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mixbits
/
Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF

Text Generation
GGUF
nvidia
nemotron
nemotron-3
nemotron-3-super
q4_k_m
yarn
long-context
llama.cpp
ollama
Model card Files Files and versions
xet
Community

Instructions to use mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF with llama.cpp:

    Install (macOS, Linux)
    curl -LsSf https://llama.app/install.sh | sh
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF:Q4_K_M
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF:Q4_K_M
    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 mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    ./llama-cli -hf mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF:Q4_K_M
    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 mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF:Q4_K_M
    Use Docker
    docker model run hf.co/mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF:Q4_K_M
  • LM Studio
  • Jan
  • vLLM

    How to use mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF:Q4_K_M
  • Ollama

    How to use mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF with Ollama:

    ollama run hf.co/mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF:Q4_K_M
  • Unsloth Desktop
  • Docker Model Runner

    How to use mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF with Docker Model Runner:

    docker model run hf.co/mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF:Q4_K_M
  • Lemonade

    How to use mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull mixbits/Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF:Q4_K_M
    Run and chat with the model
    lemonade run user.Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF-Q4_K_M
    List all available models
    lemonade list
  • Atomic Chat
Nemotron-3-Super-Q4_K_M-YaRN-1M-GGUF
86.8 GB
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  • 1 contributor
History: 2 commits
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mixbits
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  • .gitattributes
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  • LICENSE
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  • Modelfile
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  • Nemotron-3-Super-Q4_K_M-yarn1m-00001-of-00003.gguf
    40 GB
    xet
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  • Nemotron-3-Super-Q4_K_M-yarn1m-00002-of-00003.gguf
    39.5 GB
    xet
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  • Nemotron-3-Super-Q4_K_M-yarn1m-00003-of-00003.gguf
    7.32 GB
    xet
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  • README.md
    6.26 kB
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