Internvl3 8B Hf

🚀 InternVL3-8B Transformers 🤗 实现

本项目是 OpenGVLab/InternVL3-8B 模型的 Hugging Face 🤗 Transformers 实现，功能上与原 OpenGVLab 版本等效。作为原生 Transformers 模型，它支持核心库的各种特性，如多种注意力机制实现（包括 SDPA 和 FA2），并能对图像、视频和文本输入进行高效的批量推理。

[📜 InternVL 1.0] [📜 InternVL 1.5] [📜 InternVL 2.5] [📜 InternVL2.5-MPO] [📜 InternVL3]

[🆕 博客] [🗨️ 聊天演示] [🤗 HF 演示] [🚀 快速开始] [📖 文档]

🚀 快速开始

本仓库包含了 OpenGVLab/InternVL3-8B 模型的 Hugging Face 🤗 Transformers 实现。它在功能上与 OpenGVLab 的原始版本相当。作为一个原生的 Transformers 模型，它支持核心库的特性，如各种注意力实现（包括 SDPA 和 FA2），并能够对交错的图像、视频和文本输入进行高效的批量推理。

✨ 主要特性

我们推出了 InternVL3，这是一系列先进的多模态大语言模型（MLLM），展现出卓越的整体性能。与 InternVL 2.5 相比，InternVL3 具有更出色的多模态感知和推理能力，并且进一步扩展了其多模态能力，涵盖了工具使用、GUI 代理、工业图像分析、3D 视觉感知等领域。此外，我们将 InternVL3 与 Qwen2.5 聊天模型进行了比较，InternVL3 的语言组件初始化使用了 Qwen2.5 相应的预训练基础模型。得益于原生多模态预训练，InternVL3 系列在整体文本性能上甚至优于 Qwen2.5 系列。

你可以在原始检查点 OpenGVLab/InternVL3-8B 中找到更多关于 InternVL3 系列的信息。

💻 使用示例

基础用法

使用 Pipeline 进行推理

以下是如何使用 image-text-to-text 管道，仅用几行代码就可以对 InternVL3 模型进行推理：

>>> from transformers import pipeline

>>> messages = [
...     {
...         "role": "user",
...         "content": [
...             {
...                 "type": "image",
...                 "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg",
...             },
...             {"type": "text", "text": "Describe this image."},
...         ],
...     },
... ]

>>> pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL3-8B-hf")
>>> outputs = pipe(text=messages, max_new_tokens=50, return_full_text=False)
>>> outputs[0]["generated_text"]
'The image showcases a vibrant scene of nature, featuring several flowers and a bee. \n\n1. **Foreground Flowers**: \n   - The primary focus is on a large, pink cosmos flower with a prominent yellow center. The petals are soft and slightly r'

单张图像推理

此示例展示了如何使用聊天模板对 InternVL 模型进行单张图像推理。

⚠️ 重要提示

请注意，该模型是使用特定的聊天提示格式进行训练的。使用 processor.apply_chat_template(my_conversation_dict) 来正确格式化你的提示。

>>> from transformers import AutoProcessor, AutoModelForImageTextToText
>>> import torch

>>> torch_device = "cuda"
>>> model_checkpoint = "OpenGVLab/InternVL3-8B-hf"
>>> processor = AutoProcessor.from_pretrained(model_checkpoint)
>>> model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)

>>> messages = [
...     {
...         "role": "user",
...         "content": [
...             {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
...             {"type": "text", "text": "Please describe the image explicitly."},
...         ],
...     }
... ]

>>> inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16)

>>> generate_ids = model.generate(**inputs, max_new_tokens=50)
>>> decoded_output = processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)

>>> decoded_output
'The image shows two cats lying on a pink blanket. The cat on the left is a tabby with a mix of brown, black, and white fur, and it appears to be sleeping with its head resting on the blanket. The cat on the'

