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 mindchain/haddock_reward_mini
# Run inference directly in the terminal:
llama-cli -hf mindchain/haddock_reward_mini
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mindchain/haddock_reward_mini
# Run inference directly in the terminal:
llama-cli -hf mindchain/haddock_reward_mini
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 mindchain/haddock_reward_mini
# Run inference directly in the terminal:
./llama-cli -hf mindchain/haddock_reward_mini
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 mindchain/haddock_reward_mini
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mindchain/haddock_reward_mini
Use Docker
docker model run hf.co/mindchain/haddock_reward_mini
Quick Links

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Check out the documentation for more information.

Quantized Reward Model

  • Original model: Skywork/Skywork-Reward-Llama-3.1-8B-v0.2
  • Quantization: Q4_K_M
  • Format: GGUF
  • Size: ~4.6GB
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GGUF
Model size
8B params
Architecture
llama
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Collection including mindchain/haddock_reward_mini