version: "1.0.1" name: bailian-finetune metadata: version: "1.17.1" requires: bins: ["bl"] description: >- 阿里云百炼模型精调训练入口：用户要精调、微调、训练自己的模型（fine-tune，支持 SFT / SFT-LoRA / DPO / DPO-LoRA / CPT， 覆盖文本、语音、图像）、校验或上传训练数据集、看训练进度和日志、挑 checkpoint、导出精调产物、 把专属模型部署成服务时使用 bl dataset / bl finetune / bl deploy。链路是 validate 校验数据 → upload 拿 file-id → finetune create 建任务 → watch 看进度 → export 导出 → deploy 上线，需要 API key； 写操作先用 --dry-run 预览。反触发：用户点名火山方舟/ark 的精调不走本 skill；只是要选哪个模型走 bailian-model-recommend；用现成模型生图生视频走 bailian-gen；百炼其他资源管理走 bailian-cli。 官方安装：bl skill init（与共享协议 bailian-protocol 同装）。

Bailian fine-tuning pipeline (bl dataset / bl finetune / bl deploy)

CRITICAL — Before executing, MUST read the shared protocol in [`../bailian-protocol/SKILL.md`](../bailian-protocol/SKILL.md): Version & updates (pre-flight checklist), Setup & auth, and CLI errors: report an issue. Command details are authoritative in [`reference/`](reference/index.md) (dataset / finetune / deploy) and `bl <command> --help` — do not guess flags. The whole pipeline requires an API key. If that protocol file is missing, stop and run `bl skill init`; do not guess auth/consent.

End-to-end workflow (follow in order)

1. Validate data   bl dataset validate --file train.jsonl [--schema chatml|dpo|cpt|tts|image]
2. Upload data     bl dataset upload --file train.jsonl          # returns a file-id
3. Create job      bl finetune text|audio|image create --base-model <base> --datasets <file-id|path>
4. Watch progress  bl finetune watch --job-id ft-xxx             # or get / logs
5. Pick artifact   bl finetune checkpoints --job-id ft-xxx
6. Export model    bl finetune export --job-id ft-xxx --checkpoint ckpt-N --model-name my-model
7. Deploy service  bl deploy text|audio|image create --model-name my-model --display-name my-svc

• Unsure which training methods a base model supports → bl finetune capability --base-model <base> or --training-type sft|sft-lora|dpo|cpt.
• Text --training-type values: sft / sft-lora / dpo / dpo-lora / cpt. Audio bases include cosyvoice-v3-flash; image bases include wan2.7-image-pro.
• Deployment plans: audio defaults to --plan mu; text/image default to lora.
• Preview write operations (create / delete / cancel / scale) with --dry-run first, and confirm with the user before deleting a job or dataset.

When to use which command

Flags, usage, and examples: see `reference/` or bl <command> --help — do not guess flags.

Quick examples

bl dataset validate --file train.jsonl
bl dataset upload --file train.jsonl
bl finetune text create --base-model qwen3-8b --training-type sft-lora --datasets file-xxx
bl finetune watch --job-id ft-xxx
bl finetune export --job-id ft-xxx --checkpoint ckpt-3 --model-name my-qwen-sft
bl deploy text create --model-name my-qwen-sft --display-name my-svc

Common hand-offs

软 hand-off（按 skill 名；已安装则 Read，否则 --help / 提示 bl skill init）：

• After deployment, try the model or generate content → skill bailian-gen (media) or bl text chat (fallback: bl image\|video\|text --help).
• Unsure which base model to pick → bailian-model-recommend / bl advisor recommend.
• Training quota / usage questions → skill bailian-cli (fallback: bl quota / bl usage --help).

references

• bailian-protocol — shared protocol (install via bl skill init)
• reference/ — command details