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wirthual
/
Meta-Llama-3.2-3B-Instruct-llamafile

Text Generation
Transformers
llamafile
PyTorch
facebook
meta
llama
llama-3
Model card Files Files and versions
xet
Community

Instructions to use wirthual/Meta-Llama-3.2-3B-Instruct-llamafile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use wirthual/Meta-Llama-3.2-3B-Instruct-llamafile with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="wirthual/Meta-Llama-3.2-3B-Instruct-llamafile")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("wirthual/Meta-Llama-3.2-3B-Instruct-llamafile", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use wirthual/Meta-Llama-3.2-3B-Instruct-llamafile with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "wirthual/Meta-Llama-3.2-3B-Instruct-llamafile"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "wirthual/Meta-Llama-3.2-3B-Instruct-llamafile",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/wirthual/Meta-Llama-3.2-3B-Instruct-llamafile
  • SGLang

    How to use wirthual/Meta-Llama-3.2-3B-Instruct-llamafile 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 "wirthual/Meta-Llama-3.2-3B-Instruct-llamafile" \
        --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": "wirthual/Meta-Llama-3.2-3B-Instruct-llamafile",
    		"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 "wirthual/Meta-Llama-3.2-3B-Instruct-llamafile" \
            --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": "wirthual/Meta-Llama-3.2-3B-Instruct-llamafile",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use wirthual/Meta-Llama-3.2-3B-Instruct-llamafile with Docker Model Runner:

    docker model run hf.co/wirthual/Meta-Llama-3.2-3B-Instruct-llamafile
Meta-Llama-3.2-3B-Instruct-llamafile
10.9 GB
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  • 1 contributor
History: 4 commits
wirthual's picture
wirthual
Upload Llama-3.2-3B-Instruct-Q8_0.llamafile
3cf94d9 verified over 1 year ago
  • .gitattributes
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    Upload Llama-3.2-3B-Instruct-Q8_0.llamafile over 1 year ago
  • Llama-3.2-3B-Instruct-Q3_K_L.llamafile
    2.06 GB
    xet
    Upload 3 files over 1 year ago
  • Llama-3.2-3B-Instruct-Q4_K_M.llamafile
    2.26 GB
    xet
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  • Llama-3.2-3B-Instruct-Q6_K.llamafile
    2.89 GB
    xet
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  • Llama-3.2-3B-Instruct-Q8_0.llamafile
    3.66 GB
    xet
    Upload Llama-3.2-3B-Instruct-Q8_0.llamafile over 1 year ago
  • README.md
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    Create README.md over 1 year ago