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feat: updated model card for qwen_2.5_vl (#37099)

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* feat: updated model card for qwen_2.5_vl

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# Qwen2.5-VL
<div class="flex flex-wrap space-x-1">
<div style="float: right;">
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white"> </div>
</div>
## Overview
# Qwen2.5-VL
The [Qwen2.5-VL](https://qwenlm.github.io/blog/qwen2_5-vl/) model is an update to [Qwen2-VL](https://arxiv.org/abs/2409.12191) from Qwen team, Alibaba Group.
[Qwen2.5-VL](https://huggingface.co/papers/2502.13923) is a multimodal vision-language model, available in 3B, 7B, and 72B parameters, pretrained on 4.1T tokens. The model introduces window attention in the ViT encoder to accelerate training and inference, dynamic FPS sampling on the spatial and temporal dimensions for better video understanding across different sampling rates, and an upgraded MRoPE (multi-resolutional rotary positional encoding) mechanism to better capture and learn temporal dynamics.
The abstract from this update is the following:
*Qwen2.5-VL marks a major step forward from Qwen2-VL, built upon the latest Qwen2.5 LLM. We've accelerated training and testing through the strategic implementation of window attention within the ViT. The ViT architecture itself has been refined with SwiGLU and RMSNorm, aligning it more closely with the LLM's structure. A key innovation is the expansion of native dynamic resolution to encompass the temporal dimension, in addition to spatial aspects. Furthermore, we've upgraded MRoPE, incorporating absolute time alignment on the time axis to allow the model to effectively capture temporal dynamics, regardless of frame rate, leading to superior video understanding.*
You can find all the original Qwen2.5-VL checkpoints under the [Qwen2.5-VL](https://huggingface.co/collections/Qwen/qwen25-vl-6795ffac22b334a837c0f9a5) collection.
## Usage example
> [!TIP]
> Click on the Qwen2.5-VL models in the right sidebar for more examples of how to apply Qwen2.5-VL to different vision and language tasks.
### Single Media inference
The example below demonstrates how to generate text based on an image with [`Pipeline`] or the [`AutoModel`] class.
The model can accept both images and videos as input. Here's an example code for inference.
```python
<hfoptions id="usage">
<hfoption id="Pipeline">
```py
import torch
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from transformers import pipeline
pipe = pipeline(
task="image-text-to-text",
model="Qwen/Qwen2.5-VL-7B-Instruct",
device=0,
torch_dtype=torch.bfloat16
)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
},
{ "type": "text", "text": "Describe this image."},
]
}
]
pipe(text=messages,max_new_tokens=20, return_full_text=False)
# Load the model in half-precision on the available device(s)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", device_map="auto")
```
</hfoption>
<hfoption id="AutoModel">
```py
import torch
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-VL-7B-Instruct",
torch_dtype=torch.float16,
device_map="auto",
attn_implementation="sdpa"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
conversation = [
messages = [
{
"role":"user",
"content":[
{
"type":"image",
"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
"url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
},
{
"type":"text",
@ -60,212 +89,145 @@ conversation = [
}
]
}
]
inputs = processor.apply_chat_template(
conversation,
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt"
).to(model.device)
).to("cuda")
# Inference: Generation of the output
output_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
print(output_text)
# Video
conversation = [
{
"role": "user",
"content": [
{"type": "video", "path": "/path/to/video.mp4"},
{"type": "text", "text": "What happened in the video?"},
],
}
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
inputs = processor.apply_chat_template(
conversation,
video_fps=1,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt"
).to(model.device)
# Inference: Generation of the output
output_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
print(output_text)
```
### Batch Mixed Media Inference
The model can batch inputs composed of mixed samples of various types such as images, videos, and text. Here is an example.
```python
# Conversation for the first image
conversation1 = [
{
"role": "user",
"content": [
{"type": "image", "path": "/path/to/image1.jpg"},
{"type": "text", "text": "Describe this image."}
]
}
]
# Conversation with two images
conversation2 = [
{
"role": "user",
"content": [
{"type": "image", "path": "/path/to/image2.jpg"},
{"type": "image", "path": "/path/to/image3.jpg"},
{"type": "text", "text": "What is written in the pictures?"}
]
}
]
# Conversation with pure text
conversation3 = [
{
"role": "user",
"content": "who are you?"
