+  "encoder_patch_size": 16,
+  "image_token_index": 396,
+  "max_image_tokens": 256,
+  "max_num_patches": 1024,
+  "max_pixels_tolerance": 2.0,
+  "max_tiles": 10,
+  "min_image_tokens": 64,
+  "min_tiles": 2,
+  "model_type": "lfm2-vl",
+  "projector_bias": true,
+  "projector_hidden_act": "gelu",
+  "projector_hidden_size": 2560,
+  "text_config": {
+    "_name_or_path": "LiquidAI/LFM2-350M",
+    "architectures": [
+      "Lfm2ForCausalLM"
+    ],
+    "block_auto_adjust_ff_dim": true,
+    "block_dim": 1024,
+    "block_ff_dim": 6656,
+    "block_ffn_dim_multiplier": 1.0,
+    "block_mlp_init_scale": 1.0,
+    "block_multiple_of": 256,
+    "block_norm_eps": 1e-05,
+    "block_out_init_scale": 1.0,
+    "block_use_swiglu": true,
+    "block_use_xavier_init": true,
+    "conv_L_cache": 3,
+    "conv_bias": false,
+    "conv_dim": 1024,
+    "conv_dim_out": 1024,
+    "conv_use_xavier_init": true,
+    "eos_token_id": 7,
+    "hidden_size": 1024,
+    "initializer_range": 0.02,
+    "intermediate_size": 6656,
+    "layer_types": [
+      "conv",
+      "conv",
+      "full_attention",
+      "conv",
+      "conv",
+      "full_attention",
+      "conv",
+      "conv",
+      "full_attention",
+      "conv",
+      "full_attention",
+      "conv",
+      "full_attention",
+      "conv",
+      "full_attention",
+      "conv"
+    ],
+    "max_position_embeddings": 128000,
+    "model_type": "lfm2",
+    "norm_eps": 1e-05,
+    "num_attention_heads": 16,
+    "num_heads": 16,
+    "num_hidden_layers": 16,
+    "num_key_value_heads": 8,
+    "rope_theta": 1000000.0,
+    "torch_dtype": "bfloat16",
+    "use_cache": true,
+    "use_pos_enc": true,
+    "vocab_size": 65536
+  },
+  "tile_size": 512,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.0",
+  "use_image_special_tokens": true,
+  "use_thumbnail": false,
+  "vision_config": {
+    "attention_dropout": 0.0,
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 768,
+    "intermediate_size": 3072,
+    "layer_norm_eps": 1e-06,
+    "model_type": "siglip2_vision_model",
+    "num_attention_heads": 12,
+    "num_channels": 3,
+    "num_hidden_layers": 12,
+    "num_patches": 256,
+    "patch_size": 16,
+    "torch_dtype": "bfloat16",
+    "vision_use_head": false
+  },
+  "vision_feature_layer": -1
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 7,
+  "pad_token_id": 0,
+  "transformers_version": "4.55.0"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5e3d19ea94dd54b9b260919839df87a7f6324fd729323c62590f5c8e146bfe29
+size 901692416

modeling_lfm2_vl.py

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+"""PyTorch LFM2-VL model."""
+
+from dataclasses import dataclass
+
+import torch
+from torch import nn
+from transformers import AutoConfig, AutoModel
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache
+from transformers.configuration_utils import PretrainedConfig
+from transformers.generation import GenerationMixin
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_outputs import BaseModelOutputWithPast, ModelOutput
+from transformers.modeling_utils import PreTrainedModel
+from transformers.models.lfm2.configuration_lfm2 import Lfm2Config
+from transformers.models.siglip2.configuration_siglip2 import Siglip2VisionConfig
+from transformers.models.siglip2.modeling_siglip2 import Siglip2VisionModel
+from transformers.processing_utils import Unpack
+from transformers.utils import can_return_tuple, logging
+
+logger = logging.get_logger(__name__)
+
+
+class Lfm2VlConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Lfm2VlForConditionalGeneration`]. It is used to instantiate an
+    Lfm2Vl model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the Lfm2-VL-1.6B.
+
+    e.g. [LiquidAI/LFM2-VL-1.6B](https://huggingface.co/LiquidAI/LFM2-VL-1.6B)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vision_config (`AutoConfig | dict`,  *optional*, defaults to `Siglip2ImageConfig`):
+            The config object or dictionary of the vision backbone.
+        text_config (`AutoConfig | dict`, *optional*, defaults to `Lfm2Config`):
+            The config object or dictionary of the text backbone.
