bailian-train-deploy

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

百炼模型训练→部署闭环 (

bl

)

Bailian Model Training → Deployment Loop (

bl

)

用百炼 CLI

bl

把模型部署成可调用的专属推理服务——可以先微调再部署，也可以跳过训练直接部署基座。支持文本、音频 TTS 和图像生成三种模态，两处"等待"用 Monitor 异步轮询，不阻塞主流程。

链路 A（先训练后部署）：
数据集 → finetune <模态> create → 等 SUCCEEDED → 导出模型(通常自动) → deploy <模态> create → 等 RUNNING → 调用

链路 B（直接部署基座，跳过训练）：
选基座 → deploy <模态> create → 等 RUNNING → 调用

链路 B 适用于"只想把某个基座跑成自己的部署服务"——和直接调用 API 在推理上没本质区别，但能拿到独立部署实例、可调速率/计费方式、纳入自己的运维。仅当用户明确表示不训练 / 跳过训练 / 直接部署基座时才走这条；用户只是没提训练细节时，默认按链路 A 引导，不要擅自跳过训练。

模态分发：本技能的通用流程适用于所有模态，但数据格式、超参、推理命令因模态而异。确定用户意图后，按模态查阅对应参考文件：

文本模型（对话/推理）→
```
references/text.md
```
音频 TTS 模型（语音合成/CosyVoice）→
```
references/audio.md
```
图像生成模型（文生图/图生图/Wan2.7）→
```
references/image.md
```
未来新增模态 = 新增对应 reference 文件，本流程骨架不变

本技能假设
bl
（bailian-cli）已安装。命令/flag 细节以
bl <cmd> --help
为准；本技能聚焦流程编排与避坑。

Deploy models into callable exclusive inference services using Bailian CLI

bl

— you can fine-tune first then deploy, or deploy base models directly skipping training. Supports three modalities: text, audio TTS, and image generation. The two "waiting" steps use Monitor for asynchronous polling without blocking the main process.

Pipeline A (Train first then deploy):
Dataset → finetune <modal> create → Wait for SUCCEEDED → Export model (usually automatic) → deploy <modal> create → Wait for RUNNING → Inference

Pipeline B (Deploy base model directly, skip training):
Select base model → deploy <modal> create → Wait for RUNNING → Inference

Pipeline B applies to scenarios where you "just want to run a certain base model as your own deployment service" — there's no essential difference from directly calling the API for inference, but you get an independent deployment instance, adjustable rate/billing method, and can integrate it into your own O&M. Only use this pipeline when users explicitly state they don't want to train / skip training / deploy base models directly; when users only don't mention training details, guide them using Pipeline A by default, do not skip training without permission.

Modality Distribution: The general process of this skill applies to all modalities, but data formats, hyperparameters, and inference commands vary by modality. After confirming the user's intent, refer to the corresponding reference file by modality:

Text Models (dialogue/inference) →
```
references/text.md
```
Audio TTS Models (speech synthesis/CosyVoice) →
```
references/audio.md
```
Image Generation Models (text-to-image/image-to-image/Wan2.7) →
```
references/image.md
```
Future new modalities = add corresponding reference files, the skeleton of this process remains unchanged

This skill assumes
bl
(bailian-cli) is already installed. For details on commands/flags, refer to
bl <cmd> --help
; this skill focuses on process orchestration and pitfall avoidance.

