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-deploygit 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)<a href="https://agentmods.dev/skills/openbmb/minicpm/minicpm5-deploy"><img src="https://agentmods.dev/badge/skills/openbmb/minicpm/minicpm5-deploy.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00101 | $0.01599 |
| Opus 5 | $0.00051 | $0.00800 |
| Sonnet 5 | $0.00020 | $0.00320 |
| Haiku 4.5 | $0.00010 | $0.00160 |
Grade A, and why
minicpm5-deploy 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 7d ago.
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://localhost:PORT/v1/chat/completions \ How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deploy MiniCPM5-1B — backend router
You're being asked to deploy / serve / chat-with a MiniCPM5-1B checkpoint. Your job is to pick exactly one backend skill below based on the user's hardware, format, and goal, then invoke that skill rather than improvising.
1. Required input from the user
Before picking a backend, you MUST know:
| Variable | Example | Where to ask |
|---|---|---|
MODEL_PATH |
HF id openbmb/MiniCPM5-1B (post-release) or a local path |
"Which checkpoint? HF id or local path?" |
| Hardware | NVIDIA GPU / Apple Silicon / CPU only | infer from context, otherwise ask |
| Goal | "interactive chat" / "OpenAI server" / "Python script" / "benchmark" | infer from context |
Available checkpoints on Hugging Face
| Variant | HF repo | Use with |
|---|---|---|
| HF fp16 (recommended) | openbmb/MiniCPM5-1B |
transformers / vllm (no --quantization) / sglang / any minicpm5-finetune-* |
| GGUF F16 / Q8_0 / Q4_K_M | openbmb/MiniCPM5-1B-GGUF |
minicpm5-deploy-llama-cpp / -ollama / -lmstudio |
| MLX (Apple Silicon) | openbmb/MiniCPM5-1B-MLX |
minicpm5-deploy-mlx |
If the user has a local copy, accept any directory path that contains config.json and model.safetensors (or the equivalent GGUF / MLX layout).
2. Decision matrix — pick exactly one
| User says / wants | Hardware | Format | → Skill to invoke |
|---|---|---|---|
| "Quick Python script" / "one-shot generation" / "no server" | any GPU or CPU | HF safetensors | minicpm5-deploy-transformers |
| "OpenAI server" / "production serving" / "high QPS" | NVIDIA GPU | HF safetensors | minicpm5-deploy-vllm |
| "RadixAttention" / "prefix cache" / "batched eval" | NVIDIA GPU | HF safetensors | minicpm5-deploy-sglang |
| "GGUF" / "llama.cpp" / "llama-cli" / "CPU only" | any CPU + optional GPU | GGUF | minicpm5-deploy-llama-cpp |
| "Ollama" / "ollama run" / "Modelfile" | macOS / Linux laptop | GGUF | minicpm5-deploy-ollama |
| "LM Studio" / "desktop GUI" | macOS / Windows / Linux | GGUF or MLX | minicpm5-deploy-lmstudio |
| "MLX" / "Apple Silicon native" / "fastest on Mac" | Apple Silicon | MLX | minicpm5-deploy-mlx |
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.
- 7d ago First seen · 84 lines · 101 tokens per session scan A 111532fae204
minicpm5-deploy is a skill published in the GitHub repository OpenBMB/MiniCPM (10,309 stars, last pushed yesterday), licensed Apache-2.0. It adds 101 tokens to every session and 1,599 once invoked, about $0.0005 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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