+        image_token_id (`int`, *optional*, defaults to 396):
+            The image token index to encode the image prompt.
+        projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
+            The activation function used by the multimodal projector.
+        projector_hidden_size (`int`, *optional*, defaults to 2056):
+            The hidden size of the multimodal projector.
+        projector_bias (`bool`, *optional*, defaults to `True`):
+            Whether to use bias in the multimodal projector.
+        downsample_factor (`int`, *optional*, defaults to 2):
+            The downsample_factor factor of the vision backbone.
+        vision_feature_layer (`int`, *optional*, defaults to -1):
+            The layer of the vision tower to use as features.
+        min_image_tokens (`int`, *optional*, defaults to 64):
+            The minimum number of image tokens for smart resize.
+        max_image_tokens (`int`, *optional*, defaults to 256):
+            The maximum number of image tokens for smart resize.
+        encoder_patch_size (`int`, *optional*, defaults to 16):
+            The patch size of the encoder.
+        max_num_patches (`int`, *optional*, defaults to 1024):
+            The maximum number of image tokens passed to the encoder per image or tile.
+        use_image_special_tokens (`bool`, *optional*, defaults to `True`):
+            Whether to use image special tokens.
+        do_image_splitting (`bool`, *optional*, defaults to `True`):
+            Whether to split large images into tiles.
+        min_tiles (`int`, *optional*, defaults to 2):
+            The minimum number of tiles to split the image into.
+        max_tiles (`int`, *optional*, defaults to 10):
+            The maximum number of tiles to split the image into.
+        tile_size (`int`, *optional*, defaults to 512):
+            The size of the tile to split the image into.
+        max_pixels_tolerance (`float`, *optional*, defaults to 2.0):
+            The maximum tolerance for the number of pixels in the image before splitting.
+        use_thumbnail (`bool`, *optional*, defaults to `True`):
+            Whether to append the thumbnail of the image when splitting.
+    """
+
+    model_type = "lfm2-vl"
+    attribute_map = {
+        "image_token_id": "image_token_index",
+    }
+    sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}
+
+    def __init__(
+        self,
+        vision_config=None,
+        text_config=None,
+        image_token_index=396,
+        projector_hidden_act="gelu",
+        projector_hidden_size=2560,
+        projector_bias=True,
+        downsample_factor=2,
+        vision_feature_layer=-1,
+        min_image_tokens=64,
+        max_image_tokens=256,
+        encoder_patch_size=16,
+        max_num_patches=1024,
+        use_image_special_tokens=True,
+        do_image_splitting=True,
+        min_tiles=2,
+        max_tiles=10,
+        tile_size=512,
+        max_pixels_tolerance=2.0,
+        use_thumbnail=True,
+        torch_dtype=torch.bfloat16,
+        **kwargs,
+    ):
+        self.vision_config = vision_config
+        self.text_config = text_config
+        self.image_token_index = image_token_index
+        self.projector_hidden_act = projector_hidden_act
+        self.projector_hidden_size = projector_hidden_size
+        self.projector_bias = projector_bias
+        self.downsample_factor = downsample_factor
+        self.vision_feature_layer = vision_feature_layer
+        self.min_image_tokens = min_image_tokens
+        self.max_image_tokens = max_image_tokens
+        self.encoder_patch_size = encoder_patch_size
+        self.max_num_patches = max_num_patches
+        self.use_image_special_tokens = use_image_special_tokens
+        self.do_image_splitting = do_image_splitting
+        self.min_tiles = min_tiles
+        self.max_tiles = max_tiles
+        self.tile_size = tile_size
+        self.max_pixels_tolerance = max_pixels_tolerance
+        self.use_thumbnail = use_thumbnail
+        self.torch_dtype = torch_dtype
+        
+        if isinstance(vision_config, dict):
+            vision_config = Siglip2VisionConfig(**vision_config)
+        elif vision_config is None:
+            vision_config = Siglip2VisionConfig()
+        self.vision_config = vision_config
+
+        self.vision_config = vision_config
+
+        if isinstance(text_config, dict):
+            text_config = Lfm2Config(**text_config)
+        elif text_config is None:
+            text_config = Lfm2Config()
+
+        self.text_config = text_config
+
+        super().__init__(**kwargs)
+
+
+@dataclass
+class Lfm2VlModelOutputWithPast(BaseModelOutputWithPast):
+    r"""
+    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+        Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+        `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+        Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+        `past_key_values` input) to speed up sequential decoding.
