# HunYuanVL

## Overview

HunYuanVL is a vision-language model for image-text understanding and generation
proposed in [HunyuanOCR Technical Report
](https://huggingface.co/papers/2511.19575). 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`]((https://huggingface.co/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

- [tencent/HunyuanOCR](https://huggingface.co/tencent/HunyuanOCR) for OCR and document extraction workloads.

## 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

```python
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[[transformers.HunYuanVLProcessor]]

#### transformers.HunYuanVLProcessor[[transformers.HunYuanVLProcessor]]

[Source](https://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/processing_hunyuan_vl.py#L36)

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

[HunYuanVLProcessor](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.HunYuanVLProcessor) offers all the functionalities of [HunYuanVLImageProcessor](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.HunYuanVLImageProcessor) and [Qwen2Tokenizer](/docs/transformers/v5.13.0/en/model_doc/qwen2#transformers.Qwen2Tokenizer). See the
[~HunYuanVLImageProcessor](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.HunYuanVLImageProcessor) and [~Qwen2Tokenizer](/docs/transformers/v5.13.0/en/model_doc/qwen2#transformers.Qwen2Tokenizer) for more information.

__call__transformers.HunYuanVLProcessor.__call__https://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/processing_utils.py#L643[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None"}, {"name": "text", "val": ": str | list[str] | list[list[str]] | None = None"}, {"name": "videos", "val": ": 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"}, {"name": "audio", "val": ": typing.Union[numpy.ndarray, ForwardRef('torch.Tensor'), collections.abc.Sequence[numpy.ndarray], collections.abc.Sequence['torch.Tensor'], NoneType] = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.processing_utils.ProcessingKwargs]"}]- **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](/docs/transformers/v5.13.0/en/internal/file_utils#transformers.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](/docs/transformers/v5.13.0/en/main_classes/processors#transformers.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.0

**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.

## HunYuanVLImageProcessor[[transformers.HunYuanVLImageProcessor]]

#### transformers.HunYuanVLImageProcessor[[transformers.HunYuanVLImageProcessor]]

[Source](https://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/image_processing_hunyuan_vl.py#L88)

Constructs a HunYuanVLImageProcessor image processor.

get_number_of_image_patchestransformers.HunYuanVLImageProcessor.get_number_of_image_patcheshttps://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/image_processing_hunyuan_vl.py#L229[{"name": "height", "val": ": int"}, {"name": "width", "val": ": int"}, {"name": "images_kwargs", "val": " = None"}]
Return the `(grid_h, grid_w)` patch counts used by HunYuanVL token accounting.

**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](/docs/transformers/v5.13.0/en/main_classes/processors#transformers.ImagesKwargs), *optional*) : Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.
#### preprocess[[transformers.HunYuanVLImageProcessor.preprocess]]

[Source](https://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/image_processing_hunyuan_vl.py#L137)

**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](/docs/transformers/v5.13.0/en/internal/file_utils#transformers.TensorType), *optional*) : Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

- ****kwargs** ([ImagesKwargs](/docs/transformers/v5.13.0/en/main_classes/processors#transformers.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[[transformers.HunYuanVLImageProcessorPil]]

#### transformers.HunYuanVLImageProcessorPil[[transformers.HunYuanVLImageProcessorPil]]

[Source](https://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/image_processing_pil_hunyuan_vl.py#L89)

get_number_of_image_patchestransformers.HunYuanVLImageProcessorPil.get_number_of_image_patcheshttps://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/image_processing_pil_hunyuan_vl.py#L240[{"name": "height", "val": ": int"}, {"name": "width", "val": ": int"}, {"name": "images_kwargs", "val": " = None"}]
Return the `(grid_h, grid_w)` patch counts used by HunYuanVL token accounting.
#### preprocess[[transformers.HunYuanVLImageProcessorPil.preprocess]]

[Source](https://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/image_processing_pil_hunyuan_vl.py#L138)

**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](/docs/transformers/v5.13.0/en/internal/file_utils#transformers.TensorType), *optional*) : Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.

- ****kwargs** ([ImagesKwargs](/docs/transformers/v5.13.0/en/main_classes/processors#transformers.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[[transformers.HunYuanVLConfig]]

#### transformers.HunYuanVLConfig[[transformers.HunYuanVLConfig]]

[Source](https://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/configuration_hunyuan_vl.py#L235)

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](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.HunYuanVLTextConfig) (text backbone) and a [HunYuanVLVisionConfig](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.HunYuanVLVisionConfig) (vision tower) plus a few token ids that
delimit image spans in multimodal prompts.

Example:

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

**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.

## HunYuanVLVisionConfig[[transformers.HunYuanVLVisionConfig]]

#### transformers.HunYuanVLVisionConfig[[transformers.HunYuanVLVisionConfig]]

[Source](https://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/configuration_hunyuan_vl.py#L35)

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

Example:

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

