Instructions to use helloollel/vicuna-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use helloollel/vicuna-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="helloollel/vicuna-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("helloollel/vicuna-7b") model = AutoModelForCausalLM.from_pretrained("helloollel/vicuna-7b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use helloollel/vicuna-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "helloollel/vicuna-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "helloollel/vicuna-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/helloollel/vicuna-7b
- SGLang
How to use helloollel/vicuna-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 "helloollel/vicuna-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": "helloollel/vicuna-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 "helloollel/vicuna-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": "helloollel/vicuna-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use helloollel/vicuna-7b with Docker Model Runner:
docker model run hf.co/helloollel/vicuna-7b
How the weights retrieved?
#3
by garlicdevs - opened
Hi, could you please let us know how to retrieve the weight please? Can we use it commercially?
Nope, Vicuna is based on LLaMA, and LLaMA is not commercial, so you cannot use it in a commercial product.
I pip installed fschat, but when I try running the notebook code, I get:
ModuleNotFoundError: No module named 'fastchat.serve.monkey_patch_non_inplace'