+    image_hidden_states (`torch.FloatTensor`, *optional*):
+        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+        image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+    """
+
+    image_hidden_states: torch.FloatTensor | None = None
+
+
+@dataclass
+class Lfm2VlCausalLMOutputWithPast(ModelOutput):
+    r"""
+    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+        Language modeling loss (for next-token prediction).
+    logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+        Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+        Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+        `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+        Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+        `past_key_values` input) to speed up sequential decoding.
+    image_hidden_states (`torch.FloatTensor`, *optional*):
+        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+        image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+    """
+
+    loss: torch.FloatTensor | None = None
+    logits: torch.FloatTensor | None = None
+    past_key_values: list[torch.FloatTensor] | None = None
+    hidden_states: tuple[torch.FloatTensor] | None = None
+    attentions: tuple[torch.FloatTensor] | None = None
+    image_hidden_states: torch.FloatTensor | None = None
+
+
+class Lfm2VlMultiModalProjector(nn.Module):
+    def __init__(self, config: Lfm2VlConfig):
+        super().__init__()
+        in_channels = config.vision_config.hidden_size * (config.downsample_factor**2)
+        self.layer_norm = nn.LayerNorm(in_channels)
+        self.linear_1 = nn.Linear(
+            in_channels,
+            config.projector_hidden_size,
+            bias=config.projector_bias,
+        )
+        self.act = ACT2FN[config.projector_hidden_act]
+        self.linear_2 = nn.Linear(
+            config.projector_hidden_size,
+            config.text_config.hidden_size,
+            bias=config.projector_bias,
+        )
+
+    def forward(self, image_features):
+        image_features = self.layer_norm(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 PixelUnshuffleBlock(nn.Module):
+    def __init__(self, factor: int):
+        super().__init__()
+        self.factor = factor
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        n, w, h, c = x.size()
+        if w % self.factor != 0:
+            x = torch.concat(
+                [
+                    x,
+                    torch.zeros(
+                        (n, self.factor - (w % self.factor), h, c), dtype=x.dtype
+                    ).to(x.device),
+                ],
+                dim=1,
+            ).contiguous()
+            n, w, h, c = x.size()
+        x = x.contiguous()
+        if h % self.factor != 0:
+            x = torch.concat(
+                [
+                    x,
+                    torch.zeros(
+                        (n, w, self.factor - (h % self.factor), c), dtype=x.dtype
+                    ).to(x.device),
+                ],
+                dim=2,
+            ).contiguous()
+            n, w, h, c = x.size()
+        x = x.view(n, w, int(h / self.factor), int(c * self.factor))
+        x = x.permute(0, 2, 1, 3).contiguous()
+        x = x.view(
+            n, int(h / self.factor), int(w / self.factor), int(c * self.factor**2)
+        )
+        x = x.permute(0, 2, 1, 3).contiguous()
+        return x
+
+
+class Lfm2VlPreTrainedModel(PreTrainedModel):
+    config: Lfm2VlConfig
+    base_model_prefix = ""
+    supports_gradient_checkpointing = True
+    _skip_keys_device_placement = ["past_key_values"]
+
+    _supports_flash_attn = True
+    _supports_sdpa = True
+
+    _can_compile_fullgraph = False
+    _supports_flex_attn = True
+    _supports_attention_backend = True
+
+
+class Lfm2VlModel(Lfm2VlPreTrainedModel):
+    _checkpoint_conversion_mapping = {"language_model.model": "language_model"}
+
+    def __init__(self, config: Lfm2VlConfig):
+        super().__init__(config)
+        self.vision_tower = Siglip2VisionModel(config.vision_config)
+
+        if config.vision_feature_layer != -1:
+            self.vision_tower.vision_model.encoder.layers = (
+                self.vision_tower.vision_model.encoder.layers[
+                    : config.vision_feature_layer + 1
+                ]
+            )
+        if config.downsample_factor > 1:
+            self.pixel_unshuffle = PixelUnshuffleBlock(config.downsample_factor)
+        else:
+            self.pixel_unshuffle = nn.Identity()
+
+        self.multi_modal_projector = Lfm2VlMultiModalProjector(config)
+        self.language_model = AutoModel.from_config(config.text_config)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    def set_decoder(self, decoder):
+        self.language_model = decoder
+
+    def get_decoder(self):
+        return self.language_model
+
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        spatial_shapes: torch.Tensor,
+        pixel_attention_mask: torch.Tensor,
+        **kwargs,
+    ) -> list[torch.Tensor]:
+        """
+        Obtains image last hidden states from the vision tower and apply multimodal projection.
