Bailian fine-tuning pipeline (

bl dataset

/

bl finetune

/

bl deploy

)

../bailian-protocol/SKILL.md

reference/

bl <command> --help

npx skills add modelstudioai/cli --all -g

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

bl dataset validate

upload

.jsonl

.zip

bl dataset list

get

delete

bl finetune text|audio|image create

bl finetune list

get

watch

logs

bl finetune checkpoints

export

bl finetune cancel

delete

bl finetune capability

bl deploy text|audio|image create

list

get

update

scale

delete

models

reference/

bl <command> --help

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

--help

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