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This model was published in HF papers on 2025-11-24 and contributed to Hugging Face Transformers on 2026-07-03.

HunYuanVL

Overview

HunYuanVL is a vision-language model for image-text understanding and generation proposed in HunyuanOCR Technical Report . The open-source hunyuan_vl integration in Transformers is a dense-only image-text variant tailored for OCR and document understanding style workloads such as tencent/HunyuanOCR.

The abstract from the paper is the following:

This paper presents HunyuanOCR, a commercial-grade, open-source, and lightweight (1B parameters) Vision-Language Model (VLM) dedicated to OCR tasks. The architecture comprises a Native Vision Transformer (ViT) and a lightweight LLM connected via an MLP adapter. HunyuanOCR demonstrates superior performance, outperforming commercial APIs, traditional pipelines, and larger models (e.g., Qwen3-VL-4B). Specifically, it surpasses current public solutions in perception tasks (Text Spotting, Parsing) and excels in semantic tasks (IE, Text Image Translation), securing first place in the ICDAR 2025 DIMT Challenge (Small Model Track). Furthermore, it achieves state-of-the-art (SOTA) results on OCRBench among VLMs with fewer than 3B parameters.

HunyuanOCR achieves breakthroughs in three key aspects: 1) Unifying Versatility and Efficiency: We implement comprehensive support for core capabilities, including spotting, parsing, IE, VQA, and translation within a lightweight framework. This addresses the limitations of narrow “OCR expert models” and inefficient “General VLMs”. 2) Streamlined End-to-End Architecture: Adopting a pure end-to-end paradigm eliminates dependencies on pre-processing modules (e.g., layout analysis). This fundamentally resolves error propagation common in traditional pipelines and simplifies system deployment. 3) Data-Driven and RL Strategies: We confirm the critical role of high-quality data and, for the first time in the industry, demonstrate that Reinforcement Learning (RL) strategies yield significant performance gains in OCR tasks.

HunyuanOCR is officially open-sourced on HuggingFace. We also provide a high-performance deployment solution based on vLLM, placing its production efficiency in the top tier. We hope this model will advance frontier research and provide a solid foundation for industrial applications.

Recommended checkpoints

Usage tips

This Transformers integration intentionally exposes the image-text path that is exercised by public OCR-style checkpoints.

  • Supported: dense-only text backbone, image-text prompting, OCR/document-understanding style generation.
  • Not supported as part of this open-source variant: video inputs and runtime MoE execution paths.
  • Compatibility note: some legacy Tencent-export configuration fields are still accepted so existing checkpoints can be loaded, but those fields do not imply that the open-source implementation enables extra runtime capabilities.
  • For the currently validated OCR path, attn_implementation="eager" is the recommended starting point.
  • backend="pil" is recommended when loading the processor for the current public OCR checkpoints.
  • When batching variable-length prompts, pass padding=True if you need tensor outputs from the processor.

Usage

import torch
from transformers import AutoModelForImageTextToText, AutoProcessor


model_name_or_path = "tencent/HunyuanOCR"
processor = AutoProcessor.from_pretrained(model_name_or_path, backend="pil")
model = AutoModelForImageTextToText.from_pretrained(
    model_name_or_path,
    device_map="auto",
)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=1024)

generated_ids_trimmed = generated_ids[0][len(inputs["input_ids"][0]) :]
output = processor.decode(generated_ids_trimmed, skip_special_tokens=True)
print(output)

HunYuanVLProcessor

class transformers.HunYuanVLProcessor

< >

( image_processor = None tokenizer = None chat_template = None cat_extra_token: bool = True **kwargs )

Parameters

  • image_processor (HunYuanVLImageProcessor) — The image processor is a required input.
  • tokenizer (Qwen2Tokenizer) — The tokenizer is a required input.
  • chat_template (str) — A Jinja template to convert lists of messages in a chat into a tokenizable string.
  • cat_extra_token (bool, optional, defaults to True) — Whether to account for the two extra tokens that HunYuanVL inserts around each image span when computing the expanded image token sequence.

