Image-Text-to-Text
Transformers
PyTorch
multilingual
internvl_chat
feature-extraction
internvl
custom_code
conversational
Instructions to use OpenGVLab/InternVL2-40B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/InternVL2-40B-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL2-40B-AWQ", trust_remote_code=True) 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?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenGVLab/InternVL2-40B-AWQ", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use OpenGVLab/InternVL2-40B-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenGVLab/InternVL2-40B-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenGVLab/InternVL2-40B-AWQ", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/OpenGVLab/InternVL2-40B-AWQ
- SGLang
How to use OpenGVLab/InternVL2-40B-AWQ with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "OpenGVLab/InternVL2-40B-AWQ" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenGVLab/InternVL2-40B-AWQ", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "OpenGVLab/InternVL2-40B-AWQ" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenGVLab/InternVL2-40B-AWQ", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use OpenGVLab/InternVL2-40B-AWQ with Docker Model Runner:
docker model run hf.co/OpenGVLab/InternVL2-40B-AWQ
fix compatibility issue for transformers 4.46+
Browse files- README.md +9 -2
- config.json +1 -1
- configuration_internvl_chat.py +2 -2
- modeling_intern_vit.py +1 -0
README.md
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- custom_code
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base_model: OpenGVLab/InternVL2-40B
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base_model_relation: quantized
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---
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# InternVL2-40B-AWQ
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[\[π GitHub\]](https://github.com/OpenGVLab/InternVL) [\[π Blog\]](https://internvl.github.io/blog/) [\[π InternVL 1.0
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[\[π¨οΈ Chat Demo\]](https://internvl.opengvlab.com/) [\[π€ HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[π Quick Start\]](#quick-start) [\[π δΈζθ§£θ―»\]](https://zhuanlan.zhihu.com/p/706547971) [\[π Documents\]](https://internvl.readthedocs.io/en/latest/)
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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=
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```
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This article comprises the following sections:
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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{chen2023internvl,
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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 Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng},
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- custom_code
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base_model: OpenGVLab/InternVL2-40B
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base_model_relation: quantized
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new_version: OpenGVLab/InternVL2_5-40B-AWQ
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---
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# InternVL2-40B-AWQ
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[\[π GitHub\]](https://github.com/OpenGVLab/InternVL) [\[π Blog\]](https://internvl.github.io/blog/) [\[π InternVL 1.0\]](https://arxiv.org/abs/2312.14238) [\[π InternVL 1.5\]](https://arxiv.org/abs/2404.16821) [\[π Mini-InternVL\]](https://arxiv.org/abs/2410.16261)
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[\[π¨οΈ Chat Demo\]](https://internvl.opengvlab.com/) [\[π€ HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[π Quick Start\]](#quick-start) [\[π δΈζθ§£θ―»\]](https://zhuanlan.zhihu.com/p/706547971) [\[π Documents\]](https://internvl.readthedocs.io/en/latest/)
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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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If you find this project useful in your research, please consider citing:
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```BibTeX
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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{chen2023internvl,
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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 Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng},
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config.json
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_implementation": "eager",
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"bad_words_ids": null,
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"begin_suppress_tokens": null,
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"bos_token_id": 1,
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"architectures": [
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"LlamaForCausalLM"
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],
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"attn_implementation": "eager",
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bad_words_ids": null,
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"begin_suppress_tokens": null,
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"bos_token_id": 1,
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configuration_internvl_chat.py
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super().__init__(**kwargs)
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if vision_config is None:
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vision_config = {}
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logger.info('vision_config is None. Initializing the InternVisionConfig with default values.')
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if llm_config is None:
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llm_config = {}
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logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
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self.vision_config = InternVisionConfig(**vision_config)
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super().__init__(**kwargs)
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if vision_config is None:
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vision_config = {'architectures': ['InternVisionModel']}
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logger.info('vision_config is None. Initializing the InternVisionConfig with default values.')
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if llm_config is None:
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llm_config = {'architectures': ['LlamaForCausalLM']}
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logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
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self.vision_config = InternVisionConfig(**vision_config)
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modeling_intern_vit.py
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# Copyright (c) 2024 OpenGVLab
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# Licensed under The MIT License [see LICENSE for details]
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# --------------------------------------------------------
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from typing import Optional, Tuple, Union
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import torch
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# Copyright (c) 2024 OpenGVLab
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# Licensed under The MIT License [see LICENSE for details]
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# --------------------------------------------------------
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from typing import Optional, Tuple, Union
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import torch
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