写操作护栏（创建前必读）

Write Operation Guardrails (Read Before Creation)

bl finetune <模态> create

与

bl deploy <模态> create

都是真实写操作，会产生计费资源（微调训练 + 推理部署）。

bl

没有
--dry-run
，所以用预检命令代替预演、用计费确认把关预留资源。任何写操作前必须先过这三道闸：

预检代替 dry-run（创建前必跑，确认可行再写）。这些预检命令本身都需先通过下方前置检查的认证——未认证先
```
bl auth login
```
再预检：
- 训练前：
```
bl finetune capability --model <base>
```
  —— 确认基座支持你选的 training-type（不支持会快速失败且不耗配额；
```
create
```
  提交时也会再校验一次）。
- 部署前：
```
bl deploy models --source custom
```
  （链路 A 微调输出）或
```
--source base
```
  （链路 B 基座）—— 确认目标模型可部署、看清可用 plan，再决定
```
--plan
```
  。
- 复用检测：
```
bl deploy list --status RUNNING
```
  —— 若已有引用同一
```
finetuned_output
```
  且 RUNNING 的部署，直接复用其
```
deployed_model
```
  ，不要再建第二个计费实例。
计费确认硬闸门（mu/ptu）：
- ```
lora
```
  （默认，按 token 计费，闲置一般不计费）—— 安全默认，可直接创建。
- ```
mu
```
  /
```
ptu
```
  是预留/独占资源，闲置也计费——创建前必须向用户显式说明计费方式并取得确认。在 agent / CI 等非交互环境不能自动开通预留资源。命中 mu/ptu 时，把“这会产生闲置计费”连同命令交还给真人在终端确认（
```
bl
```
  当前不提供
```
--yes
```
  跳过确认 flag，确认行为完全由真人把控）。
账号就绪检查：
```
bl auth status
```
——
```
authenticated: false
```
即停，给
```
bl auth login --api-key sk-...
```
。百炼走 API key / access token 认证，没有独立的实名闸门，
```
auth status
```
即账号就绪检查。

bl finetune <modal> create

and

bl deploy <modal> create

are real write operations that will generate billed resources (fine-tuning training + inference deployment).

bl

does not have
--dry-run
, so use pre-check commands instead of dry runs and billing confirmation to gate reserved resources. Any write operation must pass these three gates first:

Pre-check instead of dry-run (must run before creation, confirm feasibility before writing). These pre-check commands require authentication via the Pre-checks below — if not authenticated, run
```
bl auth login
```
first then perform pre-checks:
- Before training:
```
bl finetune capability --model <base-model>
```
  — confirm that the base model supports the training-type you selected (if not supported, it will fail quickly without consuming quotas; the
```
create
```
  submission will also validate this again).
- Before deployment:
```
bl deploy models --source custom
```
  (fine-tuned output for Pipeline A) or
```
--source base
```
  (base model for Pipeline B) — confirm that the target model can be deployed, check available plans, then decide on
```
--plan
```
  .
- Reuse detection:
```
bl deploy list --status RUNNING
```
  — if there is already a running deployment referencing the same
```
finetuned_output
```
  , directly reuse its
```
deployed_model
```
  , do not create a second billed instance.
Billing Confirmation Hard Gate (mu/ptu):
- ```
lora
```
  (default, billed by token, generally no charge when idle) — safe default, can be created directly.
- ```
mu
```
  /
```
ptu
```
  are reserved/exclusive resources, billed even when idle — before creation, must explicitly explain the billing method to the user and obtain confirmation. In non-interactive environments such as agents / CI, do not automatically enable reserved resources. When mu/ptu is involved, hand over the command along with "this will incur idle charges" to a human for confirmation in the terminal (currently
```
bl
```
  does not provide a
```
--yes
```
  flag to skip confirmation, and confirmation is fully controlled by humans).
Account Readiness Check:
```
bl auth status
```
— stop if
```
authenticated: false
```
, provide
```
bl auth login --api-key sk-...
```
. Bailian uses API key / access token for authentication, there is no separate real-name verification gate,
```
auth status
```
serves as the account readiness check.

