Instructions to use kjunh/v1-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kjunh/v1-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="kjunh/v1-7B")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kjunh/v1-7B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use kjunh/v1-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kjunh/v1-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kjunh/v1-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/kjunh/v1-7B
- SGLang
How to use kjunh/v1-7B 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 "kjunh/v1-7B" \ --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": "kjunh/v1-7B", "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 "kjunh/v1-7B" \ --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": "kjunh/v1-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use kjunh/v1-7B with Docker Model Runner:
docker model run hf.co/kjunh/v1-7B
Improve model card with pipeline tag and library name
#1
by nielsr HF Staff - opened
This PR improves the model card by adding the pipeline_tag and library_name metadata. The pipeline_tag is set to image-text-to-text as the model processes both image and text data to generate text. The library_name is set to transformers based on the model's reliance on the Transformers library. This ensures the model is properly categorized on the Hugging Face Hub and allows users to easily discover it using relevant search filters.
kjunh changed pull request status to merged