# bailian-finetune

Drives a Bailian fine-tuning job end to end, from dataset check to a deployed service, with sft, lora, dpo and cpt on text, audio and image models.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 9200 installs · ★ 364
- Author: modelstudioai
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/modelstudioai/cli/tree/HEAD/skills/bailian-finetune/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/bailian-finetune

## What it does

The skill walks the whole pipeline in order: validate the dataset, upload it and get a file-id, create the job, watch the run, pick a checkpoint, export the model, compress it if you want, deploy a service. Training types are `sft`, `sft-lora`, `dpo`, `dpo-lora` and `cpt`. Text, audio and image bases all go through it, and `bl finetune capability` tells you which methods a given base accepts.

Mostly it is a dispatcher. Command detail sits in the `reference/` files for dataset, finetune and deploy, and flags come from `bl <command> --help` rather than from the skill text, so drift against the real CLI shows up fast.

Have the `bl` binary and an API key ready first. The shared `bailian-protocol` file has to be there too: `bl skill init` installs it alongside the skill, and the skill halts when that file is missing. For high-risk writes such as `capacity create`, `scale`, `renew`, `delete` and `overflow` it advises a `--dry-run` preview, and `--yes` is injected by the runtime, so do not type it yourself. Read the result from `operation_status` instead of trusting HTTP 200, and remember that `deploy list --status` filters only the page already fetched while the server total stays unfiltered.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add modelstudioai/cli --skill bailian-finetune -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/bailian-finetune/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/bailian-finetune/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add modelstudioai/cli --skill bailian-finetune -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/bailian-finetune/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add modelstudioai/cli --skill bailian-finetune -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/bailian-finetune/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install modelstudioai/cli bailian-finetune
```

2. **Manually: put the files in place**
- File: `.github/skills/bailian-finetune/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add modelstudioai/cli --skill bailian-finetune -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/modelstudioai/cli --path skills/bailian-finetune
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/bailian-finetune/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add modelstudioai/cli --skill bailian-finetune -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/modelstudioai/cli/tree/HEAD/skills/bailian-finetune/SKILL.md

## When to use it
- check a `.jsonl` against the required schema before upload
- upload a dataset and keep the returned file-id
- create an `sft-lora` job on a chosen base model
- pick a checkpoint and export the model from it
- deploy a service from the tuned model and read its state

## Example prompts
- Validate `train.jsonl` as chatml and upload it as my fine-tuning dataset
- Create an `sft-lora` job on a base model from `bl finetune capability` and follow it with watch
- Export checkpoint `ckpt-N` from job `ft-xxx` as a model named `my-model`

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 79c4f3dba2c3692d.

BEGIN THIRD-PARTY FILE 79c4f3dba2c3692d
````text
---
name: bailian-finetune
metadata:
  version: "2.5.1"
  requires:
    bins: ["bl"]
description: >-
  阿里云百炼模型精调训练入口：用户要精调、微调、训练自己的模型（fine-tune，支持 SFT / SFT-LoRA / DPO / DPO-LoRA / CPT，
  覆盖文本、语音、图像）、校验或上传训练数据集、看训练进度和日志、挑 checkpoint、导出精调产物、
  把专属模型部署成服务时使用 `bl dataset` / `bl finetune` / `bl deploy`。链路是 validate 校验数据 →
  upload 拿 file-id → finetune create 建任务 → watch 看进度 → export 导出 → 可选 compress 量化压缩 → deploy 上线，需要 API key；
  写操作先用 `--dry-run` 预览。反触发：用户点名火山方舟/ark 的精调不走本 skill；只是要选哪个模型走
  bailian-model-recommend；用现成模型生图生视频走 bailian-gen；百炼其他资源管理走 bailian-cli。
  官方安装：`bl skill init`（与共享协议 bailian-protocol 同装）。
---

# 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), Call attribution (every Agent `bl` command), 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. Examples in `reference/` stay bare; wrap with Call attribution when running. The whole pipeline requires an API key. If that protocol file is missing, stop and run `bl skill init`; do not guess auth/consent.**

**Call attribution:** Examples below and in `reference/` stay bare `bl …`. When running a command, wrap it with [`bailian-protocol` → Call attribution](../bailian-protocol/SKILL.md#call-attribution-mandatory-for-every-agent-bl-command) (bash or PowerShell). One sample for this skill (`BAILIAN_SKILL_NAME=bailian-finetune`; replace `codex` with this host). After deploy, `bl text chat` is owned by `bailian-cli`.

```bash
BAILIAN_CALLER_TYPE=agent BAILIAN_CALLER_NAME=codex BAILIAN_SKILL_NAME=bailian-finetune BAILIAN_SKILL_VERSION=2.5.0 bl dataset validate --file train.jsonl
```

## End-to-end workflow (follow in order)

```
1. Validate data   bl dataset validate --file train.jsonl [--schema chatml|dpo|cpt|tts|image]
````
END THIRD-PARTY FILE 79c4f3dba2c3692d

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