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4mrii
/
LLMV5

Transformers
GGUF
llama
conversational
Model card Files Files and versions
xet
Community

Instructions to use 4mrii/LLMV5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use 4mrii/LLMV5 with Transformers:

    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("4mrii/LLMV5", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use 4mrii/LLMV5 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 4mrii/LLMV5:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf 4mrii/LLMV5:Q4_K_M
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf 4mrii/LLMV5:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf 4mrii/LLMV5: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 4mrii/LLMV5:Q4_K_M
    # Run inference directly in the terminal:
    ./llama-cli -hf 4mrii/LLMV5: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 4mrii/LLMV5:Q4_K_M
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf 4mrii/LLMV5:Q4_K_M
    Use Docker
    docker model run hf.co/4mrii/LLMV5:Q4_K_M
  • LM Studio
  • Jan
  • Ollama

    How to use 4mrii/LLMV5 with Ollama:

    ollama run hf.co/4mrii/LLMV5:Q4_K_M
  • Unsloth Studio

    How to use 4mrii/LLMV5 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 4mrii/LLMV5 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 4mrii/LLMV5 to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for 4mrii/LLMV5 to start chatting
  • Docker Model Runner

    How to use 4mrii/LLMV5 with Docker Model Runner:

    docker model run hf.co/4mrii/LLMV5:Q4_K_M
  • Lemonade

    How to use 4mrii/LLMV5 with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull 4mrii/LLMV5:Q4_K_M
    Run and chat with the model
    lemonade run user.LLMV5-Q4_K_M
    List all available models
    lemonade list
  • Atomic Chat
LLMV5
5.25 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 5 commits
4mrii's picture
4mrii
Upload alpaca_dataV5_V2_randomize.json
36f9af3 verified about 2 years ago
  • .gitattributes
    1.65 kB
    Upload alpaca_dataV5_V2_randomize.json about 2 years ago
  • LLMV5-unsloth.Q4_K_M.gguf
    4.92 GB
    xet
    (Trained with Unsloth) about 2 years ago
  • README.md
    58 Bytes
    Update README.md about 2 years ago
  • alpaca_dataV5_V2_randomize.json
    328 MB
    xet
    Upload alpaca_dataV5_V2_randomize.json about 2 years ago
  • config.json
    29 Bytes
    (Trained with Unsloth) about 2 years ago