Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/modelstudioai/cli/bailian-finetunenpx skills add modelstudioai/cli --skill bailian-finetunegit clone --depth 1 https://github.com/modelstudioai/cliWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00241 | $0.01309 |
| Opus 5 | $0.00120 | $0.00655 |
| Sonnet 5 | $0.00048 | $0.00262 |
| Haiku 4.5 | $0.00024 | $0.00131 |
Grade A, and why
bailian-finetune scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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-typevalues:sft/sft-lora/dpo/dpo-lora/cpt. Audio bases includecosyvoice-v3-flash; image bases includewan2.7-image-pro. - Deployment plans: audio defaults to
--plan mu; text/image default tolora. - Preview write operations (create / delete / cancel / scale) with
--dry-runfirst, and confirm with the user before deleting a job or dataset.
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 |
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 76 lines · 241 tokens per session scan A aa003c09cea8
bailian-finetune is a skill published in the GitHub repository modelstudioai/cli (320 stars, last pushed 5d ago), licensed Apache-2.0. It adds 241 tokens to every session and 1,309 once invoked, about $0.0012 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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