反触发表（不归本 skill 的意图，附完整命令）

Anti-Trigger List (Intents Not Covered by This Skill, with Complete Commands)

用户意图	路由到	完整示范命令
只想试模型效果 / 一次性对话	`bailian-cli`	`bl text chat --model qwen3-8b --message "..."`
已确定模型，只要调用方式	`bailian-cli`	`bl text chat --model <model> --message "..."`
不知道选哪个基座 / 模型选型	`bailian-model-recommend`	（让该 skill 按场景推荐）
纯查模型参数 / 价格 / 上下文窗口	`bailian-docs-llm-wiki`	（查模型数据目录）
已有部署，只想生成调用示例	`bailian-cli`	`bl text chat --model <deployed_model> --message "..."` 或 `bl speech synthesize --model <deployed_model> --voice default --text "..."` 或异步 API + 触发词（图像生成，见 image.md）
对已有训练任务 / 部署做查删（生命周期）	`bl` 直接	`bl finetune list` / `bl deploy list` / `bl deploy delete --deployed-model <id>`

本 skill 只负责"新建训练任务 + 新建部署 + 调用交付"这一条闭环。训练任务与部署的全生命周期管理（list / stop / delete 历史任务、删除部署等）不在本 skill 流程内，用
bl finetune list
/
bl deploy list
/
bl deploy delete
直接操作。

User Intent	Route to	Complete Example Command
Just want to test model performance / one-time dialogue	`bailian-cli`	`bl text chat --model qwen3-8b --message "..."`
Model is confirmed, only need inference methods	`bailian-cli`	`bl text chat --model <model> --message "..."`
Don't know which base model to choose / model selection	`bailian-model-recommend`	(Let that skill recommend based on scenarios)
Purely check model parameters / price / context window	`bailian-docs-llm-wiki`	(Check model data directory)
Deployment already exists, only need to generate inference examples	`bailian-cli`	`bl text chat --model <deployed_model> --message "..."` or `bl speech synthesize --model <deployed_model> --voice default --text "..."` or asynchronous API + trigger words (image generation, see image.md)
Query/delete existing training tasks / deployments (lifecycle management)	Directly use `bl`	`bl finetune list` / `bl deploy list` / `bl deploy delete --deployed-model <id>`

This skill only handles the closed loop of "create new training task + create new deployment + deliver inference". Full lifecycle management of training tasks and deployments (list / stop / delete historical tasks, delete deployments, etc.) is not within the scope of this skill; use
bl finetune list
/
bl deploy list
/
bl deploy delete
for direct operations.

反幻觉清单

Anti-Hallucination Checklist

--model
在不同命令里含义不同，切勿复用：
- ```
bl finetune <模态> create --model
```
  → 基座模型名（文本
```
qwen3-8b
```
  ，音频
```
cosyvoice-v3-flash
```
  ，图像
```
wan2.7-image-pro
```
  ）。
- ```
bl deploy <模态> create --model
```
  → 导出模型名（链路 A：
```
qwen3-8b-ft-...
```
  /
```
cosyvoice-v3-flash-ft-...
```
  /
```
wan2.7-image-pro-ft-...
```
  ；链路 B：基座名）。
- 推理命令
```
--model
```
  → 必须用
```
deploy <模态> create
```
  响应里的
```
deployed_model
```
  ，不是你传给 deploy <模态> create 的名字。
--training-type
取值穷举：
```
sft
```
/
```
sft-lora
```
（默认）/
```
dpo
```
/
```
dpo-lora
```
/
```
cpt
```
。映射在 CLI 边界完成（
```
sft-lora
```
→
```
efficient_sft
```
），永远传 CLI 值，不要传服务端字符串。
```
cpt
```
无
```
-lora
```
变体。音频 TTS 和图像生成都只用
```
sft-lora
```
——
```
finetune audio create
```
/
```
finetune image create
```
不暴露此 flag，仅
```
finetune text create
```
接受。
--plan
取值穷举：
```
lora
```
（默认，token 计费）/
```
ptu
```
（需
```
--input-tpm
```
/
```
--output-tpm
```
）/
```
mu
```
（需
```
--deploy-spec
```
/
```
--capacity
```
）。链路 B 基座通常不支持
lora
。音频 TTS 微调模型只支持
mu
。图像生成微调模型只支持
lora
。
--deploy-spec
（非 ~~template-id~~）：mu plan 的部署规格 flag。CLI 从 catalog 读取
```
template_id
```
，在请求体中发送为
```
deploy_spec
```
。
--source
取值穷举（
```
bl deploy models
```
）：
```
custom
```
（微调输出）/
```
base
```
（基座）/
```
public
```
。
--learning-rate
必须字符串：传
```
"3e-4"
```
，不要传数字
```
3e-4
```
，避免 JSON 精度丢失（文本适用，音频/图像不需要传此参数）。
推理命令因模态不同：文本模型用
```
bl text chat
```
，音频 TTS 用
```
bl speech synthesize --voice default
```
，图像生成用异步 API + 触发词（详见
```
references/image.md
```
）。
没有这些 flag/子命令：
```
bl
```
无
```
--dry-run
```
、无裸
```
finetune create
```
/
```
deploy create
```
（必须带模态段：
```
finetune text|audio|image create
```
、
```
deploy text|audio|image create
```
）、无
```
finetune start
```
、无
```
deploy stop
```
（CLI 暂无 stop 命令，RUNNING 的 mu/ptu 需到控制台停用）。

