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Agent skill

Bailian Fine-Tuning Pipeline

by modelstudioai in modelstudioai/cli

Drives fine-tuning on Alibaba Cloud Model Studio with the bl CLI: validate and upload data, create a job, watch it, export results and deploy the model.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Bailian Fine-Tuning Pipeline

skills CLI
$ npx skills add modelstudioai/cli --skill bailian-finetune -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install modelstudioai/cli bailian-finetune --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/modelstudioai/cli.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/bailian-finetune .claude/skills/bailian-finetune && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bailian-finetune
GitHub stars
541
Token cost
~1.8k tokens
SKILL.md length
541 words
Files
5
Skills in repo
8
Repo updated
First seen
Licence
Apache-2.0

At a glance

Drives fine-tuning on Alibaba Cloud Model Studio with the bl CLI: validate and upload data, create a job, watch it, export results and deploy the model.

  • Fine-tuning a text, speech or image model on Alibaba Cloud Model Studio
  • SKILL.md covers End-to-end workflow (follow in…, When to use which command, Quick examples and Common hand-offs, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Validating and uploading a training dataset before creating a job

What it does

The workflow runs in order through `bl dataset` and `bl finetune`: validate a training file against a schema (chatml, dpo, cpt, tts or image), upload it to get a file ID, create a text, audio or image job from a base model, watch progress and logs, pick a checkpoint and export, then deploy with `bl deploy`. Training types include sft, sft-lora, dpo, dpo-lora and cpt, and a capability command shows what a base model supports. Deployment plans default to mu for audio and lora for text and image.

Before anything else the agent must read the shared `bailian-protocol` skill for version checks, setup and authentication, and if it is missing, run `bl skill init`. The whole pipeline needs an API key. Command details come from the `reference` folder and `bl --help` rather than guessed flags, write operations are previewed with `--dry-run` first, and for high-risk commands the agent never adds `--yes` by itself. Fine-tuning on Volcano Ark, model selection and image or video generation belong to other skills.

When your agent uses it

  • Fine-tuning a text, speech or image model on Alibaba Cloud Model Studio
  • Validating and uploading a training dataset before creating a job
  • Following a training job's progress and exporting a checkpoint
  • Deploying a fine-tuned model as a service

Example prompts

  • “Validate train.jsonl against the chatml schema and upload it for fine-tuning.”
  • “Create an sft-lora text fine-tuning job from that dataset, preview it with dry-run first.”
  • “Watch the running fine-tuning job and export the best checkpoint when it finishes.”

Requirements

  • The `bl` CLI installed with `bl skill init`
  • An API key for Alibaba Cloud Model Studio

What it can do on your machine

Read from SKILL.md and the folder at commit 8bbbbc7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bailian Fine-Tuning Pipeline loads about 1.8k tokens when it runs. Until then it costs about 124 tokens; SKILL.md has 541 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~124
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from modelstudioai/cli at commit 8bbbbc7, republished under its Apache-2.0 licence (© modelstudioai). 541 words, ~1,768 tokens.

Download SKILL.mdSave it as .claude/skills/bailian-finetune/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
bailian-finetune
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：`bl skill init`（与共享协议 bailian-protocol 同装）。
metadata.version
2.1.0

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

IntentCommand
Validate / upload training databl dataset validate / upload (.jsonl or .zip)
Dataset list / detail / deletebl dataset list / get / delete
Create a fine-tuning jobbl finetune text|audio|image create
Job list / detail / followbl finetune list / get / watch / logs
Artifacts and exportbl finetune checkpoints / export
Cancel / delete a jobbl finetune cancel / delete
Trainable capability lookupbl finetune capability
Deploy / lifecyclebl deploy text|audio|image create, list / get / update / scale / delete / models
Query throughput reservationsbl deploy list --plan ptu / bl deploy get
Query capacity instancesbl deploy capacity list / get
Query / wait for a capacity operationbl deploy operation get / wait
Buy / scale / renew / release capacitybl deploy capacity create / scale / renew / delete
Unsubscribe a prepaid instancebl deploy capacity unsubscribe (builds the billing console refund link)
Configure ModelCode overflow strategybl 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.

Show full SKILL.md (200 more words)Show less

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

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

© modelstudioai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files in skills/bailian-finetune of modelstudioai/cli.

  • SKILL.md
  • reference/dataset.md
  • reference/deploy.md
  • reference/finetune.md
  • reference/index.md

Open the folder on GitHubat commit 8bbbbc7

Compare with similar skills

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Train RlOpenPipe/ART11k—~2.4kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Bailian Fine-Tuning Pipeline

What does Bailian Fine-Tuning Pipeline do?

Drives fine-tuning on Alibaba Cloud Model Studio with the bl CLI: validate and upload data, create a job, watch it, export results and deploy the model. The workflow runs in order through `bl dataset` and `bl finetune`: validate a training file against a schema (chatml, dpo, cpt, tts or image), upload it to get a file ID, create a text, audio or image job from a base model, watch progress and logs, pick a checkpoint and export, then deploy with `bl deploy`. Training types include sft, sft-lora, dpo, dpo-lora and cpt, and a capability command shows what a base model supports.

When should I use Bailian Fine-Tuning Pipeline?

Bailian Fine-Tuning Pipeline fits situations like: fine-tuning a text, speech or image model on Alibaba Cloud Model Studio; validating and uploading a training dataset before creating a job; following a training job's progress and exporting a checkpoint; deploying a fine-tuned model as a service.

How do I install Bailian Fine-Tuning Pipeline in Claude Code?

Run `npx skills add modelstudioai/cli --skill bailian-finetune -a claude-code`. Or copy the skill folder (skills/bailian-finetune in modelstudioai/cli) into .claude/skills/bailian-finetune in your project. Claude Code loads it when a task matches its description.

How do I install Bailian Fine-Tuning Pipeline in Codex?

Run `npx skills add modelstudioai/cli --skill bailian-finetune -a codex`. Or copy the skill folder (skills/bailian-finetune in modelstudioai/cli) into .agents/skills/bailian-finetune in your project. Codex loads it when a task matches its description.

Can I use Bailian Fine-Tuning Pipeline in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add modelstudioai/cli --skill bailian-finetune -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bailian-finetune, .gemini/skills/bailian-finetune, .github/skills/bailian-finetune and .opencode/skills/bailian-finetune in your project.

What does Bailian Fine-Tuning Pipeline need to run?

SKILL.md names no scripts, command-line tools or credentials: Bailian Fine-Tuning Pipeline is instructions for the agent only. Our summary lists: The `bl` CLI installed with `bl skill init`; An API key for Alibaba Cloud Model Studio.

Does Bailian Fine-Tuning Pipeline access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Bailian Fine-Tuning Pipeline safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bailian Fine-Tuning Pipeline use?

Bailian Fine-Tuning Pipeline is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bailian Fine-Tuning Pipeline use?

About 1.8k tokens (SKILL.md is roughly 7.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bailian Fine-Tuning Pipeline?

Skills that share tags, products or a category with Bailian Fine-Tuning Pipeline: Peft Fine Tuning (Orchestra-Research/AI-Research-SKILLs, 13k stars), Hugging Face LLM Trainer (huggingface/skills, 11k stars), Sentence-Transformers Training Router (huggingface/skills, 11k stars) and Dataset Evaluation (awslabs/agent-plugins, 912 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bailian Fine-Tuning Pipeline?

modelstudioai (a GitHub organization) maintains it in modelstudioai/cli, which has 541 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on September 30, 2026.

Source: modelstudioai/cli on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.