How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf YellowLeaves/model:F16
# Run inference directly in the terminal:
llama-cli -hf YellowLeaves/model:F16
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf YellowLeaves/model:F16
# Run inference directly in the terminal:
llama-cli -hf YellowLeaves/model:F16
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 YellowLeaves/model:F16
# Run inference directly in the terminal:
./llama-cli -hf YellowLeaves/model:F16
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 YellowLeaves/model:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf YellowLeaves/model:F16
Use Docker
docker model run hf.co/YellowLeaves/model:F16
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Uploaded model

  • Developed by: wanyangYe
  • License: apache-2.0
  • Finetuned from model : unsloth/meta-llama-3.1-8b-bnb-4bit

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

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Model size
8B params
Architecture
llama
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