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-finetunegit 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-finetune)<a href="https://agentmods.dev/skills/openbmb/minicpm/minicpm5-finetune"><img src="https://agentmods.dev/badge/skills/openbmb/minicpm/minicpm5-finetune/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-finetune"><img src="https://agentmods.dev/badge/skills/openbmb/minicpm/minicpm5-finetune.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00111 | $0.01526 |
| Opus 5 | $0.00056 | $0.00763 |
| Sonnet 5 | $0.00022 | $0.00305 |
| Haiku 4.5 | $0.00011 | $0.00153 |
Grade A, and why
minicpm5-finetune scanned grade A with 0 findings 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 6d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fine-tune MiniCPM5-1B — framework router
You're being asked to fine-tune MiniCPM5-1B. Pick exactly one framework skill below and invoke that skill rather than improvising — every framework has at least one MiniCPM5-specific gotcha that the dedicated skill knows about.
1. Required input from the user
| Variable | Example | Notes |
|---|---|---|
BASE_MODEL |
HF id openbmb/MiniCPM5-1B (post-release) or a local path |
see cluster paths below |
DATA |
path to JSONL in messages format | [{"messages": [{"role":"user","content":"..."}, {"role":"assistant","content":"..."}]}] |
OUTPUT_DIR |
where to write checkpoints | mkdir if missing |
| Goal | "LoRA SFT" / "full SFT" / "DPO" / "QLoRA on consumer GPU" / "continue-pretrain at scale" | drives skill choice |
| Hardware | 1× GPU / multi-node | drives skill choice |
Default base model
If BASE_MODEL is not pinned, default to the Hugging Face fp16 release:
openbmb/MiniCPM5-1B
Any local directory containing config.json + model.safetensors + tokenizer.json also works.
2. Decision matrix — pick exactly one
| User says / wants | Best fit | → Skill to invoke |
|---|---|---|
| YAML / WebUI driven SFT, broad community support | LLaMA-Factory | minicpm5-finetune-llamafactory |
| ChatML template + ModelScope-native SFT/DPO/KTO/ORPO | ms-swift | minicpm5-finetune-ms-swift |
| Bare-metal Python, assistant-only loss, minimal abstractions | TRL + PEFT | minicpm5-finetune-trl |
| Single-GPU LoRA / QLoRA, tight VRAM (24 GB or less) | unsloth | minicpm5-finetune-unsloth |
| mmengine config-driven SFT, OpenMMLab stack | xtuner | minicpm5-finetune-xtuner |
Decision shortcuts
- First time fine-tuning MiniCPM5: pick
minicpm5-finetune-llamafactory— most documented, fewest surprises. - Need DPO / KTO / ORPO: pick
minicpm5-finetune-ms-swift(best out-of-the-box) orminicpm5-finetune-trl(most control). - Single 24 GB consumer GPU: pick
minicpm5-finetune-unslothwithload_in_4bit=True. - Target is llama.cpp / Ollama / LM Studio / MiniCPM Desk Pet (GGUF): train with any skill above, then convert the adapter with
minicpm5-finetune-gguf-lora(PEFT adapter → GGUF LoRA).
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.
- 6d ago Changed 74cec58ff6b4
- 11d ago First seen · 93 lines · 111 tokens per session scan A 6f8ed555f06a
minicpm5-finetune is a skill published in the GitHub repository OpenBMB/MiniCPM (10,780 stars, last pushed today), licensed Apache-2.0. It adds 111 tokens to every session and 1,526 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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