**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](https://huggingface.co/papers/2305.13245). 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.

## HunYuanVLTextConfig[[transformers.HunYuanVLTextConfig]]

#### transformers.HunYuanVLTextConfig[[transformers.HunYuanVLTextConfig]]

[Source](https://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/configuration_hunyuan_vl.py#L106)

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

Inherits the standard fields from [HunYuanDenseV1Config](/docs/transformers/v5.13.0/en/model_doc/hunyuan_v1_dense#transformers.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`.

**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](https://huggingface.co/papers/2305.13245). 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](/docs/transformers/v5.13.0/en/model_doc/hunyuan_v1_dense#transformers.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](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is necessary to ensure exact reproducibility of the pretraining results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).

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](/docs/transformers/v5.13.0/en/model_doc/hunyuan_v1_dense#transformers.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.

## HunYuanVLVisionTransformer[[transformers.HunYuanVLVisionTransformer]]

#### transformers.HunYuanVLVisionTransformer[[transformers.HunYuanVLVisionTransformer]]

[Source](https://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/modeling_hunyuan_vl.py#L698)

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.

forwardtransformers.HunYuanVLVisionTransformer.forwardhttps://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/modeling_hunyuan_vl.py#L724[{"name": "pixel_values", "val": ": Tensor"}, {"name": "grid_thw", "val": ": LongTensor"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **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.0[BaseModelOutputWithPooling](/docs/transformers/v5.13.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPooling](/docs/transformers/v5.13.0/en/main_classes/output#transformers.modeling_outputs.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](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.HunYuanVLConfig)) and inputs.
The [HunYuanVLVisionTransformer](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.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.

**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](/docs/transformers/v5.13.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)``

A [BaseModelOutputWithPooling](/docs/transformers/v5.13.0/en/main_classes/output#transformers.modeling_outputs.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](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.HunYuanVLConfig)) and inputs.

## HunYuanVLTextModel[[transformers.HunYuanVLTextModel]]

#### transformers.HunYuanVLTextModel[[transformers.HunYuanVLTextModel]]

[Source](https://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/modeling_hunyuan_vl.py#L761)

The bare Hunyuan Vl Text Model outputting raw hidden-states without any specific head on to.

This model inherits from [PreTrainedModel](/docs/transformers/v5.13.0/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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.

forwardtransformers.HunYuanVLTextModel.forwardhttps://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/modeling_hunyuan_vl.py#L783[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "use_cache", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **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](/docs/transformers/v5.13.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.13.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/v5.13.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#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?](../glossary#attention-mask)
- **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?](../glossary#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](/docs/transformers/v5.13.0/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
  If no `past_key_values` are passed, [DynamicCache](/docs/transformers/v5.13.0/en/internal/generation_utils#transformers.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`).0[BaseModelOutputWithPast](/docs/transformers/v5.13.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPast](/docs/transformers/v5.13.0/en/main_classes/output#transformers.modeling_outputs.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](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.HunYuanVLConfig)) and inputs.
The [HunYuanVLTextModel](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.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](/docs/transformers/v5.13.0/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

  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.

**Parameters:**

config ([HunYuanVLTextConfig](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.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()](/docs/transformers/v5.13.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

`[BaseModelOutputWithPast](/docs/transformers/v5.13.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or `tuple(torch.FloatTensor)``

A [BaseModelOutputWithPast](/docs/transformers/v5.13.0/en/main_classes/output#transformers.modeling_outputs.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](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.HunYuanVLConfig)) and inputs.

## HunYuanVLModel[[transformers.HunYuanVLModel]]

#### transformers.HunYuanVLModel[[transformers.HunYuanVLModel]]

[Source](https://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/modeling_hunyuan_vl.py#L862)

The HunYuanVL model which consists of a vision backbone and a language model, without a language modeling head.

This model inherits from [PreTrainedModel](/docs/transformers/v5.13.0/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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.