Constructs a HunYuanVLProcessor which wraps a image processor and a tokenizer into a single processor.

HunYuanVLProcessor offers all the functionalities of HunYuanVLImageProcessor and Qwen2Tokenizer. See the ~HunYuanVLImageProcessor and ~Qwen2Tokenizer for more information.

__call__

< >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None text: str | list[str] | list[list[str]] | None = None videos: typing.Union[list['PIL.Image.Image'], numpy.ndarray, ForwardRef('torch.Tensor'), list[numpy.ndarray], list['torch.Tensor'], list[list['PIL.Image.Image']], list[list[numpy.ndarray]], list[list['torch.Tensor']], transformers.video_utils.URL, list[transformers.video_utils.URL], list[list[transformers.video_utils.URL]], transformers.video_utils.Path, list[transformers.video_utils.Path], list[list[transformers.video_utils.Path]], NoneType] = None audio: typing.Union[numpy.ndarray, ForwardRef('torch.Tensor'), collections.abc.Sequence[numpy.ndarray], collections.abc.Sequence['torch.Tensor'], NoneType] = None **kwargs: typing_extensions.Unpack[transformers.processing_utils.ProcessingKwargs] )

Parameters

  • images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]], optional) — Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set do_rescale=False.
  • text (Union[str, list[str], list[list[str]]], optional) — The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If you pass a pretokenized input, set is_split_into_words=True to avoid ambiguity with batched inputs.
  • videos (Union[list[PIL.Image.Image], numpy.ndarray, torch.Tensor, list[numpy.ndarray], list[torch.Tensor], list[list[PIL.Image.Image]], list[list[numpy.ndarray]], list[list[torch.Tensor]], ~video_utils.URL, list[~video_utils.URL], list[list[~video_utils.URL]], ~video_utils.Path, list[~video_utils.Path], list[list[~video_utils.Path]]], optional) — Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If passing in videos with pixel values between 0 and 1, set do_rescale=False.
  • audio (Union[numpy.ndarray, torch.Tensor, collections.abc.Sequence[numpy.ndarray], collections.abc.Sequence[torch.Tensor]], optional) — The audio or batch of audios to be prepared. Each audio can be a NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each audio should be of shape (C, T), where C is a number of channels, and T is the sample length of the audio.
  • return_tensors (str or TensorType, optional) — If set, will return tensors of a particular framework. Acceptable values are:

    • 'pt': Return PyTorch torch.Tensor objects.
    • 'np': Return NumPy np.ndarray objects.
  • **kwargs (ProcessingKwargs, optional) — Additional processing options for each modality (text, images, videos, audio). Model-specific parameters are listed above; see the TypedDict class for the complete list of supported arguments.

HunYuanVLImageProcessor

class transformers.HunYuanVLImageProcessor

< >

( **kwargs: typing_extensions.Unpack[transformers.models.hunyuan_vl.image_processing_hunyuan_vl.HunYuanVLImageProcessorKwargs] )

Parameters

  • min_pixels (int, kwargs, optional, defaults to 512 * 512) — The min pixels of the image to resize the image.
  • max_pixels (int, kwargs, optional, defaults to 2048 * 2048) — The max pixels of the image to resize the image.
  • patch_size (int, kwargs, optional, defaults to 16) — The spatial patch size of the vision encoder.
  • temporal_patch_size (int, kwargs, optional, defaults to 1) — The temporal patch size of the vision encoder.
  • merge_size (int, kwargs, optional, defaults to 2) — The merge size of the vision encoder to llm encoder.
  • **kwargs (ImagesKwargs, optional) — Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Constructs a HunYuanVLImageProcessor image processor.

get_number_of_image_patches

< >

( height: int width: int images_kwargs = None )

Return the (grid_h, grid_w) patch counts used by HunYuanVL token accounting.

