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C10X
/
r1-mini

PyTorch
GGUF
qwen2
conversational
Model card Files Files and versions
xet
Community

Instructions to use C10X/r1-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • llama-cpp-python

    How to use C10X/r1-mini with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="C10X/r1-mini",
    	filename="unsloth.F16.gguf",
    )
    
    llm.create_chat_completion(
    	messages = "No input example has been defined for this model task."
    )
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • llama.cpp

    How to use C10X/r1-mini with llama.cpp:

    Install from brew
    brew install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf C10X/r1-mini:F16
    # Run inference directly in the terminal:
    llama-cli -hf C10X/r1-mini:F16
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf C10X/r1-mini:F16
    # Run inference directly in the terminal:
    llama-cli -hf C10X/r1-mini: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 C10X/r1-mini:F16
    # Run inference directly in the terminal:
    ./llama-cli -hf C10X/r1-mini: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 C10X/r1-mini:F16
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf C10X/r1-mini:F16
    Use Docker
    docker model run hf.co/C10X/r1-mini:F16
  • LM Studio
  • Jan
  • Ollama

    How to use C10X/r1-mini with Ollama:

    ollama run hf.co/C10X/r1-mini:F16
  • Unsloth Studio new

    How to use C10X/r1-mini 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 C10X/r1-mini 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 C10X/r1-mini to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for C10X/r1-mini to start chatting
  • Docker Model Runner

    How to use C10X/r1-mini with Docker Model Runner:

    docker model run hf.co/C10X/r1-mini:F16
  • Lemonade

    How to use C10X/r1-mini with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull C10X/r1-mini:F16
    Run and chat with the model
    lemonade run user.r1-mini-F16
    List all available models
    lemonade list
r1-mini
8.24 GB
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  • 1 contributor
History: 2 commits
C10X's picture
C10X
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  • .gitattributes
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  • config.json
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  • generation_config.json
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  • pytorch_model.bin

    Detected Pickle imports (3)

    • "collections.OrderedDict",
    • "torch._utils._rebuild_tensor_v2",
    • "torch.HalfStorage"

    What is a pickle import?

    3.55 GB
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  • special_tokens_map.json
    472 Bytes
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  • tokenizer.json
    11.4 MB
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  • tokenizer_config.json
    6.76 kB
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  • unsloth.F16.gguf
    3.56 GB
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  • unsloth.Q4_K_M.gguf
    1.12 GB
    xet
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