纯文本生成

此示例展示了如何在不提供任何图像输入的情况下，使用 InternVL 模型生成文本。

>>> from transformers import AutoProcessor, AutoModelForImageTextToText
>>> import torch

>>> torch_device = "cuda"
>>> model_checkpoint = "OpenGVLab/InternVL3-8B-hf"
>>> processor = AutoProcessor.from_pretrained(model_checkpoint)
>>> model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)

>>> messages = [
...     {
...         "role": "user",
...         "content": [
...             {"type": "text", "text": "Write a haiku"},
...         ],
...     }
... ]

>>> inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(torch_device, dtype=torch.bfloat16)

>>> generate_ids = model.generate(**inputs, max_new_tokens=50)
>>> decoded_output = processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)

>>> print(decoded_output)
"Whispers of dawn,\nSilent whispers of the night,\nNew day's light begins."

高级用法

批量图像和文本输入

InternVL 模型还支持批量图像和文本输入。

>>> from transformers import AutoProcessor, AutoModelForImageTextToText
>>> import torch

>>> torch_device = "cuda"
>>> model_checkpoint = "OpenGVLab/InternVL3-8B-hf"
>>> processor = AutoProcessor.from_pretrained(model_checkpoint)
>>> model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)

>>> messages = [
...     [
...         {
...             "role": "user",
...             "content": [
...                 {"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
...                 {"type": "text", "text": "Write a haiku for this image"},
...             ],
...         },
...     ],
...     [
...         {
...             "role": "user",
...             "content": [
...                 {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
...                 {"type": "text", "text": "Describe this image"},
...             ],
...         },
...     ],
... ]


>>> inputs = processor.apply_chat_template(messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16)

>>> output = model.generate(**inputs, max_new_tokens=25)

>>> decoded_outputs = processor.batch_decode(output, skip_special_tokens=True)
>>> decoded_outputs
["user\n\nWrite a haiku for this image\nassistant\nSilky lake,  \nWooden pier,  \nNature's peace.",
 'user\n\nDescribe this image\nassistant\nThe image shows a street scene with a traditional Chinese archway, known as a "Chinese Gate" or "Chinese Gate of']

批量多图像输入

InternVL 模型的此实现支持每个文本对应不同数量图像的批量文本 - 图像输入。

>>> from transformers import AutoProcessor, AutoModelForImageTextToText
>>> import torch

>>> torch_device = "cuda"
>>> model_checkpoint = "OpenGVLab/InternVL3-8B-hf"
>>> processor = AutoProcessor.from_pretrained(model_checkpoint)
>>> model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)

>>> messages = [
...     [
...         {
...             "role": "user",
...             "content": [
...                 {"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
...                 {"type": "text", "text": "Write a haiku for this image"},
...             ],
...         },
...     ],
...     [
...         {
...             "role": "user",
...             "content": [
...                 {"type": "image", "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"},
...                 {"type": "image", "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg"},
...                 {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
...             ],
...         },
...     ],
>>> ]

>>> inputs = processor.apply_chat_template(messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16)

>>> output = model.generate(**inputs, max_new_tokens=25)

>>> decoded_outputs = processor.batch_decode(output, skip_special_tokens=True)
>>> decoded_outputs
["user\n\nWrite a haiku for this image\nassistant\nSilky lake,  \nWooden pier,  \nNature's peace.",
 'user\n\n\nThese images depict two different landmarks. Can you identify them?\nassistant\nYes, these images depict the Statue of Liberty and the Golden Gate Bridge.']