preprocess

< >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']] **kwargs: typing_extensions.Unpack[transformers.models.hunyuan_vl.image_processing_hunyuan_vl.HunYuanVLImageProcessorKwargs] ) → ~image_processing_base.BatchFeature

Parameters

  • images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]) — Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set do_rescale=False.
  • min_pixels (int, kwargs, optional, defaults to 512 * 512) — The min pixels of the image to resize the image.
  • max_pixels (int, kwargs, optional, defaults to 2048 * 2048) — The max pixels of the image to resize the image.
  • patch_size (int, kwargs, optional, defaults to 16) — The spatial patch size of the vision encoder.
  • temporal_patch_size (int, kwargs, optional, defaults to 1) — The temporal patch size of the vision encoder.
  • merge_size (int, kwargs, optional, defaults to 2) — The merge size of the vision encoder to llm encoder.
  • return_tensors (str or TensorType, optional) — Returns stacked tensors if set to 'pt', otherwise returns a list of tensors.
  • **kwargs (ImagesKwargs, optional) — Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Returns

~image_processing_base.BatchFeature

  • data (dict) — Dictionary of lists/arrays/tensors returned by the call method (‘pixel_values’, etc.).
  • tensor_type (Union[None, str, TensorType], optional) — You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization.

HunYuanVLImageProcessorPil

class transformers.HunYuanVLImageProcessorPil

< >

( **kwargs: typing_extensions.Unpack[transformers.models.hunyuan_vl.image_processing_pil_hunyuan_vl.HunYuanVLImageProcessorKwargs] )

get_number_of_image_patches

< >

( height: int width: int images_kwargs = None )

Return the (grid_h, grid_w) patch counts used by HunYuanVL token accounting.

preprocess

< >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']] **kwargs: typing_extensions.Unpack[transformers.models.hunyuan_vl.image_processing_pil_hunyuan_vl.HunYuanVLImageProcessorKwargs] ) → ~image_processing_base.BatchFeature

Parameters

  • images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]) — Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set do_rescale=False.
  • min_pixels (int, kwargs, optional, defaults to 512 * 512) — The min pixels of the image to resize the image.
  • max_pixels (int, kwargs, optional, defaults to 2048 * 2048) — The max pixels of the image to resize the image.
  • patch_size (int, kwargs, optional, defaults to 16) — The spatial patch size of the vision encoder.
  • temporal_patch_size (int, kwargs, optional, defaults to 1) — The temporal patch size of the vision encoder.
  • merge_size (int, kwargs, optional, defaults to 2) — The merge size of the vision encoder to llm encoder.
  • return_tensors (str or TensorType, optional) — Returns stacked tensors if set to 'pt', otherwise returns a list of tensors.
  • **kwargs (ImagesKwargs, optional) — Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Returns

~image_processing_base.BatchFeature

  • data (dict) — Dictionary of lists/arrays/tensors returned by the call method (‘pixel_values’, etc.).
  • tensor_type (Union[None, str, TensorType], optional) — You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization.

HunYuanVLForConditionalGeneration is the main public entrypoint for image-text generation. HunYuanVLModel exposes the multimodal base model without the language modeling head, while HunYuanVLTextModel exposes the lower-level text backbone.

HunYuanVLConfig

class transformers.HunYuanVLConfig

< >

( transformers_version: str | None = None architectures: list[str] | None = None output_hidden_states: bool | None = False return_dict: bool | None = True dtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None chunk_size_feed_forward: int = 0 is_encoder_decoder: bool = False id2label: dict[int, str] | dict[str, str] | None = None label2id: dict[str, int] | dict[str, str] | None = None problem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = None text_config: dict | transformers.configuration_utils.PreTrainedConfig | None = None vision_config: dict | transformers.configuration_utils.PreTrainedConfig | None = None image_token_id: int = 120120 tie_word_embeddings: bool = True im_start_id: int = 120118 im_end_id: int = 120119 im_newline_id: int = 120121 )

