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twpc
/
fortunetelling

GGUF
llama
conversational
Model card Files Files and versions
xet
Community

Instructions to use twpc/fortunetelling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • llama-cpp-python

    How to use twpc/fortunetelling with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="twpc/fortunetelling",
    	filename="fortunetelling.Q8_0.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 twpc/fortunetelling with llama.cpp:

    Install from brew
    brew install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf twpc/fortunetelling:Q8_0
    # Run inference directly in the terminal:
    llama-cli -hf twpc/fortunetelling:Q8_0
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf twpc/fortunetelling:Q8_0
    # Run inference directly in the terminal:
    llama-cli -hf twpc/fortunetelling:Q8_0
    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 twpc/fortunetelling:Q8_0
    # Run inference directly in the terminal:
    ./llama-cli -hf twpc/fortunetelling:Q8_0
    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 twpc/fortunetelling:Q8_0
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf twpc/fortunetelling:Q8_0
    Use Docker
    docker model run hf.co/twpc/fortunetelling:Q8_0
  • LM Studio
  • Jan
  • Ollama

    How to use twpc/fortunetelling with Ollama:

    ollama run hf.co/twpc/fortunetelling:Q8_0
  • Unsloth Studio new

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

    How to use twpc/fortunetelling with Docker Model Runner:

    docker model run hf.co/twpc/fortunetelling:Q8_0
  • Lemonade

    How to use twpc/fortunetelling with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull twpc/fortunetelling:Q8_0
    Run and chat with the model
    lemonade run user.fortunetelling-Q8_0
    List all available models
    lemonade list
fortunetelling
8.54 GB
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  • 1 contributor
History: 5 commits
twpc's picture
twpc
Create readme.md
2f9977f verified about 1 year ago
  • .gitattributes
    1.63 kB
    Rename unsloth.Q8_0.gguf to fortunetelling.Q8_0.gguf about 1 year ago
  • config.json
    29 Bytes
    (Trained with Unsloth) about 1 year ago
  • fortunetelling.Q8_0.gguf
    8.54 GB
    xet
    Rename unsloth.Q8_0.gguf to fortunetelling.Q8_0.gguf about 1 year ago
  • readme.md
    41 Bytes
    Create readme.md about 1 year ago