forwardtransformers.HunYuanVLModel.forwardhttps://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/modeling_hunyuan_vl.py#L1076[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "use_cache", "val": ": bool | None = None"}, {"name": "pixel_values", "val": ": torch.FloatTensor | None = None"}, {"name": "image_grid_thw", "val": ": torch.LongTensor | None = None"}, {"name": "mm_token_type_ids", "val": ": torch.IntTensor | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **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](/docs/transformers/v5.13.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.13.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/v5.13.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#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?](../glossary#attention-mask)
- **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?](../glossary#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](/docs/transformers/v5.13.0/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
  If no `past_key_values` are passed, [DynamicCache](/docs/transformers/v5.13.0/en/internal/generation_utils#transformers.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](/docs/transformers/v5.13.0/en/model_doc/auto#transformers.AutoProcessor). See [ProcessorMixin.__call__()](/docs/transformers/v5.13.0/en/main_classes/processors#transformers.ProcessorMixin.__call__) for details.0`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](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.HunYuanVLConfig)) and inputs.
The [HunYuanVLModel](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.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.

**Parameters:**

config ([HunYuanVLConfig](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.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()](/docs/transformers/v5.13.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**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](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.HunYuanVLConfig)) and inputs.
#### get_image_features[[transformers.HunYuanVLModel.get_image_features]]

[Source](https://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/modeling_hunyuan_vl.py#L1002)

- **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.

**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](/docs/transformers/v5.13.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)``

A [BaseModelOutputWithPooling](/docs/transformers/v5.13.0/en/main_classes/output#transformers.modeling_outputs.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](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.HunYuanVLConfig)) and inputs.

## HunYuanVLForConditionalGeneration[[transformers.HunYuanVLForConditionalGeneration]]

#### transformers.HunYuanVLForConditionalGeneration[[transformers.HunYuanVLForConditionalGeneration]]

[Source](https://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/modeling_hunyuan_vl.py#L1145)

The Hunyuan Vl Model for token generation conditioned on other modalities (e.g. image-text-to-text generation).

This model inherits from [PreTrainedModel](/docs/transformers/v5.13.0/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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.

forwardtransformers.HunYuanVLForConditionalGeneration.forwardhttps://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/modeling_hunyuan_vl.py#L1166[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "use_cache", "val": ": bool | None = None"}, {"name": "logits_to_keep", "val": ": int | torch.Tensor = 0"}, {"name": "pixel_values", "val": ": torch.FloatTensor | None = None"}, {"name": "image_grid_thw", "val": ": torch.LongTensor | None = None"}, {"name": "mm_token_type_ids", "val": ": torch.IntTensor | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **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](/docs/transformers/v5.13.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.13.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/v5.13.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#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?](../glossary#attention-mask)
- **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?](../glossary#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](/docs/transformers/v5.13.0/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
  If no `past_key_values` are passed, [DynamicCache](/docs/transformers/v5.13.0/en/internal/generation_utils#transformers.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](/docs/transformers/v5.13.0/en/model_doc/auto#transformers.AutoProcessor). See [ProcessorMixin.__call__()](/docs/transformers/v5.13.0/en/main_classes/processors#transformers.ProcessorMixin.__call__) for details.0[CausalLMOutputWithPast](/docs/transformers/v5.13.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast) or `tuple(torch.FloatTensor)`A [CausalLMOutputWithPast](/docs/transformers/v5.13.0/en/main_classes/output#transformers.modeling_outputs.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](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.HunYuanVLConfig)) and inputs.
The [HunYuanVLForConditionalGeneration](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.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](/docs/transformers/v5.13.0/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

  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:

```python
>>> 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))
```

**Parameters:**

config ([HunYuanVLConfig](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.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()](/docs/transformers/v5.13.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

`[CausalLMOutputWithPast](/docs/transformers/v5.13.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast) or `tuple(torch.FloatTensor)``

A [CausalLMOutputWithPast](/docs/transformers/v5.13.0/en/main_classes/output#transformers.modeling_outputs.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](/docs/transformers/v5.13.0/en/model_doc/hunyuan_vl#transformers.HunYuanVLConfig)) and inputs.
#### get_image_features[[transformers.HunYuanVLForConditionalGeneration.get_image_features]]

[Source](https://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/hunyuan_vl/modeling_hunyuan_vl.py#L1158)