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

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Check out the documentation for more information.

FM nearswap family

  • GhostShip-31b-Q4_K_M.gguf - FM nearswap merge: Gemma4-Gutenberg-31B-Heretic <- gemma-4-31B-it-scotoma-2, nearswap strength t 0.0001 -> 0.0007 linear across layers (avg 4e-4). Same family as Robinson/BenGunn, ~4x peak strength.
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GGUF
Model size
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Architecture
gemma4
Hardware compatibility
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