必填：

finetune <模态> create

的

--model

--datasets

；

deploy <模态> create

的

--model

--name

。

--model
has different meanings in different commands, do not reuse it:
- ```
bl finetune <modal> create --model
```
  → Base model name (text
```
qwen3-8b
```
  , audio
```
cosyvoice-v3-flash
```
  , image
```
wan2.7-image-pro
```
  ).
- ```
bl deploy <modal> create --model
```
  → Exported model name (Pipeline A:
```
qwen3-8b-ft-...
```
  /
```
cosyvoice-v3-flash-ft-...
```
  /
```
wan2.7-image-pro-ft-...
```
  ; Pipeline B: base model name).
- Inference command
```
--model
```
  → Must use
```
deployed_model
```
  from the response of
```
deploy <modal> create
```
  , not the name you passed to deploy <modal> create.
--training-type
values are exhaustive:
```
sft
```
/
```
sft-lora
```
(default) /
```
dpo
```
/
```
dpo-lora
```
/
```
cpt
```
. Mapping is completed at the CLI boundary (
```
sft-lora
```
→
```
efficient_sft
```
), always pass CLI values, do not pass server-side strings.
```
cpt
```
has no
```
-lora
```
variant. Audio TTS and image generation only use
```
sft-lora
```
—
```
finetune audio create
```
/
```
finetune image create
```
do not expose this flag, only
```
finetune text create
```
accepts it.
--plan
values are exhaustive:
```
lora
```
(default, billed by token) /
```
ptu
```
(requires
```
--input-tpm
```
/
```
--output-tpm
```
) /
```
mu
```
(requires
```
--deploy-spec
```
/
```
--capacity
```
). Base models in Pipeline B usually do not support
lora
. Audio TTS fine-tuned models only support
mu
. Image generation fine-tuned models only support
lora
.
--deploy-spec
(not ~~template-id~~): Deployment specification flag for mu plan. The CLI reads
```
template_id
```
from the catalog and sends it as
```
deploy_spec
```
in the request body.
--source
values are exhaustive (
```
bl deploy models
```
):
```
custom
```
(fine-tuned output) /
```
base
```
(base model) /
```
public
```
.
--learning-rate
must be a string: Pass
```
"3e-4"
```
, do not pass the number
```
3e-4
```
to avoid JSON precision loss (applies to text models, not required for audio/image).
Inference commands vary by modality: Use
```
bl text chat
```
for text models,
```
bl speech synthesize --voice default
```
for audio TTS models, and asynchronous API + trigger words for image generation (see
```
references/image.md
```
).
These flags/subcommands do not exist:
```
bl
```
has no
```
--dry-run
```
, no bare
```
finetune create
```
/
```
deploy create
```
(must include modality segment:
```
finetune text|audio|image create
```
,
```
deploy text|audio|image create
```
), no
```
finetune start
```
, no
```
deploy stop
```
(CLI has no stop command yet, running mu/ptu instances need to be disabled in the console).

