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LFM3-VL

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This model was contributed to Hugging Face Transformers on 2026-11-10.

LFM3-VL

Overview

LFM3-VL is the vision-language model family built on LFM3 by Liquid AI. It takes images and videos at their native resolution together with text.

Architecture

LFM3-VL consists of a vision encoder, a projector and an LFM3 language model. The vision encoder is itself a Liquid hybrid: a linear patchifier followed by gated depthwise 2D convolution layers and bidirectional grouped-query attention layers with 2D rotary embeddings over the patch grid of each frame. Images are split into 16x16 patches with the dynamic-resolution preprocessing of Qwen2-VL, videos additionally pair consecutive frames and prefix each frame with its timestamp. The projector merges every 2x2 block of patch features into one language token with a layer norm and a two-layer MLP.

Example

The following example shows how to generate an answer using the AutoModelForImageTextToText class.

from transformers import AutoModelForImageTextToText, AutoProcessor

# Load model and processor
model_id = "LiquidAI/LFM3-VL-2B"
model = AutoModelForImageTextToText.from_pretrained(model_id, device_map="auto")
processor = AutoProcessor.from_pretrained(model_id)

# Load image and create conversation
conversation = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "https://www.ilankelman.org/stopsigns/australia.jpg"},
            {"type": "text", "text": "What is in this image?"},
        ],
    },
]

# Generate answer
inputs = processor.apply_chat_template(
    conversation,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
    tokenize=True,
).to(model.device, model.dtype)

outputs = model.generate(**inputs, max_new_tokens=64)
processor.batch_decode(outputs, skip_special_tokens=True)[0]

Lfm3VlProcessor

class transformers.Lfm3VlProcessor

< >

( image_processor = Nonetokenizer = Nonevideo_processor = Nonechat_template = None**kwargs )

Parameters

  • image_processor (Qwen2VLImageProcessor) — The image processor is a required input.
  • tokenizer (tokenizer_class) — The tokenizer is a required input.
  • video_processor (Qwen3VLVideoProcessor) — The video processor is a required input.
  • chat_template (str) — A Jinja template to convert lists of messages in a chat into a tokenizable string.

Constructs a Lfm3VlProcessor which wraps a image processor, a tokenizer, and a video processor into a single processor.

Lfm3VlProcessor offers all the functionalities of Qwen2VLImageProcessor, tokenizer_class, and Qwen3VLVideoProcessor. See the ~Qwen2VLImageProcessor, ~tokenizer_class, and ~Qwen3VLVideoProcessor for more information.

post_process_image_text_to_text

< >

( generated_outputsskip_special_tokens = Trueclean_up_tokenization_spaces = False**kwargs ) → list[str]

Parameters

  • generated_outputs (torch.Tensor or np.ndarray) — The output of the model generate function. The output is expected to be a tensor of shape (batch_size, sequence_length) or (sequence_length,).
  • skip_special_tokens (bool, optional, defaults to True) — Whether or not to remove special tokens in the output. Argument passed to the tokenizer’s batch_decode method.
  • clean_up_tokenization_spaces (bool, optional, defaults to False) — Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer’s batch_decode method.
  • **kwargs — Additional arguments to be passed to the tokenizer’s batch_decode method.

Returns

list[str]

The decoded text.

Post-process the output of the model to decode the text.

Lfm3VlConfig

class transformers.Lfm3VlConfig

< >

( transformers_version: str | None = Nonearchitectures: list[str] | None = Noneoutput_hidden_states: bool | None = Falsereturn_dict: bool | None = Truedtype: str | torch.dtype | None = Nonechunk_size_feed_forward: int = 0is_encoder_decoder: bool = Falseid2label: dict[int, str] | dict[str, str] | None = Nonelabel2id: dict[str, int] | dict[str, str] | None = Noneproblem_type: Literal['regression', 'single_label_classification', 'multi_label_classification'] | None = Nonevision_config: dict | transformers.configuration_utils.PreTrainedConfig | None = Nonetext_config: dict | transformers.configuration_utils.PreTrainedConfig | None = Noneimage_token_id: int = 125030video_token_id: int = 125029vision_start_token_id: int = 125009vision_end_token_id: int = 125010projector_hidden_act: str = 'gelu'projector_hidden_size: int = 4096projector_bias: bool = Truetie_word_embeddings: bool = True )

