@modelstudioai/bailian-finetune

@modelstudioai/bailian-finetune — AI coding skill

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SKILL.md
namebailian-finetune
description>-

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.
  • For risk: high or requires_confirmation, follow bailian-protocol; never add --yes automatically.

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 ;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

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