Required parameters:

--model

--datasets

for

finetune <modal> create

;

--model

--name

for

deploy <modal> create

前置检查（动作流起点）

Pre-checks (Start of Action Flow)

认证：

bl auth status

，确认已配置 API key（

DASHSCOPE_API_KEY

或

bl auth login --api-key sk-...

）。

基座选型：查询训练能力用
```
bl finetune capability
```
（查 listFoundationModels，走 API key、无需 console 登录）：
- ```
bl finetune capability --model <base>
```
  —— 该模型支持哪些训练类型。
- ```
bl finetune capability --training-type sft-lora
```
  —— 反向查：哪些模型支持该训练类型（返回
```
models
```
  列表，含中文名）。
- 选定基座后可直接进入第 2 步；
```
bl finetune <模态> create
```
  提交前也会再用 listFoundationModels 校验，不支持会快速失败。
- 文本推理推荐 Qwen3 系列（
```
qwen3-8b
```
  /
```
qwen3-14b
```
  /
```
qwen3.6-flash
```
  ）；音频 TTS 用
```
cosyvoice-v3-flash
```
  ；图像生成用
```
wan2.7-image-pro
```
  /
```
wan2.7-image
```
  。

Authentication:

bl auth status

, confirm API key is configured (

DASHSCOPE_API_KEY

bl auth login --api-key sk-...

Base model selection: Query training capabilities with
```
bl finetune capability
```
(queries listFoundationModels, uses API key, no console login required):
- ```
bl finetune capability --model <base-model>
```
  — which training types are supported by this model.
- ```
bl finetune capability --training-type sft-lora
```
  — reverse query: which models support this training type (returns
```
models
```
  list with Chinese names).
- After selecting a base model, you can directly proceed to Step 2;
```
bl finetune <modal> create
```
  will also validate with listFoundationModels before submission, and will fail quickly if not supported.
- Recommended text inference models are Qwen3 series (
```
qwen3-8b
```
  /
```
qwen3-14b
```
  /
```
qwen3.6-flash
```
  ); use
```
cosyvoice-v3-flash
```
  for audio TTS; use
```
wan2.7-image-pro
```
  /
```
wan2.7-image
```
  for image generation.

第 1 步：准备数据集

Step 1: Prepare Dataset

支持三种数据来源，由你（Agent）根据用户意图灵活选用，不必写死交互流程——你本身具备主动提问能力，知道有哪些选项后自然向用户确认即可：

本地数据集 —— 用户提供本地文件路径，直接使用。
已上传数据集 —— 从百炼上已有的数据集中选取（
```
bl dataset list
```
）。
生成示例数据 —— 征得用户同意后，由你生成一份小规模示例数据，仅用于跑通流程（效果有限，需如实告知用户）。

数据格式因模态不同，详见对应参考文件：

文本模型：
```
.jsonl
```
文件（ChatML/DPO/CPT schema），详见
```
references/text.md
```
。

音频 TTS：

.zip

包（内含

data.jsonl

train/*.wav

），详见

references/audio.md

。

图像生成：
```
.zip
```
包（内含
```
data.jsonl
```
+ 图像文件平铺），详见
```
references/image.md
```
。

提交训练前用

bl dataset validate --file <path>

校验通过再继续（CLI 自动探测 schema，也可

--schema tts

--schema image

--schema chatml

显式指定）。

Supports three data sources, you (Agent) can flexibly choose based on user intent, do not hardcode the interaction process — you have the ability to ask questions proactively, so naturally confirm with users after knowing the options:

Local Dataset — User provides local file path, use directly.
Uploaded Dataset — Select from existing datasets on Bailian (
```
bl dataset list
```
).
Generate Sample Data — With user consent, generate a small-scale sample data only for running through the process (limited effect, need to inform users truthfully).