Parameters

  • vision_config (Union[dict, ~configuration_utils.PreTrainedConfig], optional) — The config object or dictionary of the vision backbone.
  • text_config (Union[dict, ~configuration_utils.PreTrainedConfig], optional) — The config object or dictionary of the text backbone.
  • image_token_id (int, optional, defaults to 125030) — The image token index used as a placeholder for input images.
  • video_token_id (int, optional, defaults to 125029) — The video token index used as a placeholder for input videos.
  • vision_start_token_id (int, optional, defaults to 125009) — Token ID that marks the start of a visual segment in the multimodal input sequence.
  • vision_end_token_id (int, optional, defaults to 125010) — Token ID that marks the end of a visual segment in the multimodal input sequence.
  • projector_hidden_act (str, optional, defaults to gelu) — The activation function used by the multimodal projector.
  • projector_hidden_size (int, optional, defaults to 4096) — Dimensionality of text and vision projection layers.
  • projector_bias (bool, optional, defaults to True) — Whether to use bias in the multimodal projector.
  • tie_word_embeddings (bool, optional, defaults to True) — Whether to tie weight embeddings according to model’s tied_weights_keys mapping.

This is the configuration class to store the configuration of a Lfm3VlModel. It is used to instantiate a Lfm3 Vl 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 LiquidAI/LFM3-VL-2B

Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information.

Lfm3VlVisionConfig

class transformers.Lfm3VlVisionConfig

< >

( transformers_version: str | None = Nonearchitectures: list[str] | None = Noneoutput_hidden_states: bool | None = Falsereturn_dict: bool | None = Truedtype: str | torch.dtype | None = Nonechunk_size_feed_forward: int = 0is_encoder_decoder: bool = Falseid2label: dict[int, str] | dict[str, str] | None = Nonelabel2id: dict[str, int] | dict[str, str] | None = Noneproblem_type: Literal['regression', 'single_label_classification', 'multi_label_classification'] | None = Nonehidden_size: int = 1024intermediate_size: int = 2560in_channels: int = 3patch_size: int | list[int] | tuple[int, int] = 16spatial_merge_size: int = 2temporal_patch_size: int | list[int] | tuple[int, int] = 2initializer_range: float = 0.02rope_parameters: dict | None = Nonenum_hidden_layers: int = 14num_attention_heads: int = 16num_key_value_heads: int = 8layer_types: list[str] | None = Noneconv_kernel_size: int = 3norm_eps: float = 1e-05 )

Parameters

  • hidden_size (int, optional, defaults to 1024) — Dimension of the hidden representations.
  • intermediate_size (int, optional, defaults to 2560) — Dimension of the MLP representations.
  • in_channels (int, optional, defaults to 3) — The number of input channels.
  • patch_size (Union[int, list[int], tuple[int, int]], optional, defaults to 16) — The size (resolution) of each patch.
  • spatial_merge_size (int, optional, defaults to 2) — The size of the spatial merge window used to reduce the number of visual tokens by merging neighboring patches.
  • temporal_patch_size (Union[int, list[int], tuple[int, int]], optional, defaults to 2) — Temporal patch size used in the 3D patch embedding for video inputs.
  • initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
  • rope_parameters (dict, optional) — Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for rope_theta and optionally parameters used for scaling in case you want to use RoPE with longer max_position_embeddings.
  • num_hidden_layers (int, optional, defaults to 14) — Number of hidden layers in the Transformer decoder.
  • num_attention_heads (int, optional, defaults to 16) — Number of attention heads for each attention layer in the Transformer decoder.
  • num_key_value_heads (int, optional, defaults to 8) — This is the number of key_value heads that should be used to implement Grouped Query Attention. If num_key_value_heads=num_attention_heads, the model will use Multi Head Attention (MHA), if num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out this paper. If it is not specified, will default to num_attention_heads.
  • layer_types (list[str], optional) — A list that explicitly maps each layer index with its layer type. If not provided, it will be automatically generated based on config values.
  • conv_kernel_size (int, optional, defaults to 3) — Kernel size of the depthwise convolution over the patch grid of each frame.
  • norm_eps (float, optional, defaults to 1e-05) — The epsilon used by the layer normalization layers.

This is the configuration class to store the configuration of a Lfm3VlModel. It is used to instantiate a Lfm3 Vl 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 LiquidAI/LFM3-VL-2B

Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information.

