bailian-finetune

bởi modelstudioai

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：`npx skills add modelstudioai/cli --all -g`（与共享协议...

npx skills add https://github.com/modelstudioai/cli --skill bailian-finetune

Tải ZIP GitHub

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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 --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 my-model --name my-svc

Unsure which training methods a base model supports → bl finetune capability --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 --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 my-qwen-sft --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).