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lmsys
/
vicuna-33b-v1.3

Text Generation
Transformers
PyTorch
llama
text-generation-inference
Model card Files Files and versions
xet
Community
13

Instructions to use lmsys/vicuna-33b-v1.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use lmsys/vicuna-33b-v1.3 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="lmsys/vicuna-33b-v1.3")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-33b-v1.3")
    model = AutoModelForCausalLM.from_pretrained("lmsys/vicuna-33b-v1.3")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use lmsys/vicuna-33b-v1.3 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "lmsys/vicuna-33b-v1.3"
    # 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-33b-v1.3",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/lmsys/vicuna-33b-v1.3
  • SGLang

    How to use lmsys/vicuna-33b-v1.3 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-33b-v1.3" \
        --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-33b-v1.3",
    		"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-33b-v1.3" \
            --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-33b-v1.3",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use lmsys/vicuna-33b-v1.3 with Docker Model Runner:

    docker model run hf.co/lmsys/vicuna-33b-v1.3
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Adding `safetensors` variant of this model

#13 opened about 2 years ago by
SFconvertbot

Adding Evaluation Results

#11 opened over 2 years ago by
leaderboard-pr-bot

When we can expect vicuna variant of CodeLlama-2 34b model?

๐Ÿ‘ 1
#10 opened over 2 years ago by
perelmanych

Failed. Reason: The primary container for production variant AllTraffic did not pass the ping health check

#9 opened over 2 years ago by
Shivam1410

Bigger is NOT always better...

๐Ÿ‘ 1
5
#8 opened almost 3 years ago by
MrDevolver

Adding `safetensors` variant of this model

#6 opened almost 3 years ago by
mmahlwy3

Adding `safetensors` variant of this model

#5 opened almost 3 years ago by
mmahlwy3

How much GPU graphics memory is required for deployment

2
#3 opened almost 3 years ago by
chenfeicqq

Is there a 4bit quantize version for the FastChat?

6
#2 opened almost 3 years ago by
ruradium

Prompt format?

10
#1 opened almost 3 years ago by
Thireus
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