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compilade
/
quant-tests

GGUF
Model card Files Files and versions
xet
Community

Instructions to use compilade/quant-tests with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • llama-cpp-python

    How to use compilade/quant-tests with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="compilade/quant-tests",
    	filename="TriLM_1.5B_Unpacked-TQ1_0-F16.gguf",
    )
    
    output = llm(
    	"Once upon a time,",
    	max_tokens=512,
    	echo=True
    )
    print(output)
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • llama.cpp

    How to use compilade/quant-tests with llama.cpp:

    Install from brew
    brew install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf compilade/quant-tests:F16
    # Run inference directly in the terminal:
    llama-cli -hf compilade/quant-tests:F16
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf compilade/quant-tests:F16
    # Run inference directly in the terminal:
    llama-cli -hf compilade/quant-tests:F16
    Use pre-built binary
    # Download pre-built binary from:
    # https://github.com/ggerganov/llama.cpp/releases
    # Start a local OpenAI-compatible server with a web UI:
    ./llama-server -hf compilade/quant-tests:F16
    # Run inference directly in the terminal:
    ./llama-cli -hf compilade/quant-tests:F16
    Build from source code
    git clone https://github.com/ggerganov/llama.cpp.git
    cd llama.cpp
    cmake -B build
    cmake --build build -j --target llama-server llama-cli
    # Start a local OpenAI-compatible server with a web UI:
    ./build/bin/llama-server -hf compilade/quant-tests:F16
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf compilade/quant-tests:F16
    Use Docker
    docker model run hf.co/compilade/quant-tests:F16
  • LM Studio
  • Jan
  • Ollama

    How to use compilade/quant-tests with Ollama:

    ollama run hf.co/compilade/quant-tests:F16
  • Unsloth Studio new

    How to use compilade/quant-tests with Unsloth Studio:

    Install Unsloth Studio (macOS, Linux, WSL)
    curl -fsSL https://unsloth.ai/install.sh | sh
    # Run unsloth studio
    unsloth studio -H 0.0.0.0 -p 8888
    # Then open http://localhost:8888 in your browser
    # Search for compilade/quant-tests to start chatting
    Install Unsloth Studio (Windows)
    irm https://unsloth.ai/install.ps1 | iex
    # Run unsloth studio
    unsloth studio -H 0.0.0.0 -p 8888
    # Then open http://localhost:8888 in your browser
    # Search for compilade/quant-tests to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for compilade/quant-tests to start chatting
  • Docker Model Runner

    How to use compilade/quant-tests with Docker Model Runner:

    docker model run hf.co/compilade/quant-tests:F16
  • Lemonade

    How to use compilade/quant-tests with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull compilade/quant-tests:F16
    Run and chat with the model
    lemonade run user.quant-tests-F16
    List all available models
    lemonade list
quant-tests
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  • 1 contributor
History: 15 commits
compilade's picture
compilade
Format bench-TriLMs.py with black
879e55c over 1 year ago
  • .gitattributes
    1.56 kB
    Add some TriLM test models over 1 year ago
  • BENCHMARKING.md
    872 Bytes
    Add benchmarking script over 1 year ago
  • TriLM_1.5B_Unpacked-TQ1_0-F16.gguf
    691 MB
    xet
    Add some TriLM test models over 1 year ago
  • TriLM_2.4B_Unpacked-TQ1_0-F16.gguf
    936 MB
    xet
    Add some TriLM test models over 1 year ago
  • TriLM_3.9B_Unpacked-TQ1_0-F16.gguf
    1.4 GB
    xet
    Add some TriLM test models over 1 year ago
  • bench-TriLMs.py
    7.78 kB
    Format bench-TriLMs.py with black over 1 year ago
  • bench-TriLMs.sh
    2.22 kB
    Add a Python-based benchmarking script over 1 year ago