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name minicpm5-deploy-llama-cpp
description Run MiniCPM5-1B or MiniCPM5-2B with llama.cpp using the released GGUF artifacts (F16 / Q8_0 / Q4_K_M). Use when the user wants CPU-only / consumer-GPU / cross-platform native deployment, asks for "llama.cpp", "llama-cli", "llama-server", "GGUF", or has no Python available.

Deploy MiniCPM5-1B and MiniCPM5-2B with llama.cpp

CPU / edge / consumer-GPU deployment via the released GGUF artifacts. The artifacts work directly with vanilla llama.cpp and every downstream runtime (Ollama / LM Studio / llama-cpp-python).

Required input

Var Example Default
GGUF_REPO openbmb/MiniCPM5-2B-GGUF required; openbmb/MiniCPM5-1B-GGUF also works
QUANT Q4_K_M (1.56 GB, recommended) / Q8_0 (2.68 GB) / F16 (5.04 GB) Q4_K_M
NGL 99 (all layers on GPU) / 0 (CPU only) 99 if NVIDIA GPU, else 0
CTX 8192 (default) up to 131072 (128 K) 8192

Steps

1. Install llama.cpp

# macOS
brew install llama.cpp

# Linux / cross-platform: pre-built binary
curl -fsSL https://github.com/ggerganov/llama.cpp/releases/latest/download/llama-cli-linux.tar.gz | tar -xz
# OR build from source:
git clone --depth=1 https://github.com/ggerganov/llama.cpp.git && cd llama.cpp
mkdir build && cd build
cmake .. -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release    # CPU-only: omit GGML_CUDA=ON
cmake --build . --config Release -j $(nproc) --target llama-cli llama-server

2. Download the GGUF

mkdir -p ~/minicpm5 && cd ~/minicpm5
huggingface-cli download ${GGUF_REPO} MiniCPM5-2B-${QUANT}.gguf --local-dir .

3a. Interactive chat (CLI)

llama-cli -m MiniCPM5-2B-${QUANT}.gguf \
    -n 2048 --temp 1.0 --top-p 0.95 --min-p 0.0 -ngl ${NGL} -c ${CTX}

3b. OpenAI-compatible HTTP server

llama-server -m MiniCPM5-2B-${QUANT}.gguf \
    --port 8080 -ngl ${NGL} -c ${CTX} --jinja

4. Validate

curl http://localhost:8080/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "MiniCPM5-2B",
        "messages": [{"role":"user","content":"1+1=?"}],
        "temperature": 1.0, "top_p": 0.95, "min_p": 0.0, "max_tokens": 64
    }'

Expected: "2" in the reply.

In llama.cpp, the default min_p=0.05 can lead to repetitive output: it filters out tokens whose probability is below 5% of the highest-probability token, potentially discarding the exact tokens needed to break out of a repetition loop. To prevent this, we set min_p=0.0.

Sampling defaults

Mode --temp --top-p --min-p
MiniCPM5-2B Think 1.0 0.95 0.0
MiniCPM5-1B Think 0.9 0.95 0.0
MiniCPM5-1B No-think 0.7 0.95 0.0

Choosing a quant

Quant Disk Quality
F16 5.04 GB reference
Q8_0 2.68 GB ~indistinguishable from F16
Q4_K_M 1.56 GB small drop, ideal for laptops

Common pitfalls

  • Slow on CPU + large context: drop -c 131072 to -c 8192 if you don't need 128 K.

Building your own GGUF (advanced)

If you've trained your own MiniCPM5-1B or MiniCPM5-2B variant, build a GGUF with:

python convert_hf_to_gguf.py /path/to/your-fp16-hf --outfile out/F16.gguf --outtype f16
llama-quantize out/F16.gguf out/Q4_K_M.gguf Q4_K_M

Trained a LoRA adapter (not a full model) and want to apply it at runtime with --lora instead of baking it in? Convert it to a GGUF adapter — see minicpm5-finetune-gguf-lora.

When NOT to use

  • NVIDIA GPU + want OpenAI-compatible serving → minicpm5-deploy-vllm
  • Apple Silicon native → minicpm5-deploy-mlx is faster
  • Just want one-line desktop run → minicpm5-deploy-ollama
  • Want a desktop GUI → minicpm5-deploy-lmstudio

Reference

docs/deployment/llama_cpp.md