Lfm3VlVisionModel

class transformers.Lfm3VlVisionModel

< >

( config: Lfm3VlVisionConfig )

Parameters

  • config (Lfm3VlVisionConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The LFM3-VL vision encoder: a stack of gated convolution and bidirectional attention layers over the patches of each image or video frame, embedded by a linear patch embedding.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< >

( pixel_values: Tensorgrid_thw: Tensor**kwargs: *<class 'transformers.utils.generic.TransformersKwargs'> ) → BaseModelOutputWithPooling or tuple(torch.Tensor)

Parameters

  • pixel_values (torch.Tensor of shape (num_patches, in_channels * temporal_patch_size * patch_size**2)) — The flattened patches of every image or video, grouped by merge block.
  • grid_thw (torch.LongTensor of shape (num_images_or_videos, 3)) — The temporal, height and width dimensions of the patch grid of each image or video.

Returns

BaseModelOutputWithPooling or tuple(torch.Tensor)

A BaseModelOutputWithPooling or a tuple of torch.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (Lfm3VlConfig) and inputs.

The Lfm3VlVisionModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

  • last_hidden_state (torch.Tensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.

  • pooler_output (torch.Tensor of shape (batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.

  • hidden_states (tuple(torch.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.Tensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Lfm3VlModel

class transformers.Lfm3VlModel

< >

( config: Lfm3VlConfig )

Parameters

  • config (Lfm3VlConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The bare Lfm3 Vl Model outputting raw hidden-states without any specific head on top.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< >

( input_ids: typing.Optional[torch.LongTensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Nonepast_key_values: transformers.cache_utils.Cache | None = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Nonepixel_values: typing.Optional[torch.FloatTensor] = Noneimage_grid_thw: typing.Optional[torch.LongTensor] = Nonepixel_values_videos: typing.Optional[torch.FloatTensor] = Nonevideo_grid_thw: typing.Optional[torch.LongTensor] = Noneuse_cache: bool | None = Nonemm_encoder_outputs: dict[str, transformers.modeling_outputs.BaseModelOutputWithPooling] | None = None**kwargs: *<class 'transformers.utils.generic.TransformersKwargs'> ) → Lfm3VlModelOutputWithPast or tuple(torch.Tensor)

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

    Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

    What are input IDs?

  • attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
    • 1 for tokens that are not masked,
    • 0 for tokens that are masked.

    What are attention masks?

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].

    What are position IDs?

  • past_key_values (~cache_utils.Cache, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values returned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.

    Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.

    The model will output the same cache format that is fed as input.

    If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_ids of shape (batch_size, sequence_length).

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
  • pixel_values (torch.FloatTensor of shape (batch_size, num_channels, image_size, image_size), optional) — The tensors corresponding to the input images. Pixel values can be obtained using Qwen2VLImageProcessor. See Qwen2VLImageProcessor.call() for details (Lfm3VlProcessor uses Qwen2VLImageProcessor for processing images).
  • image_grid_thw (torch.LongTensor of shape (num_images, 3), optional) — The temporal, height and width of feature shape of each image in LLM.
  • pixel_values_videos (torch.FloatTensor of shape (batch_size, num_frames, num_channels, frame_size, frame_size), optional) — The tensors corresponding to the input video. Pixel values for videos can be obtained using Qwen3VLVideoProcessor. See Qwen3VLVideoProcessor.call() for details (Lfm3VlProcessor uses Qwen3VLVideoProcessor for processing videos).
  • video_grid_thw (torch.LongTensor of shape (num_videos, 3), optional) — The temporal, height and width of feature shape of each video in LLM.
  • 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).
  • mm_encoder_outputs (dict[str, ~modeling_outputs.BaseModelOutputWithPooling], optional) — Dict where keys are supported modalities and values are encoded outputs for that modality. Each encoded output is a tuple that consists of (pooler_output, optional: last_hidden_states, optional: hidden_states, optional: attentions) pooler_output of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of multimmodal features of the encoder merged into text embeddings.

Returns

Lfm3VlModelOutputWithPast or tuple(torch.Tensor)

A Lfm3VlModelOutputWithPast or a tuple of torch.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (Lfm3VlConfig) and inputs.

The Lfm3VlModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

  • last_hidden_state (torch.Tensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.

    If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.

  • past_key_values (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a Cache instance. For more details, see our kv cache guide.

    Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

  • hidden_states (tuple(torch.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.Tensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

  • image_pooler_output (torch.FloatTensor, optional) — A torch.FloatTensor of size (num_image_tokens, hidden_size). Image features produced by the vision encoder and projected to the language model hidden size.

  • video_pooler_output (torch.FloatTensor, optional) — A torch.FloatTensor of size (num_video_tokens, hidden_size). Video features produced by the vision encoder and projected to the language model hidden size.