Parameters

  • text_config (HunYuanVLTextConfig or dict, optional) — Configuration of the text backbone. When None, default values are used.
  • vision_config (HunYuanVLVisionConfig or dict, optional) — Configuration of the vision tower. When None, default values are used.
  • image_token_id (int, optional, defaults to 120120) — The image token index used as a placeholder for input images.
  • tie_word_embeddings (bool, optional, defaults to True) — Whether to tie weight embeddings according to model’s tied_weights_keys mapping.
  • im_start_id (int, optional, defaults to 120118) — Token id marking the beginning of an image span in multimodal prompts.
  • im_end_id (int, optional, defaults to 120119) — Token id marking the end of an image span in multimodal prompts.
  • im_newline_id (int, optional, defaults to 120121) — Token id used for newline-style separators inserted inside serialized image regions.

Top-level configuration for the open-source HunYuanVL integration.

This configuration describes the dense-only, image-text-only variant used for OCR and document-understanding style workloads. It mirrors the Qwen2_5_VL / Qwen3_VL family layout: the top-level config simply composes a HunYuanVLTextConfig (text backbone) and a HunYuanVLVisionConfig (vision tower) plus a few token ids that delimit image spans in multimodal prompts.

Example:

>>> from transformers import HunYuanVLConfig, HunYuanVLForConditionalGeneration
>>>
>>> configuration = HunYuanVLConfig()
>>> model = HunYuanVLForConditionalGeneration(configuration)
>>> configuration = model.config

HunYuanVLVisionConfig

class transformers.HunYuanVLVisionConfig

< >

( transformers_version: str | None = None architectures: list[str] | None = None output_hidden_states: bool | None = False return_dict: bool | None = True dtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None chunk_size_feed_forward: int = 0 is_encoder_decoder: bool = False id2label: dict[int, str] | dict[str, str] | None = None label2id: dict[str, int] | dict[str, str] | None = None problem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = None hidden_act: str = 'gelu' hidden_size: int = 1152 intermediate_size: int = 4304 interpolate_mode: str = 'bilinear' rms_norm_eps: float = 1e-05 attention_dropout: float = 0.0 num_attention_heads: int = 16 num_key_value_heads: int | None = None num_channels: int = 3 num_hidden_layers: int = 27 out_hidden_size: int = 4096 patch_size: int = 16 spatial_merge_size: int = 2 temporal_patch_size: int = 1 img_max_token_num: int = 4096 max_image_size: int = 2048 min_image_size: int = 512 max_vit_seq_len: int = 16384 text_hidden_size: int = 3072 )

Parameters

  • hidden_act (str, optional, defaults to gelu) — The non-linear activation function (function or string) in the decoder. For example, "gelu", "relu", "silu", etc.
  • hidden_size (int, optional, defaults to 1152) — Dimension of the hidden representations.
  • intermediate_size (int, optional, defaults to 4304) — Dimension of the MLP representations.
  • interpolate_mode (str, optional, defaults to "bilinear") — Interpolation mode used when resizing learned patch positional embeddings to match the current image grid.
  • rms_norm_eps (float, optional, defaults to 1e-05) — The epsilon used by the rms normalization layers.
  • attention_dropout (float, optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
  • 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) — 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.
  • num_channels (int, optional, defaults to 3) — The number of input channels.
  • num_hidden_layers (int, optional, defaults to 27) — Number of hidden layers in the Transformer decoder.
  • out_hidden_size (int, optional, defaults to 4096) — Output hidden size produced by the vision tower before it is consumed by the text backbone.
  • patch_size (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 (int, optional, defaults to 1) — Temporal patch size used in the 3D patch embedding for video inputs.
  • img_max_token_num (int, optional, defaults to 4096) — Maximum image token count expected by the vision stack.
  • max_image_size (int, optional, defaults to 2048) — Maximum supported image size for the current open-source vision configuration.
  • min_image_size (int, optional, defaults to 512) — Minimum supported image size for the current open-source vision configuration.
  • max_vit_seq_len (int, optional, defaults to 16384) — Maximum sequence length produced by the vision transformer.
  • text_hidden_size (int, optional, defaults to 3072) — Hidden size expected by the text backbone when consuming visual embeddings.