}
]
# Conversation with mixed midia
conversation4 = [
{
"role": "user",
"content": [
{"type": "image", "path": "/path/to/image3.jpg"},
{"type": "image", "path": "/path/to/image4.jpg"},
{"type": "video", "path": "/path/to/video.jpg"},
{"type": "text", "text": "What are the common elements in these medias?"},
],
}
]
conversations = [conversation1, conversation2, conversation3, conversation4]
# Preparation for batch inference
ipnuts = processor.apply_chat_template(
conversations,
video_fps=1,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt"
).to(model.device)
# Batch Inference
output_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
print(output_text)
```
### Usage Tips
#### Image Resolution trade-off
The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs.
```python
min_pixels = 224*224
max_pixels = 2048*2048
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
```
In case of limited GPU RAM, one can reduce the resolution as follows:
```python
min_pixels = 256*28*28
max_pixels = 1024*28*28
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
```
This ensures each image gets encoded using a number between 256-1024 tokens. The 28 comes from the fact that the model uses a patch size of 14 and a temporal patch size of 2 (14 x 2 = 28).
#### Multiple Image Inputs
By default, images and video content are directly included in the conversation. When handling multiple images, it's helpful to add labels to the images and videos for better reference. Users can control this behavior with the following settings:
```python
conversation = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": "Hello, how are you?"}
]
},
{
"role": "assistant",
"content": "I'm doing well, thank you for asking. How can I assist you today?"
},
{
"role": "user",
"content": [
{"type": "text", "text": "Can you describe these images and video?"},
{"type": "image"},
{"type": "image"},
{"type": "video"},
{"type": "text", "text": "These are from my vacation."}
]
},
{
"role": "assistant",
"content": "I'd be happy to describe the images and video for you. Could you please provide more context about your vacation?"
},
{
"role": "user",
"content": "It was a trip to the mountains. Can you see the details in the images and video?"
}
]
# default:
prompt_without_id = processor.apply_chat_template(conversation, add_generation_prompt=True)
# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Hello, how are you?<|im_end|>\n<|im_start|>assistant\nI'm doing well, thank you for asking. How can I assist you today?<|im_end|>\n<|im_start|>user\nCan you describe these images and video?<|vision_start|><|image_pad|><|vision_end|><|vision_start|><|image_pad|><|vision_end|><|vision_start|><|video_pad|><|vision_end|>These are from my vacation.<|im_end|>\n<|im_start|>assistant\nI'd be happy to describe the images and video for you. Could you please provide more context about your vacation?<|im_end|>\n<|im_start|>user\nIt was a trip to the mountains. Can you see the details in the images and video?<|im_end|>\n<|im_start|>assistant\n'
# add ids
prompt_with_id = processor.apply_chat_template(conversation, add_generation_prompt=True, add_vision_id=True)
# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nPicture 1: <|vision_start|><|image_pad|><|vision_end|>Hello, how are you?<|im_end|>\n<|im_start|>assistant\nI'm doing well, thank you for asking. How can I assist you today?<|im_end|>\n<|im_start|>user\nCan you describe these images and video?Picture 2: <|vision_start|><|image_pad|><|vision_end|>Picture 3: <|vision_start|><|image_pad|><|vision_end|>Video 1: <|vision_start|><|video_pad|><|vision_end|>These are from my vacation.<|im_end|>\n<|im_start|>assistant\nI'd be happy to describe the images and video for you. Could you please provide more context about your vacation?<|im_end|>\n<|im_start|>user\nIt was a trip to the mountains. Can you see the details in the images and video?<|im_end|>\n<|im_start|>assistant\n'
```
#### Flash-Attention 2 to speed up generation
First, make sure to install the latest version of Flash Attention 2:
```bash
pip install -U flash-attn --no-build-isolation
```
Also, you should have hardware that is compatible with FlashAttention 2. Read more about it in the official documentation of the [flash attention repository](https://github.com/Dao-AILab/flash-attention). FlashAttention-2 can only be used when a model is loaded in `torch.float16` or `torch.bfloat16`.