Data format varies by modality, see corresponding reference files:

Text Models:
```
.jsonl
```
files (ChatML/DPO/CPT schema), see
```
references/text.md
```
.

Audio TTS:

.zip

package (contains

data.jsonl

train/*.wav

), see

references/audio.md

Image Generation:
```
.zip
```
package (contains
```
data.jsonl
```
+ flat image files), see
```
references/image.md
```
.

Before submitting training, run

bl dataset validate --file <path>

to pass validation before proceeding (CLI automatically detects schema, you can also explicitly specify with

--schema tts

--schema image

--schema chatml

第 2 步：创建微调任务

Step 2: Create Fine-tuning Task

bash

bl finetune <模态> create \
  --model <base-model> \
  --datasets <path-or-file-id> \
  --output json

training-type 取值与映射详见

references/finetune.md

。模态特异性超参见对应参考文件：

文本模型（

finetune text create

）：接受

--training-type

--n-epochs

--learning-rate

等，详见

references/text.md

。

音频 TTS（
```
finetune audio create
```
）：不暴露
--training-type
（内部固定
```
sft-lora
```
），超参由 CLI 自动注入，详见
```
references/audio.md
```
。
图像生成（
```
finetune image create
```
）：不暴露
--training-type
（内部固定
```
sft-lora
```
），超参由 CLI 自动注入，详见
```
references/image.md
```
。

从响应记下：

output.job_id

、

output.finetuned_output

（输出模型名，形如

<base>-ft-<ts>-<id>

）。

bash

bl finetune <modal> create \
  --model <base-model> \
  --datasets <path-or-file-id> \
  --output json

training-type values and mapping see

references/finetune.md

. Modality-specific hyperparameters see corresponding reference files:

Text models (

finetune text create

): Accept

--training-type

--n-epochs

--learning-rate

, etc., see

references/text.md

Audio TTS (
```
finetune audio create
```
): Does not expose
--training-type
(internally fixed to
```
sft-lora
```
), hyperparameters are automatically injected by CLI, see
```
references/audio.md
```
.
Image generation (
```
finetune image create
```
): Does not expose
--training-type
(internally fixed to
```
sft-lora
```
), hyperparameters are automatically injected by CLI, see
```
references/image.md
```
.

Record from the response:

output.job_id

output.finetuned_output

(exported model name, in the format

<base>-ft-<ts>-<id>

第 3 步：等待训练完成（异步）

Step 3: Wait for Training Completion (Asynchronous)

用 Monitor 工具运行本技能自带的等待脚本——它会在状态变化时通知，到终态退出：

Monitor command: bash <本技能目录>/scripts/wait.sh finetune <JOB_ID>

<本技能目录>

即本技能的 base 目录（技能加载时会给出，含

scripts/wait.sh

），用实际路径替换。脚本每 30s 轮询，终态为

SUCCEEDED

FAILED

CANCELED

PARTIALLY_SUCCEEDED

。

⚠️ 避坑：不要在 zsh 里手写
status=...
轮询循环。

status

是 zsh 的只读内置变量，赋值会报

read-only variable

并让脚本 exit 1。用本技能的 bash 脚本（

#!/usr/bin/env bash

）规避，或自写时改用

st

等变量名。

Use the Monitor tool to run the built-in waiting script of this skill — it will notify when status changes and exit when reaching terminal state:

Monitor command: bash <skill-directory>/scripts/wait.sh finetune <JOB_ID>

<skill-directory>

is the base directory of this skill (provided when the skill is loaded, contains

scripts/wait.sh

), replace with the actual path. The script polls every 30s, terminal states are

SUCCEEDED

FAILED

CANCELED

PARTIALLY_SUCCEEDED

⚠️ Pitfall Avoidance: Do not write a manual
status=...
polling loop in zsh.

status

is a read-only built-in variable in zsh, assigning it will report

read-only variable

and exit the script with code 1. Use the bash script of this skill (

#!/usr/bin/env bash

) to avoid this, or use variable names like

st

when writing your own.

