Image-Text-to-Text
Transformers
PyTorch
multilingual
internvl_chat
feature-extraction
internvl
custom_code
Instructions to use OpenGVLab/InternVL-Chat-V1-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/InternVL-Chat-V1-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL-Chat-V1-1", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenGVLab/InternVL-Chat-V1-1", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use OpenGVLab/InternVL-Chat-V1-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenGVLab/InternVL-Chat-V1-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenGVLab/InternVL-Chat-V1-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/OpenGVLab/InternVL-Chat-V1-1
- SGLang
How to use OpenGVLab/InternVL-Chat-V1-1 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/InternVL-Chat-V1-1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenGVLab/InternVL-Chat-V1-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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/InternVL-Chat-V1-1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenGVLab/InternVL-Chat-V1-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use OpenGVLab/InternVL-Chat-V1-1 with Docker Model Runner:
docker model run hf.co/OpenGVLab/InternVL-Chat-V1-1

- Xet hash:
- b4ea0554bcdbef4b86df5b3645dc7a85c27c21e9c9b8ab5890e1158252863424
- Size of remote file:
- 126 kB
- SHA256:
- 08487494b8dc08d44bc36491adf3ab89ff30d13a3122da86f3cd67cad89eeee8
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