To load and run a model using FlashAttention-2, add `attn_implementation="flash_attention_2"` when loading the model:
```python
from transformers import Qwen2_5_VLForConditionalGeneration
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-VL-7B-Instruct",
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```
</hfoption>
</hfoptions>
Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
The example below uses [torchao](../quantization/torchao) to only quantize the weights to int4.
```python
import torch
from transformers import TorchAoConfig, Gemma3ForConditionalGeneration, AutoProcessor
quantization_config = TorchAoConfig("int4_weight_only", group_size=128)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-VL-7B-Instruct",
torch_dtype=torch.bfloat16,
device_map="auto",
quantization_config=quantization_config
)
```
### Notes
- Use Qwen2.5-VL for video inputs by setting `"type": "video"` as shown below.
```python
conversation = [
{
"role": "user",
"content": [
{"type": "video", "path": "/path/to/video.mp4"},
{"type": "text", "text": "What happened in the video?"},
],
}
]
inputs = processor.apply_chat_template(
conversation,
video_fps=1,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt"
).to(model.device)
# Inference: Generation of the output
output_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
print(output_text)
```
- Use Qwen2.5-VL for a mixed batch of inputs (images, videos, text). Add labels when handling multiple images or videos for better reference
as show below.
```python
import torch
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-VL-7B-Instruct",
torch_dtype=torch.float16,
device_map="auto",
attn_implementation="sdpa"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
conversation = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": "Hello, how are you?"}
]
},
{
"role": "assistant",
"content": "I'm doing well, thank you for asking. How can I assist you today?"
},
{
"role": "user",
"content": [
{"type": "text", "text": "Can you describe these images and video?"},
{"type": "image"},
{"type": "image"},
{"type": "video"},
{"type": "text", "text": "These are from my vacation."}
]
},
{
"role": "assistant",
"content": "I'd be happy to describe the images and video for you. Could you please provide more context about your vacation?"
},
{
"role": "user",
"content": "It was a trip to the mountains. Can you see the details in the images and video?"
}
]
# default:
prompt_without_id = processor.apply_chat_template(conversation, add_generation_prompt=True)
# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Hello, how are you?<|im_end|>\n<|im_start|>assistant\nI'm doing well, thank you for asking. How can I assist you today?<|im_end|>\n<|im_start|>user\nCan you describe these images and video?<|vision_start|><|image_pad|><|vision_end|><|vision_start|><|image_pad|><|vision_end|><|vision_start|><|video_pad|><|vision_end|>These are from my vacation.<|im_end|>\n<|im_start|>assistant\nI'd be happy to describe the images and video for you. Could you please provide more context about your vacation?<|im_end|>\n<|im_start|>user\nIt was a trip to the mountains. Can you see the details in the images and video?<|im_end|>\n<|im_start|>assistant\n'
# add ids
prompt_with_id = processor.apply_chat_template(conversation, add_generation_prompt=True, add_vision_id=True)
# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nPicture 1: <|vision_start|><|image_pad|><|vision_end|>Hello, how are you?<|im_end|>\n<|im_start|>assistant\nI'm doing well, thank you for asking. How can I assist you today?<|im_end|>\n<|im_start|>user\nCan you describe these images and video?Picture 2: <|vision_start|><|image_pad|><|vision_end|>Picture 3: <|vision_start|><|image_pad|><|vision_end|>Video 1: <|vision_start|><|video_pad|><|vision_end|>These are from my vacation.<|im_end|>\n<|im_start|>assistant\nI'd be happy to describe the images and video for you. Could you please provide more context about your vacation?<|im_end|>\n<|im_start|>user\nIt was a trip to the mountains. Can you see the details in the images and video?<|im_end|>\n<|im_start|>assistant\n'
```
- Use the `min_pixels` and `max_pixels` parameters in [`AutoProcessor`] to set the resolution.
```python
min_pixels = 224*224
max_pixels = 2048*2048
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
```
Higher resolution can require more compute whereas reducing the resolution can save memory as follows:
```python
min_pixels = 256*28*28
max_pixels = 1024*28*28
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
```
## Qwen2_5_VLConfig
[[autodoc]] Qwen2_5_VLConfig