Instructions to use lmsys/vicuna-7b-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lmsys/vicuna-7b-v1.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lmsys/vicuna-7b-v1.5")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-7b-v1.5") model = AutoModelForCausalLM.from_pretrained("lmsys/vicuna-7b-v1.5", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lmsys/vicuna-7b-v1.5 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lmsys/vicuna-7b-v1.5" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lmsys/vicuna-7b-v1.5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lmsys/vicuna-7b-v1.5
- SGLang
How to use lmsys/vicuna-7b-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 "lmsys/vicuna-7b-v1.5" \ --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": "lmsys/vicuna-7b-v1.5", "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 "lmsys/vicuna-7b-v1.5" \ --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": "lmsys/vicuna-7b-v1.5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lmsys/vicuna-7b-v1.5 with Docker Model Runner:
docker model run hf.co/lmsys/vicuna-7b-v1.5
Adding Evaluation Results
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by leaderboard-pr-bot - opened
README.md
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## Difference between different versions of Vicuna
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See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)
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## Difference between different versions of Vicuna
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See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__vicuna-7b-v1.5)
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| Metric | Value |
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| Avg. | 45.9 |
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| ARC (25-shot) | 53.24 |
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| HellaSwag (10-shot) | 77.39 |
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| MMLU (5-shot) | 51.04 |
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| TruthfulQA (0-shot) | 50.34 |
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| Winogrande (5-shot) | 72.14 |
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| GSM8K (5-shot) | 8.19 |
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| DROP (3-shot) | 8.96 |
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