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--- |
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license: mit |
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pipeline_tag: image-text-to-text |
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library_name: transformers |
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base_model: OpenGVLab/InternVL2-2B |
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new_version: OpenGVLab/InternVL2_5-2B-AWQ |
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base_model_relation: quantized |
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language: |
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- multilingual |
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tags: |
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- internvl |
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- custom_code |
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--- |
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# InternVL2-2B-AWQ |
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[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238) [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821) [\[📜 Mini-InternVL\]](https://arxiv.org/abs/2410.16261) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271) |
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[\[🆕 Blog\]](https://internvl.github.io/blog/) [\[🗨️ Chat Demo\]](https://internvl.opengvlab.com/) [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[🚀 Quick Start\]](#quick-start) [\[📖 Documents\]](https://internvl.readthedocs.io/en/latest/) |
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## Introduction |
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<div align="center"> |
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<img src="https://raw.githubusercontent.com/InternLM/lmdeploy/0be9e7ab6fe9a066cfb0a09d0e0c8d2e28435e58/resources/lmdeploy-logo.svg" width="450"/> |
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</div> |
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### INT4 Weight-only Quantization and Deployment (W4A16) |
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LMDeploy adopts [AWQ](https://arxiv.org/abs/2306.00978) algorithm for 4bit weight-only quantization. By developed the high-performance cuda kernel, the 4bit quantized model inference achieves up to 2.4x faster than FP16. |
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LMDeploy supports the following NVIDIA GPU for W4A16 inference: |
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- Turing(sm75): 20 series, T4 |
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- Ampere(sm80,sm86): 30 series, A10, A16, A30, A100 |
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- Ada Lovelace(sm90): 40 series |
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Before proceeding with the quantization and inference, please ensure that lmdeploy is installed. |
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```shell |
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pip install lmdeploy>=0.5.3 |
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``` |
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This article comprises the following sections: |
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<!-- toc --> |
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- [Inference](#inference) |
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- [Service](#service) |
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<!-- tocstop --> |
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### Inference |
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Trying the following codes, you can perform the batched offline inference with the quantized model: |
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```python |
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from lmdeploy import pipeline, TurbomindEngineConfig |
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from lmdeploy.vl import load_image |
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model = 'OpenGVLab/InternVL2-2B-AWQ' |
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image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg') |
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backend_config = TurbomindEngineConfig(model_format='awq') |
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pipe = pipeline(model, backend_config=backend_config, log_level='INFO') |
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response = pipe(('describe this image', image)) |
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print(response.text) |
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``` |
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For more information about the pipeline parameters, please refer to [here](https://github.com/InternLM/lmdeploy/blob/main/docs/en/inference/pipeline.md). |
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### Service |
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LMDeploy's `api_server` enables models to be easily packed into services with a single command. The provided RESTful APIs are compatible with OpenAI's interfaces. Below are an example of service startup: |
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```shell |
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lmdeploy serve api_server OpenGVLab/InternVL2-2B-AWQ --backend turbomind --server-port 23333 --model-format awq |
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``` |
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To use the OpenAI-style interface, you need to install OpenAI: |
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```shell |
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pip install openai |
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``` |
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Then, use the code below to make the API call: |
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```python |
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from openai import OpenAI |
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client = OpenAI(api_key='YOUR_API_KEY', base_url='http://0.0.0.0:23333/v1') |
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model_name = client.models.list().data[0].id |
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response = client.chat.completions.create( |
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model=model_name, |
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messages=[{ |
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'role': |
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'user', |
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'content': [{ |
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'type': 'text', |
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'text': 'describe this image', |
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}, { |
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'type': 'image_url', |
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'image_url': { |
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'url': |
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'https://modelscope.oss-cn-beijing.aliyuncs.com/resource/tiger.jpeg', |
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}, |
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}], |
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}], |
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temperature=0.8, |
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top_p=0.8) |
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print(response) |
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``` |
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## License |
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This project is released under the MIT License. This project uses the pre-trained internlm2-chat-1_8b as a component, which is licensed under the Apache License 2.0. |
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## Citation |
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If you find this project useful in your research, please consider citing: |
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```BibTeX |
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@article{chen2024expanding, |
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title={Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling}, |
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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}, |
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journal={arXiv preprint arXiv:2412.05271}, |
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year={2024} |
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} |
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@article{gao2024mini, |
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title={Mini-internvl: A flexible-transfer pocket multimodal model with 5\% parameters and 90\% performance}, |
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author={Gao, Zhangwei and Chen, Zhe and Cui, Erfei and Ren, Yiming and Wang, Weiyun and Zhu, Jinguo and Tian, Hao and Ye, Shenglong and He, Junjun and Zhu, Xizhou and others}, |
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journal={arXiv preprint arXiv:2410.16261}, |
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year={2024} |
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} |
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@article{chen2024far, |
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title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites}, |
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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}, |
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journal={arXiv preprint arXiv:2404.16821}, |
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year={2024} |
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} |
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@inproceedings{chen2024internvl, |
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title={Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks}, |
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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}, |
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
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pages={24185--24198}, |
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year={2024} |
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} |
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``` |
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