| name | bailian-finetune |
| metadata | {"version":"1.17.0","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: Version & updates (pre-flight checklist), Setup & auth, and CLI errors: report an issue. Command details are authoritative in reference/ (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
| Intent | Command |
|---|
| Validate / upload training data | bl dataset validate / upload (.jsonl or .zip) |
| Dataset list / detail / delete | bl dataset list / get / delete |
| Create a fine-tuning job | bl finetune text|audio|image create |
| Job list / detail / follow | bl finetune list / get / watch / logs |
| Artifacts and export | bl finetune checkpoints / export |
| Cancel / delete a job | bl finetune cancel / delete |
| Trainable capability lookup | bl finetune capability |
| Deploy / lifecycle | bl deploy text|audio|image create, list / get / update / scale / delete / models |
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
