---name: bailian-finetunemetadata: version: 2.1.0requires: bins: ["bl"]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
Query throughput reservations	bl deploy list --plan ptu / bl deploy get
Query capacity instances	bl deploy capacity list / get
Query / wait for a capacity operation	bl deploy operation get / wait
Buy / scale / renew / release capacity	bl deploy capacity create / scale / renew / delete
Unsubscribe a prepaid instance	bl deploy capacity unsubscribe (builds the billing console refund link)
Configure ModelCode overflow strategy	bl deploy overflow

The capacity list / get, operation get / wait and deploy list / get queries are read-only. Capacity values are kTPM; effective, configured and target capacities are distinct. deploy list --status filters only the fetched page locally, with the server total left unfiltered. operation get reports status as data; operation wait exits non-zero on failure or timeout and refreshes capacity after success. Use IDs returned by the API; waiting never retries a write.

The capacity create / scale / renew / delete and overflow commands are high-risk writes: preview with --dry-run, then confirm with the runtime-injected --yes. Each write is submitted once and never auto-retried; HTTP 200 is not success, so pass --wait or check operation_status. Scale values are one instance's absolute kTPM, not deltas or ModelCode totals, and zero is not release. capacity delete releases an instance but keeps the ModelCode; active prepaid instances cannot be DELETEd — run capacity unsubscribe --instance-id <id> to get the billing console refund link and finish there (no API exists for refunds). Release is confirmed by deleted=true. overflow applies to the whole ModelCode, not one instance. There is no estimator or standalone auto-renewal endpoint — renewal settings ride along purchase/scale/renew.

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

• bailian-protocol — shared protocol (install via bl skill init)
• reference/ — command details