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R136a1
/
Apalah

GGUF
imatrix
conversational
Model card Files Files and versions
xet
Community

Instructions to use R136a1/Apalah with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • llama-cpp-python

    How to use R136a1/Apalah with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="R136a1/Apalah",
    	filename="allhator.Q6_K-imat.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 R136a1/Apalah with llama.cpp:

    Install from brew
    brew install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf R136a1/Apalah:Q6_K
    # Run inference directly in the terminal:
    llama-cli -hf R136a1/Apalah:Q6_K
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf R136a1/Apalah:Q6_K
    # Run inference directly in the terminal:
    llama-cli -hf R136a1/Apalah:Q6_K
    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 R136a1/Apalah:Q6_K
    # Run inference directly in the terminal:
    ./llama-cli -hf R136a1/Apalah:Q6_K
    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 R136a1/Apalah:Q6_K
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf R136a1/Apalah:Q6_K
    Use Docker
    docker model run hf.co/R136a1/Apalah:Q6_K
  • LM Studio
  • Jan
  • Ollama

    How to use R136a1/Apalah with Ollama:

    ollama run hf.co/R136a1/Apalah:Q6_K
  • Unsloth Studio new

    How to use R136a1/Apalah 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 R136a1/Apalah 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 R136a1/Apalah to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for R136a1/Apalah to start chatting
  • Docker Model Runner

    How to use R136a1/Apalah with Docker Model Runner:

    docker model run hf.co/R136a1/Apalah:Q6_K
  • Lemonade

    How to use R136a1/Apalah with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull R136a1/Apalah:Q6_K
    Run and chat with the model
    lemonade run user.Apalah-Q6_K
    List all available models
    lemonade list
Apalah
118 GB
Ctrl+K
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  • 1 contributor
History: 10 commits
R136a1's picture
R136a1
Upload 4 files
d746196 verified almost 2 years ago
  • .gitattributes
    2.22 kB
    Upload 4 files almost 2 years ago
  • allhator.Q6_K-imat.gguf
    13.6 GB
    xet
    Upload folder using huggingface_hub almost 2 years ago
  • allhator.Q8_0-imat.gguf
    16 GB
    xet
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  • allstheno.Q6_K-imat.gguf
    13.6 GB
    xet
    Upload folder using huggingface_hub almost 2 years ago
  • allstheno.Q8_0-imat.gguf
    16 GB
    xet
    Upload folder using huggingface_hub almost 2 years ago
  • elgato.Q6_K-imat.gguf
    13.6 GB
    xet
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  • elgato.Q8_0-imat.gguf
    16 GB
    xet
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  • imatrix-gato32.dat
    9.98 MB
    xet
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  • imatrix-hator32.dat
    9.98 MB
    xet
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  • imatrix-pipti32.dat
    9.98 MB
    xet
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  • imatrix-stheno32.dat
    9.98 MB
    xet
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  • pipti.Q6_K-imat.gguf
    13.6 GB
    xet
    Upload folder using huggingface_hub almost 2 years ago
  • pipti.Q8_0-imat.gguf
    16 GB
    xet
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