Lfm3VlForConditionalGeneration

class transformers.Lfm3VlForConditionalGeneration

< >

( config: Lfm3VlConfig )

Parameters

  • config (Lfm3VlConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The LFM3_VL model which consists of a vision backbone and a language model.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< >

( input_ids: typing.Optional[torch.LongTensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Nonepast_key_values: transformers.cache_utils.Cache | None = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Nonepixel_values: typing.Optional[torch.FloatTensor] = Noneimage_grid_thw: typing.Optional[torch.LongTensor] = Nonepixel_values_videos: typing.Optional[torch.FloatTensor] = Nonevideo_grid_thw: typing.Optional[torch.LongTensor] = Nonelabels: typing.Optional[torch.LongTensor] = Noneuse_cache: bool | None = Nonelogits_to_keep: typing.Union[int, torch.Tensor] = 0mm_encoder_outputs: dict[str, transformers.modeling_outputs.BaseModelOutputWithPooling] | None = None**kwargs: *<class 'transformers.utils.generic.TransformersKwargs'> ) → Lfm3VlCausalLMOutputWithPast or tuple(torch.Tensor)

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

    Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

    What are input IDs?

  • attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
    • 1 for tokens that are not masked,
    • 0 for tokens that are masked.

    What are attention masks?

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].

    What are position IDs?

  • past_key_values (~cache_utils.Cache, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values returned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.

    Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.

    The model will output the same cache format that is fed as input.

    If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_ids of shape (batch_size, sequence_length).

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
  • pixel_values (torch.FloatTensor of shape (batch_size, num_channels, image_size, image_size), optional) — The tensors corresponding to the input images. Pixel values can be obtained using Qwen2VLImageProcessor. See Qwen2VLImageProcessor.call() for details (Lfm3VlProcessor uses Qwen2VLImageProcessor for processing images).
  • image_grid_thw (torch.LongTensor of shape (num_images, 3), optional) — The temporal, height and width of feature shape of each image in LLM.
  • pixel_values_videos (torch.FloatTensor of shape (batch_size, num_frames, num_channels, frame_size, frame_size), optional) — The tensors corresponding to the input video. Pixel values for videos can be obtained using Qwen3VLVideoProcessor. See Qwen3VLVideoProcessor.call() for details (Lfm3VlProcessor uses Qwen3VLVideoProcessor for processing videos).
  • video_grid_thw (torch.LongTensor of shape (num_videos, 3), optional) — The temporal, height and width of feature shape of each video in LLM.
  • 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].
  • 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).
  • logits_to_keep (Union[int, torch.Tensor], optional, defaults to 0) — If an int, compute logits for the last logits_to_keep tokens. If 0, calculate logits for all input_ids (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a torch.Tensor, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length).
  • mm_encoder_outputs (dict[str, ~modeling_outputs.BaseModelOutputWithPooling], optional) — Dict where keys are supported modalities and values are encoded outputs for that modality. Each encoded output is a tuple that consists of (pooler_output, optional: last_hidden_states, optional: hidden_states, optional: attentions) pooler_output of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of multimmodal features of the encoder merged into text embeddings.

Returns

Lfm3VlCausalLMOutputWithPast or tuple(torch.Tensor)

A Lfm3VlCausalLMOutputWithPast or a tuple of torch.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (Lfm3VlConfig) and inputs.

The Lfm3VlForConditionalGeneration forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

  • loss (torch.Tensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).

  • logits (torch.Tensor 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) — It is a Cache instance. For more details, see our kv cache guide.

    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.

  • hidden_states (tuple(torch.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.Tensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

  • image_pooler_output (torch.FloatTensor, optional) — A torch.FloatTensor of size (num_image_tokens, hidden_size). Image features produced by the vision encoder and projected to the language model hidden size.

  • video_pooler_output (torch.FloatTensor, optional) — A torch.FloatTensor of size (num_video_tokens, hidden_size). Video features produced by the vision encoder and projected to the language model hidden size.

Example:

>>> from transformers import AutoProcessor, AutoModelForImageTextToText

>>> model = AutoModelForImageTextToText.from_pretrained("LiquidAI/LFM3-VL-2B")
>>> processor = AutoProcessor.from_pretrained("LiquidAI/LFM3-VL-2B")

>>> conversation = [
...     {
...         "role": "user",
...         "content": [
...             {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
...             {"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"
... ).to(model.device, model.dtype)

>>> outputs = model.generate(**inputs, max_new_tokens=45)
>>> processor.batch_decode(outputs, skip_special_tokens=True)[0]
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