Agent Skills › modelstudioai/cli › bailian-finetune

bailian-finetune

GitHub

提供阿里云百炼模型精调全流程支持，涵盖数据校验上传、SFT/DPO等训练任务创建与监控、Checkpoint导出及专属模型部署上线。

skills/bailian-finetune/SKILL.md modelstudioai/cli

Trigger Scenarios

用户需要微调或精调自己的AI模型用户希望查看或管理模型训练进度和日志用户需要将精调后的模型部署为服务

Install

npx skills add modelstudioai/cli --skill bailian-finetune -g -y

More Options

Use without installing

npx skills use modelstudioai/cli@bailian-finetune

指定 Agent (Claude Code)

npx skills add modelstudioai/cli --skill bailian-finetune -a claude-code -g -y

安装 repo 全部 skill

npx skills add modelstudioai/cli --all -g -y

预览 repo 内 skill

npx skills add modelstudioai/cli --list

SKILL.md

Frontmatter

{
    "name": "bailian-finetune",
    "metadata": {
        "version": "1.14.0",
        "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 → 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：`npx skills add modelstudioai\/cli --all -g`（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: 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 npx skills add modelstudioai/cli --all -g; 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 --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

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`

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 --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

软 hand-off（按 skill 名；已安装则 Read，否则 --help / 提示 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).