MiniCPM is a family of compact language models, including MiniCPM5-1B, designed to run locally on devices with limited resources. Developers use it for on-device assistants, reasoning, code, tool use, deployment, and fine-tuning, while the repository also includes a desktop-pet example. The catalogue entries support deployment and fine-tuning workflows for the models.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add OpenBMB/MiniCPM --skill minicpm5-deploy-mlxgit clone --depth 1 https://github.com/OpenBMB/MiniCPMWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/openbmb/minicpm/minicpm5-deploy-mlx)<a href="https://agentmods.dev/skills/openbmb/minicpm/minicpm5-deploy-mlx"><img src="https://agentmods.dev/badge/skills/openbmb/minicpm/minicpm5-deploy-mlx/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/openbmb/minicpm/minicpm5-deploy-mlx"><img src="https://agentmods.dev/badge/skills/openbmb/minicpm/minicpm5-deploy-mlx.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 62 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00078 | $0.00907 |
| Opus 5 | $0.00039 | $0.00453 |
| Sonnet 5 | $0.00016 | $0.00181 |
| Haiku 4.5 | $0.00008 | $0.00091 |
Grade A, and why
minicpm5-deploy-mlx scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl http://127.0.0.1:8000/v1/chat/completions \ How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deploy MiniCPM5-1B and MiniCPM5-2B with MLX (Apple Silicon)
Apple's on-device tensor framework. Highest throughput on M-series. Stays inside one Python process — no separate server, no llama.cpp build chain.
Required input
| Var | Example | Default |
|---|---|---|
MLX_REPO |
openbmb/MiniCPM5-2B-MLX (pre-converted 4-bit affine) |
required; openbmb/MiniCPM5-1B-MLX also works |
OR HF_REPO + QUANT |
openbmb/MiniCPM5-2B, 4bit or bf16 |
for local conversion |
MAX_TOKENS |
200 |
200 |
Steps
1. Install (once)
pip install "mlx-lm>=0.31" "gguf"
2A. Use a pre-converted MLX repo (recommended when available)
mlx_lm.generate --model "${MLX_REPO}" \
--prompt "<|im_start|>user
1+1=?<|im_end|>
<|im_start|>assistant
" \
--max-tokens ${MAX_TOKENS} --temp 1.0 --top-p 0.95
2B. Convert from a HF checkpoint locally (advanced)
Use mlx_lm.convert only if you have a self-trained HF fp16 checkpoint:
HF=/path/to/your-fp16-hf
# Convert: bf16
mlx_lm.convert --hf-path "$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": 1.0, "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 onmlx-lm < 0.31(older versions ignore multi-ideos_token_idlists). Upgrade, or pass--extra-eos-token "<|im_end|>"as a manual override —<|im_end|>is token id 130073 and is already listed ingeneration_config.jsonon 0.31+.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today Changed · +8 tokens per session b5b84a004698
- 9d ago First seen · 85 lines · 70 tokens per session scan A 95c1f0b05f6e
minicpm5-deploy-mlx is a skill published in the GitHub repository OpenBMB/MiniCPM (10,478 stars, last pushed today), licensed Apache-2.0. It adds 78 tokens to every session and 907 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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