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legraphista
/
Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF

Text Generation
GGUF
quantized
GGUF
imatrix
quantization
imat
static
16bit
8bit
6bit
5bit
4bit
3bit
2bit
1bit
conversational
Model card Files Files and versions
xet
Community

Instructions to use legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • llama-cpp-python

    How to use legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF",
    	filename="Meta-Llama-3-8B-Instruct-abliterated-v3.BF16.gguf",
    )
    
    llm.create_chat_completion(
    	messages = [
    		{
    			"role": "user",
    			"content": "What is the capital of France?"
    		}
    	]
    )
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF with llama.cpp:

    Install from brew
    brew install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF:Q4_K_S
    # Run inference directly in the terminal:
    llama-cli -hf legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF:Q4_K_S
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF:Q4_K_S
    # Run inference directly in the terminal:
    llama-cli -hf legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF:Q4_K_S
    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 legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF:Q4_K_S
    # Run inference directly in the terminal:
    ./llama-cli -hf legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF:Q4_K_S
    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 legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF:Q4_K_S
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF:Q4_K_S
    Use Docker
    docker model run hf.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF:Q4_K_S
  • LM Studio
  • Jan
  • vLLM

    How to use legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF:Q4_K_S
  • Ollama

    How to use legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF with Ollama:

    ollama run hf.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF:Q4_K_S
  • Unsloth Studio

    How to use legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF 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 legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF 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 legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF to start chatting
  • Docker Model Runner

    How to use legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF with Docker Model Runner:

    docker model run hf.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF:Q4_K_S
  • Lemonade

    How to use legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF:Q4_K_S
    Run and chat with the model
    lemonade run user.Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF-Q4_K_S
    List all available models
    lemonade list
Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF
5.28 MB
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  • 1 contributor
History: 4 commits
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legraphista
Upload imatrix.dataset with huggingface_hub
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  • .gitattributes
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  • README.md
    7.3 kB
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  • imatrix.dat
    4.99 MB
    xet
    Upload imatrix.dat with huggingface_hub about 2 years ago
  • imatrix.dataset
    280 kB
    Upload imatrix.dataset with huggingface_hub about 2 years ago