Minicpm5 Finetune
Pick the right fine-tuning framework for a MiniCPM5-1B base checkpoint and route to a...
Install
npx skills add OpenBMB/MiniCPM --skill minicpm5-finetune -g -a claude-code -y- Repository
- OpenBMB/MiniCPM11.8k
- Category
- Engineering
- Updated
- 1mo ago
- License
- Apache-2.0
- Context cost
- Lightabout 1,224 tokens
- Runs scripts
- No, instructions only
What it does
Pick the right fine-tuning framework for a MiniCPM5-1B base checkpoint and route to a framework-specific cookbook skill. Use when the user wants to SFT / LoRA / DPO / continue-pretrain MiniCPM5 and has not yet committed to a specific framework, or when they say "fine-tune MiniCPM5", "train MiniCPM5", "MiniCPM5 微调", "LoRA MiniCPM5", "继续训练 MiniCPM5".
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 |