视频输入

InternVL 模型还可以处理视频输入。以下是如何使用聊天模板对视频输入进行推理的示例。

>>> from transformers import AutoProcessor, AutoModelForImageTextToText, BitsAndBytesConfig

>>> model_checkpoint = "OpenGVLab/InternVL3-8B-hf"
>>> quantization_config = BitsAndBytesConfig(load_in_4bit=True)
>>> processor = AutoProcessor.from_pretrained(model_checkpoint)
>>> model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, quantization_config=quantization_config)

>>> messages = [
...     {
...         "role": "user",
...         "content": [
...             {
...                 "type": "video",
...                 "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4",
...             },
...             {"type": "text", "text": "What type of shot is the man performing?"},
...         ],
...     }
>>> ]
>>> inputs = processor.apply_chat_template(
...     messages,
...     return_tensors="pt",
...     add_generation_prompt=True,
...     tokenize=True,
...     return_dict=True,
>>> ).to(model.device, dtype=torch.float16)

>>> output = model.generate(**inputs, max_new_tokens=25)

>>> decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
>>> decoded_output
'The man is performing a forehand shot.'

交错的图像和视频输入

此示例展示了如何使用聊天模板处理包含交错图像和视频输入的批量聊天对话。

>>> from transformers import AutoProcessor, AutoModelForImageTextToText, BitsAndBytesConfig
>>> import torch

>>> torch_device = "cuda"
>>> model_checkpoint = "OpenGVLab/InternVL3-8B-hf"
>>> processor = AutoProcessor.from_pretrained(model_checkpoint)
>>> model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)

>>> messages = [
...     [
...         {
...             "role": "user",
...             "content": [
...                 {"type": "image", "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"},
...                 {"type": "image", "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg"},
...                 {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
...             ],
...         },
...     ],
...     [
...         {
...             "role": "user",
...             "content": [
...                 {"type": "video", "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4"},
...                 {"type": "text", "text": "What type of shot is the man performing?"},
...             ],
...         },
...     ],
...     [
...         {
...             "role": "user",
...             "content": [
...                 {"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
...                 {"type": "text", "text": "Write a haiku for this image"},
...             ],
...         },
...     ],
>>> ]
>>> inputs = processor.apply_chat_template(
...     messages,
...     padding=True,
...     add_generation_prompt=True,
...     tokenize=True,
...     return_dict=True,
...     return_tensors="pt",
>>> ).to(model.device, dtype=torch.bfloat16)

>>> outputs = model.generate(**inputs, max_new_tokens=25)

>>> decoded_outputs = processor.batch_decode(outputs, skip_special_tokens=True)
>>> decoded_outputs
['user\n\n\nThese images depict two different landmarks. Can you identify them?\nassistant\nThe images depict the Statue of Liberty and the Golden Gate Bridge.',
 'user\nFrame1: \nFrame2: \nFrame3: \nFrame4: \nFrame5: \nFrame6: \nFrame7: \nFrame8: \nWhat type of shot is the man performing?\nassistant\nA forehand shot',
 "user\n\nWrite a haiku for this image\nassistant\nSilky lake,  \nWooden pier,  \nNature's peace."]

📄 许可证

本项目根据 MIT 许可证发布。本项目使用了预训练的 Qwen2.5 作为组件，该组件遵循 Qwen 许可证。

📚 详细文档

如果你在研究中发现本项目很有用，请考虑引用：

@article{chen2024expanding,
  title={Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling},
  author={Chen, Zhe and Wang, Weiyun and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Cui, Erfei and Zhu, Jinguo and Ye, Shenglong and Tian, Hao and Liu, Zhaoyang and others},
  journal={arXiv preprint arXiv:2412.05271},
  year={2024}
}
@article{wang2024mpo,
  title={Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization},
  author={Wang, Weiyun and Chen, Zhe and Wang, Wenhai and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Zhu, Jinguo and Zhu, Xizhou and Lu, Lewei and Qiao, Yu and Dai, Jifeng},
  journal={arXiv preprint arXiv:2411.10442},
  year={2024}
}
@article{chen2024far,
  title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites},
  author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others},
  journal={arXiv preprint arXiv:2404.16821},
  year={2024}
}
@inproceedings{chen2024internvl,
  title={Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks},
  author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and others},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={24185--24198},
  year={2024}
}