Vision backbone configuration for the dense-only, image-text HunYuanVL open-source variant.

Example:

>>> from transformers import HunYuanVLVisionConfig
>>>
>>> configuration = HunYuanVLVisionConfig()
>>> configuration.hidden_size
1152

HunYuanVLTextConfig

class transformers.HunYuanVLTextConfig

< >

( transformers_version: str | None = None architectures: list[str] | None = None output_hidden_states: bool | None = False return_dict: bool | None = True dtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None chunk_size_feed_forward: int = 0 is_encoder_decoder: bool = False id2label: dict[int, str] | dict[str, str] | None = None label2id: dict[str, int] | dict[str, str] | None = None problem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = None vocab_size: int = 290943 hidden_size: int = 4096 intermediate_size: int = 11008 num_hidden_layers: int = 32 num_attention_heads: int = 32 num_key_value_heads: int | None = None hidden_act: str = 'silu' max_position_embeddings: int = 2048 initializer_range: float = 0.02 rms_norm_eps: float = 1e-05 use_cache: bool = True pad_token_id: int | None = 0 bos_token_id: int | None = 1 eos_token_id: int | list[int] | None = 2 eod_token_id: int | None = 3 pretraining_tp: int = 1 tie_word_embeddings: bool = True rope_parameters: transformers.modeling_rope_utils.RopeParameters | dict | None = None attention_bias: bool = False attention_dropout: float | int = 0.0 head_dim: int | None = None sep_token_id: int | None = 4 )

Parameters

  • vocab_size (int, optional, defaults to 290943) — Vocabulary size of the model. Defines the number of different tokens that can be represented by the input_ids.
  • hidden_size (int, optional, defaults to 4096) — Dimension of the hidden representations.
  • intermediate_size (int, optional, defaults to 11008) — Dimension of the MLP representations.
  • num_hidden_layers (int, optional, defaults to 32) — Number of hidden layers in the Transformer decoder.
  • num_attention_heads (int, optional, defaults to 32) — Number of attention heads for each attention layer in the Transformer decoder.
  • num_key_value_heads (int, optional) — 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.
  • hidden_act (str, optional, defaults to silu) — The non-linear activation function (function or string) in the decoder. For example, "gelu", "relu", "silu", etc.
  • max_position_embeddings (int, optional, defaults to 2048) — The maximum sequence length that this model might ever be used with.
  • initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
  • rms_norm_eps (float, optional, defaults to 1e-05) — The epsilon used by the rms normalization layers.
  • use_cache (bool, optional, defaults to True) — Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if config.is_decoder=True or when the model is a decoder-only generative model.
  • pad_token_id (int, optional, defaults to 0) — Token id used for padding in the vocabulary.
  • bos_token_id (int, optional, defaults to 1) — Token id used for beginning-of-stream in the vocabulary.
  • eos_token_id (Union[int, list[int]], optional, defaults to 2) — Token id used for end-of-stream in the vocabulary.
  • eod_token_id (int, optional, defaults to 3) — Token id representing the end-of-document marker. Inherited from HunYuanDenseV1Config and re-documented here so the auto-generated docstring stays in sync.
  • pretraining_tp (int, optional, defaults to 1) — Experimental feature. Tensor parallelism rank used during pretraining. Please refer to this document to understand more about it. This value is necessary to ensure exact reproducibility of the pretraining results. Please refer to this issue.
  • tie_word_embeddings (bool, optional, defaults to True) — Whether to tie weight embeddings according to model’s tied_weights_keys mapping.
  • rope_parameters (dict, optional) — RoPE configuration inherited from HunYuanDenseV1Config. When mrope_section is present, it partitions half of each attention head across HunYuanVL’s multimodal RoPE axes. The expected order is (width, height, image_index) for 3-axis multimodal RoPE and (position, width, height, image_index) for 4-axis multimodal RoPE. The image_index axis is the ordinal of the image/frame in the input sequence; all visual tokens from one image share the same value on that axis.
  • attention_bias (bool, optional, defaults to False) — Whether to use a bias in the query, key, value and output projection layers during self-attention.
  • attention_dropout (Union[float, int], optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
  • head_dim (int, optional) — The attention head dimension. If None, it will default to hidden_size // num_attention_heads
  • sep_token_id (int, optional, defaults to 4) — Token id used as a separator marker by HunYuan tokenizers.