+
+        Args:
+            pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`):
+               The tensors corresponding to the input images.
+            spatial_shapes (`torch.Tensor` of shape `(batch_size, 2)`):
+                The spatial shapes of the input images.
+            pixel_attention_mask (`torch.Tensor` of shape `(batch_size, height, width)`):
+                The pixel attention mask of the input images.
+        Returns:
+            image_features (`list[torch.Tensor]`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
+        """
+        image_outputs = self.vision_tower(
+            pixel_values=pixel_values,
+            spatial_shapes=spatial_shapes,
+            pixel_attention_mask=pixel_attention_mask,
+        ).last_hidden_state
+
+        img_feature_lengths = pixel_attention_mask.sum(dim=1)
+        image_features = []
+
+        for img_idx in range(image_outputs.size(0)):
+            feature = image_outputs[img_idx]
+            # unpad the image representation
+            feature = feature[: img_feature_lengths[img_idx], :].unsqueeze(0)
+
+            feature_org_h, feature_org_w = spatial_shapes[img_idx]
+            feature = feature.reshape(1, feature_org_h, feature_org_w, -1)
+            feature = self.pixel_unshuffle(feature)
+
+            # project the image representation
+            img_embedding = self.multi_modal_projector(feature)
+
+            # flatten here to handle variable length in naflex
+            img_embedding = img_embedding.reshape(-1, img_embedding.size(-1))
+            image_features.append(img_embedding)
+
+        return image_features
+
+    def get_placeholder_mask(
+        self,
+        input_ids: torch.LongTensor | None,
+        inputs_embeds: torch.FloatTensor,
+        image_features: torch.FloatTensor,
+    ):
+        """
+        Obtains multimodal placeholdr mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+        equal to the length of multimodal features. If the lengths are different, an error is raised.
+        """
+        if input_ids is None:
+            special_image_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(
+                    self.config.image_token_id,
+                    dtype=torch.long,
+                    device=inputs_embeds.device,
+                )
+            )
+            special_image_mask = special_image_mask.all(-1)
+        else:
+            special_image_mask = input_ids == self.config.image_token_id
+        n_image_tokens = special_image_mask.sum()
+        special_image_mask = (
+            special_image_mask.unsqueeze(-1)
+            .expand_as(inputs_embeds)
+            .to(inputs_embeds.device)
+        )
+        n_image_features = image_features.shape[0]
+        if inputs_embeds[special_image_mask].numel() != image_features.numel():
+            raise ValueError(
+                f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
+            )
+        return special_image_mask
+
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        pixel_values: torch.FloatTensor = None,
+        spatial_shapes: torch.Tensor = None,
+        pixel_attention_mask: torch.Tensor = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+        image_sizes: torch.Tensor = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple | Lfm2VlModelOutputWithPast:
+        """
+        spatial_shapes (`torch.Tensor` of shape `(batch_size, 2)`, *optional*):
+            The spatial shapes of the input images.
+        pixel_attention_mask (`torch.Tensor` of shape `(batch_size, height, width)`, *optional*):
+            The pixel attention mask of the input images.
