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`](../bailian-protocol/SKILL.md): Version & updates (pre-flight checklist), Setup & auth, and CLI errors: report an issue. Command details are authoritative in [`reference/`](reference/index.md) (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/`](reference/index.md) or `bl <command> --help` — do not guess flags. ## Quick examples ```bash 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](../bailian-protocol/SKILL.md) — shared protocol (install via `bl skill init`) - [reference/](reference/index.md) — command details