Text backbone configuration for the dense-only, image-text HunYuanVL open-source variant.

Inherits the standard fields from HunYuanDenseV1Config and declares the canonical field names (pad_token_id, head_dim, vocab_size) as the only public attributes. Legacy aliases that some Tencent checkpoints persist on disk (pad_id, attention_head_dim, org_vocab_size) are mapped onto those canonical fields via attribute_map, so the rest of the model only ever needs to read the canonical fields. Legacy RoPE payloads persisted as rope_scaling / rope_theta are normalized by the base configuration class into rope_parameters.

HunYuanVLVisionTransformer

class transformers.HunYuanVLVisionTransformer

< >

( config: HunYuanVLVisionConfig )

HunYuanVL vision tower: patch embedding -> transformer blocks -> per-image patch merger.

Inputs are flat per-patch pixel tensors plus an image_grid_thw tensor describing the spatial layout of every image in the batch. The output is the concatenation of merged image embeddings, ready to be scattered into the language-model embedding stream.

forward

< >

( pixel_values: Tensor grid_thw: LongTensor **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → BaseModelOutputWithPooling or tuple(torch.FloatTensor)

Parameters

  • pixel_values (torch.Tensor of shape (num_patches, num_channels * patch_size * patch_size)) — Flat per-patch pixel features produced by the image processor.
  • grid_thw (torch.LongTensor of shape (num_images, 3)) — The temporal, height and width dimensions for each image. Each row contains [t, h, w] patch counts.

Returns

BaseModelOutputWithPooling or tuple(torch.FloatTensor)

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

The HunYuanVLVisionTransformer 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.FloatTensor 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.FloatTensor 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.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (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.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (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.

HunYuanVLTextModel

class transformers.HunYuanVLTextModel

< >

( config: HunYuanVLTextConfig )

Parameters

  • config (HunYuanVLTextConfig) — 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 Hunyuan Vl Text Model outputting raw hidden-states without any specific head on to.

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: torch.LongTensor | None = None attention_mask: torch.Tensor | None = None position_ids: torch.LongTensor | None = None past_key_values: transformers.cache_utils.Cache | None = None inputs_embeds: torch.FloatTensor | None = None use_cache: bool | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → BaseModelOutputWithPast or tuple(torch.FloatTensor)

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.
  • 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).

Returns

BaseModelOutputWithPast or tuple(torch.FloatTensor)

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

The HunYuanVLTextModel 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.FloatTensor 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.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (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.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (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.

HunYuanVLModel

class transformers.HunYuanVLModel

< >

( config: HunYuanVLConfig )

Parameters

  • config (HunYuanVLConfig) — 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 HunYuanVL model which consists of a vision backbone and a language model, without a language modeling head.

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: torch.LongTensor | None = None attention_mask: torch.Tensor | None = None position_ids: torch.LongTensor | None = None past_key_values: transformers.cache_utils.Cache | None = None inputs_embeds: torch.FloatTensor | None = None use_cache: bool | None = None pixel_values: torch.FloatTensor | None = None image_grid_thw: torch.LongTensor | None = None mm_token_type_ids: torch.IntTensor | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → HunYuanVLModelOutputWithPast or tuple(torch.FloatTensor)

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.
  • 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).
  • pixel_values (torch.FloatTensor, optional) — Flat per-patch pixel features produced by the image processor.
  • image_grid_thw (torch.LongTensor of shape (num_images, 3), optional) — The temporal, height and width of feature shape of each image in LLM.
  • mm_token_type_ids (torch.IntTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens matching each modality. For example text (0), image (1), video (2). Multimodal token type ids can be obtained using AutoProcessor. See ProcessorMixin.call() for details.

