Instructions to use AlekseyKorshuk/vicuna-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlekseyKorshuk/vicuna-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AlekseyKorshuk/vicuna-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AlekseyKorshuk/vicuna-7b") model = AutoModelForCausalLM.from_pretrained("AlekseyKorshuk/vicuna-7b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use AlekseyKorshuk/vicuna-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AlekseyKorshuk/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": "AlekseyKorshuk/vicuna-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AlekseyKorshuk/vicuna-7b
- SGLang
How to use AlekseyKorshuk/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 "AlekseyKorshuk/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": "AlekseyKorshuk/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 "AlekseyKorshuk/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": "AlekseyKorshuk/vicuna-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AlekseyKorshuk/vicuna-7b with Docker Model Runner:
docker model run hf.co/AlekseyKorshuk/vicuna-7b
Vicuna 7B without "ethics" filtering
This repository contains an alternative version of the Vicuna 7B model.
This model was natively fine-tuned using ShareGPT data, but without the "ethics" filtering used for the original Vicuna.
A GPTQ quantised 4-bit version is available here.
Original Vicuna Model Card
Model details
Model type: Vicuna is an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. It is an auto-regressive language model, based on the transformer architecture.
Model date: Vicuna was trained between March 2023 and April 2023.
Organizations developing the model: The Vicuna team with members from UC Berkeley, CMU, Stanford, and UC San Diego.
Paper or resources for more information: https://vicuna.lmsys.org/
License: Apache License 2.0
Where to send questions or comments about the model: https://github.com/lm-sys/FastChat/issues
Intended use
Primary intended uses: The primary use of Vicuna is research on large language models and chatbots.
Primary intended users: The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
Training dataset
70K conversations collected from ShareGPT.com.
Evaluation dataset
A preliminary evaluation of the model quality is conducted by creating a set of 80 diverse questions and utilizing GPT-4 to judge the model outputs. See https://vicuna.lmsys.org/ for more details.
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