GGUF
conversational
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 1bit-MONSTER/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 1bit-MONSTER/MiniCPM5-2B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf 1bit-MONSTER/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 1bit-MONSTER/MiniCPM5-2B-GGUF: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 1bit-MONSTER/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf 1bit-MONSTER/MiniCPM5-2B-GGUF: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 1bit-MONSTER/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 1bit-MONSTER/MiniCPM5-2B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/1bit-MONSTER/MiniCPM5-2B-GGUF:Q4_K_M
Quick Links

MiniCPM5-2B โ€” GGUF (Q4_K_M)

OpenBMB's own Q4_K_M GGUF, re-hosted with measured performance for the 1bit engine on Strix Halo (Vulkan backend).

Contents

  • MiniCPM5-2B-Q4_K_M.gguf

Measured performance (Strix Halo, Vulkan)

pp512: 4179 tok/s ยท tg128: 106 tok/s

Running it

1bit serve -m MiniCPM5-2B-Q4_K_M.gguf --device vulkan

Attribution

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GGUF
Model size
3B params
Architecture
llama
Hardware compatibility
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4-bit

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