Returns

HunYuanVLModelOutputWithPast or tuple(torch.FloatTensor)

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

The HunYuanVLModel 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.

  • image_hidden_states (torch.FloatTensor, optional) — Last image features produced by the vision tower and scattered into the language-model token stream.

get_image_features

< >

( pixel_values: FloatTensor image_grid_thw: torch.LongTensor | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → BaseModelOutputWithPooling or tuple(torch.FloatTensor)

Parameters

  • pixel_values (torch.FloatTensor) — Flat per-patch pixel features produced by the image processor.
  • image_grid_thw (torch.LongTensor of shape (num_images, 3), optional) — The temporal, height and width of feature shape of each image in LLM.

Returns

BaseModelOutputWithPooling or tuple(torch.FloatTensor)

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

  • last_hidden_state (torch.FloatTensor 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.FloatTensor 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.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (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.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (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.

HunYuanVLForConditionalGeneration

class transformers.HunYuanVLForConditionalGeneration

< >

( config: HunYuanVLConfig )

Parameters

  • config (HunYuanVLConfig) — 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 Hunyuan Vl Model for token generation conditioned on other modalities (e.g. image-text-to-text generation).

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: torch.LongTensor | None = None attention_mask: torch.Tensor | None = None position_ids: torch.LongTensor | None = None past_key_values: transformers.cache_utils.Cache | None = None inputs_embeds: torch.FloatTensor | None = None labels: torch.LongTensor | None = None use_cache: bool | None = None logits_to_keep: int | torch.Tensor = 0 pixel_values: torch.FloatTensor | None = None image_grid_thw: torch.LongTensor | None = None mm_token_type_ids: torch.IntTensor | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → CausalLMOutputWithPast or tuple(torch.FloatTensor)

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.
  • 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).
  • pixel_values (torch.FloatTensor, optional) — Flat per-patch pixel features produced by the image processor.
  • image_grid_thw (torch.LongTensor of shape (num_images, 3), optional) — The temporal, height and width of feature shape of each image in LLM.
  • mm_token_type_ids (torch.IntTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens matching each modality. For example text (0), image (1), video (2). Multimodal token type ids can be obtained using AutoProcessor. See ProcessorMixin.call() for details.

Returns

CausalLMOutputWithPast or tuple(torch.FloatTensor)

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

The HunYuanVLForConditionalGeneration 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.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) — 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.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (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.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (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.

Example:

>>> from transformers import AutoProcessor, HunYuanVLForConditionalGeneration
>>> import torch

>>> model_id = "tencent/HunyuanOCR"
>>> processor = AutoProcessor.from_pretrained(model_id, backend="pil")
>>> model = HunYuanVLForConditionalGeneration.from_pretrained(
...     model_id, attn_implementation="eager", torch_dtype=torch.bfloat16, device_map="auto"
... )

>>> messages = [
...     {
...         "role": "user",
...         "content": [
...             {"type": "image", "image": "path/to/your/image.jpg"},
...             {"type": "text", "text": "Extract the text from the image."},
...         ],
...     }
... ]
>>> inputs = processor.apply_chat_template(
...     messages,
...     tokenize=True,
...     add_generation_prompt=True,
...     return_tensors="pt",
...     return_dict=True,
...     processor_kwargs={"padding": True},
... )

>>> with torch.no_grad():
...     generated_ids = model.generate(**inputs, max_new_tokens=128)
>>> generated_trimmed = generated_ids[0][inputs["input_ids"].shape[-1]:]
>>> print(processor.decode(generated_trimmed, skip_special_tokens=True))

get_image_features

< >

( pixel_values: FloatTensor image_grid_thw: torch.LongTensor | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] )

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