Instructions to use VisGym/visgym_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VisGym/visgym_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="VisGym/visgym_model")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("VisGym/visgym_model", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use VisGym/visgym_model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "VisGym/visgym_model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "VisGym/visgym_model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/VisGym/visgym_model
- SGLang
How to use VisGym/visgym_model 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 "VisGym/visgym_model" \ --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": "VisGym/visgym_model", "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 "VisGym/visgym_model" \ --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": "VisGym/visgym_model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use VisGym/visgym_model with Docker Model Runner:
docker model run hf.co/VisGym/visgym_model
Add model card metadata and resource links
#1
by nielsr HF Staff - opened
Hi, I'm Niels from the community science team at Hugging Face. I've opened this PR to improve your model card with relevant metadata and resource links.
This update includes:
- Metadata for
pipeline_tag(image-text-to-text) andlibrary_name(transformers). - Links to the research paper, project page, and GitHub repository.
- A brief description of the VisGym framework.
This helps make your model more discoverable and provides users with the necessary context to use it effectively.
Junyi42 changed pull request status to merged