bailian-finetune

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 npx skills add modelstudioai/cli --all -g; 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

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 npx skills add modelstudioai/cli --all -g）：

• 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 --all -g)
• reference/ — command details