+        """
+        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
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You must specify exactly one of input_ids or inputs_embeds"
+            )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        if pixel_values is not None:
+            image_features = self.get_image_features(
+                pixel_values=pixel_values,
+                spatial_shapes=spatial_shapes,
+                pixel_attention_mask=pixel_attention_mask,
+            )
+            image_features = torch.cat(image_features, dim=0).to(
+                inputs_embeds.device, inputs_embeds.dtype
+            )
+            special_image_mask = self.get_placeholder_mask(
+                input_ids=input_ids,
+                inputs_embeds=inputs_embeds,
+                image_features=image_features,
+            )
+            inputs_embeds = inputs_embeds.masked_scatter(
+                special_image_mask, image_features
+            )
+
+        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=True,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        return Lfm2VlModelOutputWithPast(
+            last_hidden_state=outputs.last_hidden_state,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=image_features if pixel_values is not None else None,
+        )
+
+
+class Lfm2VlForConditionalGeneration(Lfm2VlPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config: Lfm2VlConfig):
+        super().__init__(config)
+        self.model = Lfm2VlModel(config)
+        self.lm_head = nn.Linear(
+            config.text_config.hidden_size, config.text_config.vocab_size, bias=False
+        )
+        self.post_init()
+
+    def _supports_default_dynamic_cache(self):
+        return False
+
+    def get_input_embeddings(self):
+        return self.model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.model.set_input_embeddings(value)
+
+    def get_output_embeddings(self) -> nn.Module:
+        return self.lm_head
+
+    def set_decoder(self, decoder):
+        self.model.set_decoder(decoder)
+
+    def get_decoder(self):
+        return self.model.get_decoder()
+
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        spatial_shapes: torch.Tensor,
+        pixel_attention_mask: torch.Tensor,
+        **kwargs,
+    ):
+        return self.model.get_image_features(
+            pixel_values=pixel_values,
+            spatial_shapes=spatial_shapes,
+            pixel_attention_mask=pixel_attention_mask,
+            **kwargs,
+        )
+
+    @property
+    def language_model(self):
+        return self.model.language_model
+
+    @property
+    def vision_tower(self):
+        return self.model.vision_tower
+
+    @property
+    def multi_modal_projector(self):
+        return self.model.multi_modal_projector
+
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        pixel_values: torch.FloatTensor = None,
+        spatial_shapes: torch.Tensor = None,
+        pixel_attention_mask: torch.Tensor = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        image_sizes: torch.Tensor | None = None,
+        **kwargs,
+    ) -> tuple | Lfm2VlCausalLMOutputWithPast:
+        r"""
+        pixel_values (`torch.FloatTensor` of shape `(batch_size, channels, height, width)`, *optional*):
+            The input image tensors.
+        spatial_shapes (`torch.Tensor` of shape `(batch_size, 2)`, *optional*):
+            The spatial shapes of the input images.
+        pixel_attention_mask (`torch.Tensor` of shape `(batch_size, height, width)`, *optional*):
+            The pixel attention mask of the input images.
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Example:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoProcessor, AutoModelForImageTextToText
+        >>> from transformers.image_utils import load_image
+
+        >>> model = AutoModelForImageTextToText.from_pretrained(
+        ...     "LiquidAI/LFM2-VL-1.6B",
+        ...     trust_remote_code=True
+        ... )
+        >>> processor = AutoProcessor.from_pretrained(
+        ...     "LiquidAI/LFM2-VL-1.6B",
+        ...     trust_remote_code=True
+        ... )
+
+        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+        >>> image = load_image(url)
+
+        >>> conversation = [
+        ...     {
+        ...         "role": "user",
+        ...         "content": [
+        ...             {"type": "image", "image": image},
+        ...             {"type": "text", "text": "What is in this image?"},
+        ...         ],
+        ...     },
+        ... ]
+
+        >>> inputs = processor.apply_chat_template(
+        ...     conversation,
+        ...     add_generation_prompt=True,
+        ...     tokenize=True,
+        ...     return_dict=True,
+        ...     return_tensors="pt"
+        ... )
+
+        >>> # Generate
+        >>> outputs = model.generate(**inputs, max_new_tokens=45)
+        >>> processor.batch_decode(outputs, skip_special_tokens=True)[0]
+        'This image depicts a vibrant street scene in what appears to be a Chinatown or similar cultural area. The focal point is a large red stop sign with white lettering, mounted on a pole.'
+        ```"""
+        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
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            spatial_shapes=spatial_shapes,
+            pixel_attention_mask=pixel_attention_mask,
+            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=True,
+            cache_position=cache_position,
+            image_sizes=image_sizes,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = (
+            slice(-logits_to_keep, None)
+            if isinstance(logits_to_keep, int)
+            else logits_to_keep
+        )
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.config.text_config.vocab_size,
+                **kwargs,
+            )
+
+        return Lfm2VlCausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=outputs.image_hidden_states,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        inputs_embeds=None,
+        pixel_values=None,
+        attention_mask=None,
+        cache_position=None,
+        logits_to_keep=None,
+        **kwargs,
+    ):
+        # Overwritten -- in specific circumstances we don't want to forward image inputs to the model
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            cache_position=cache_position,
+            logits_to_keep=logits_to_keep,
+            **kwargs,
+        )
+
+        if cache_position[0] == 0:
+            # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
+            # Otherwise we need pixel values to be passed to model
+            model_inputs["pixel_values"] = pixel_values
+
+        return model_inputs
+
+
+__all__ = ["Lfm2VlForConditionalGeneration", "Lfm2VlModel", "Lfm2VlPreTrainedModel"]