Instructions to use imi2/TMAC-Llama-2-7b-EfficientQAT-w2g64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use imi2/TMAC-Llama-2-7b-EfficientQAT-w2g64 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 imi2/TMAC-Llama-2-7b-EfficientQAT-w2g64 # Run inference directly in the terminal: llama cli -hf imi2/TMAC-Llama-2-7b-EfficientQAT-w2g64
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf imi2/TMAC-Llama-2-7b-EfficientQAT-w2g64 # Run inference directly in the terminal: llama cli -hf imi2/TMAC-Llama-2-7b-EfficientQAT-w2g64
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 imi2/TMAC-Llama-2-7b-EfficientQAT-w2g64 # Run inference directly in the terminal: ./llama-cli -hf imi2/TMAC-Llama-2-7b-EfficientQAT-w2g64
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 imi2/TMAC-Llama-2-7b-EfficientQAT-w2g64 # Run inference directly in the terminal: ./build/bin/llama-cli -hf imi2/TMAC-Llama-2-7b-EfficientQAT-w2g64
Use Docker
docker model run hf.co/imi2/TMAC-Llama-2-7b-EfficientQAT-w2g64
- LM Studio
- Jan
- Ollama
How to use imi2/TMAC-Llama-2-7b-EfficientQAT-w2g64 with Ollama:
ollama run hf.co/imi2/TMAC-Llama-2-7b-EfficientQAT-w2g64
- Unsloth Desktop
- Docker Model Runner
How to use imi2/TMAC-Llama-2-7b-EfficientQAT-w2g64 with Docker Model Runner:
docker model run hf.co/imi2/TMAC-Llama-2-7b-EfficientQAT-w2g64
- Lemonade
How to use imi2/TMAC-Llama-2-7b-EfficientQAT-w2g64 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull imi2/TMAC-Llama-2-7b-EfficientQAT-w2g64
Run and chat with the model
lemonade run user.TMAC-Llama-2-7b-EfficientQAT-w2g64-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
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Check out the documentation for more information.
the speed is not identical to other quantizations due to the two large layers kept in f16.
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