Minicpm5 Deploy Mlx

DevOps & Security

minicpm5-deploy-mlx

Minicpm5 Deploy Mlx is an Agent Skill for devops & security published in the OpenBMB/MiniCPM repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Run MiniCPM5-1B natively on Apple Silicon with Apple's MLX framework. Use when the user has an Apple Silicon Mac and asks for "MLX", "mlx_lm", "mlx_lm.convert", "mlx_lm.generate", or wants the fastest path on Apple Silicon.

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QUICK START

How to use this skill

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Copy and paste the prompt below.
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Prompt to paste

Inspect the Agent Skill at https://github.com/OpenBMB/MiniCPM/blob/HEAD/skills/minicpm5-deploy-mlx/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/minicpm5-deploy-mlx, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

Deploy MiniCPM5-1B with MLX (Apple Silicon)

Apple's on-device tensor framework. Highest throughput on M-series. Stays inside one Python process — no separate server, no llama.cpp build chain.

Required input

Var	Example	Default
`MLX_REPO`	`openbmb/MiniCPM5-1B-MLX` (pre-converted 4-bit affine)	required
OR `HF_REPO` + `QUANT`	`openbmb/MiniCPM5-1B`, `4bit` or `bf16`	for local conversion
`MAX_TOKENS`	`200`	`200`

Steps

1. Install (once)

pip install "mlx-lm>=0.31" "gguf"

2A. Use a pre-converted MLX repo (recommended when available)

mlx_lm.generate --model "${MLX_REPO}" \
    --prompt "<|im_start|>user
1+1=?<|im_end|>
<|im_start|>assistant
" \
    --max-tokens ${MAX_TOKENS} --temp 0.7 --top-p 0.95

2B. Convert from a HF checkpoint locally (advanced)

Use mlx_lm.convert only if you have a self-trained HF fp16 checkpoint:

HF=/path/to/your-fp16-hf

# Convert: bf16
mlx_lm.convert --hf-path "$HF" --mlx-path ./minicpm5-mlx-bf16

# Convert: 4-bit (smaller / faster)
mlx_lm.convert --hf-path "$HF" --mlx-path ./minicpm5-mlx-q4 -q --q-bits 4

Then run as in 2A.

3. Validate

The reply should contain "2" for 1+1=?.

OpenAI-compatible server (mlx-lm)

mlx_lm.server --model "${MLX_REPO}" --host 127.0.0.1 --port 8000

curl http://127.0.0.1:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "default",
        "messages": [{"role":"user","content":"1+1=?"}],
        "temperature": 0.7, "top_p": 0.95, "max_tokens": 64
    }'

Common pitfalls

Slow first generate: MLX JIT-compiles kernels on first call (~5-10 s); subsequent calls hit the warm cache.
Model runs past <|im_end|>: only happens on mlx-lm < 0.31 (older versions ignore multi-id eos_token_id lists). Upgrade, or pass --extra-eos-token "<|im_end|>" as a manual override — <|im_end|> is token id 130073 and is already listed in generation_config.json on 0.31+.

When NOT to use

Not on Apple Silicon → minicpm5-deploy-llama-cpp (CPU/CUDA) or minicpm5-deploy-vllm (CUDA)
Want a desktop GUI → minicpm5-deploy-lmstudio (LM Studio bundles an MLX runtime)
Want one-line CLI run → minicpm5-deploy-ollama

Reference

docs/deployment/mlx.md