Instructions to use OpenGVLab/Mini-InternVL-Chat-4B-V1-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/Mini-InternVL-Chat-4B-V1-5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="OpenGVLab/Mini-InternVL-Chat-4B-V1-5", 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/Mini-InternVL-Chat-4B-V1-5", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OpenGVLab/Mini-InternVL-Chat-4B-V1-5 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenGVLab/Mini-InternVL-Chat-4B-V1-5" # 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/Mini-InternVL-Chat-4B-V1-5", "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/Mini-InternVL-Chat-4B-V1-5
- SGLang
How to use OpenGVLab/Mini-InternVL-Chat-4B-V1-5 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/Mini-InternVL-Chat-4B-V1-5" \ --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/Mini-InternVL-Chat-4B-V1-5", "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/Mini-InternVL-Chat-4B-V1-5" \ --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/Mini-InternVL-Chat-4B-V1-5", "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/Mini-InternVL-Chat-4B-V1-5 with Docker Model Runner:
docker model run hf.co/OpenGVLab/Mini-InternVL-Chat-4B-V1-5
doesnt work
when i start this code i get this error: You are not running the flash-attention implementation, expect numerical differences. how to fix it? i have flash_attn installed
Make sure you use transformers==4.37.2. and your GPU version is Ampere or higher (A100, H100 etc). Otherwise, modify the "use_flash_attn" value to "false" in config file and then you can still run it with warning "You are not running the flash-attention implementation, expect numerical differences". I used Tesla 4 to run this demo and the answer still looks fine.
when i start this code i get this error: You are not running the flash-attention implementation, expect numerical differences. how to fix it? i have flash_attn installed
Thank you for your feedback. Now that flash attention is enabled for Phi3, eager attention is automatically used if flash attention is not installed in the environment.