How to use from
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 Snapkitty/snapkitty-harness:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Snapkitty/snapkitty-harness:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Snapkitty/snapkitty-harness:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Snapkitty/snapkitty-harness:Q4_K_M
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 Snapkitty/snapkitty-harness:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Snapkitty/snapkitty-harness:Q4_K_M
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 Snapkitty/snapkitty-harness:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Snapkitty/snapkitty-harness:Q4_K_M
Use Docker
docker model run hf.co/Snapkitty/snapkitty-harness:Q4_K_M
Quick Links

snapkitty-harness

SnapKitty 4B harness โ€” adversarial evaluation framework, 353 training pairs, 146 red-team pairs.

Part of the SNAPKITTYWEST Sovereign Compute constellation.

Unified theory: 10.5281/zenodo.21816366

Research Papers โ†’ GitHub โ†’

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Model size
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Architecture
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