第 4 步：导出最佳模型（通常可跳过）

Step 4: Export Best Model (Usually Skippable)

任务 SUCCEEDED 后，平台会自动导出 best checkpoint 为可部署模型——直接进第 5 步即可，无需手动导出。

只有要部署非 best 的某个 checkpoint 时才显式导出：

bash

bl finetune checkpoints --job-id <JOB_ID>          # 列出可用 checkpoint
bl finetune export --job-id <JOB_ID> --checkpoint <name> --model-name <自定义名>

After the task is SUCCEEDED, the platform will automatically export the best checkpoint as a deployable model — you can directly proceed to Step 5 without manual export.

Only explicitly export when you need to deploy a non-best checkpoint:

bash

bl finetune checkpoints --job-id <JOB_ID>          # List available checkpoints
bl finetune export --job-id <JOB_ID> --checkpoint <name> --model-name <custom-name>

第 5 步：创建部署

Step 5: Create Deployment

创建前先过写操作护栏：
bl deploy list --status RUNNING
查是否已有同模型部署可复用；
bl deploy models --source custom|base
确认可用 plan；
mu
/
ptu
必须先取得用户计费确认。

⚠️ 关键避坑：微调后的模型不能直接用微调输出名调用，会 404
Model not exist
。必须先创建部署。（链路 B 部署基座同理——直接调用走的是公共推理，不经过你的部署实例。）

bash

bl deploy <模态> create \
  --model <model-name> \              # 微调输出名(链路A) 或基座名(链路B)
  --name <display-name> \
  --plan <lora|ptu|mu> \              # 见下方说明
  --output json

模态段（
```
text
```
/
```
audio
```
/
```
image
```
）须与部署模型模态匹配；三个模态子命令 flag 完全相同。
```
--model
```
：链路 A 传第 2 步的
```
finetuned_output
```
；链路 B 直接传基座模型名。
```
--plan
```
：
- 文本微调模型默认
```
lora
```
  （token 计费），也可用
```
mu
```
  。
- 音频 TTS 微调模型只支持
  mu
  （不支持
```
lora
```
  /
```
ptu
```
  ），需
```
--deploy-spec
```
  +
```
--capacity
```
  。
- 图像生成微调模型只支持
  lora
  （不支持
```
mu
```
  /
```
ptu
```
  ）。
- 链路 B 基座通常只支持
```
ptu
```
  /
```
mu
```
  ，不支持
  lora
  。
- 各 plan 必填参数与计费细则见
```
references/deploy.md
```
  。
```
--deploy-spec
```
：mu plan 的部署规格（如
```
dps-20260521172224-1vabse
```
），省略时自动从 catalog 匹配。
不确定支持哪些 plan：链路 A 用
```
bl deploy models --source custom
```
，链路 B 用
```
bl deploy models --source base
```
，按返回的
```
plans
```
选。

⚠️ 避坑（最高频错误）：
--model
在
deploy <模态> create
与推理命令里含义不同——

deploy <模态> create --model

传导出模型名，响应返回的

output.deployed_model

才是部署实例 id，推理命令

--model

必须用

deployed_model

，不要复用。详见

references/deploy.md

。

从响应记下：

output.deployed_model

。

Before creation, pass the Write Operation Guardrails:
bl deploy list --status RUNNING
to check if there is an existing deployment of the same model that can be reused;
bl deploy models --source custom|base
to confirm available plans;
mu
/
ptu
must obtain user billing confirmation first.

⚠️ Critical Pitfall: Fine-tuned models cannot be directly called using the fine-tuned output name, it will return 404
Model not exist
. You must create a deployment first. (Same for deploying base models in Pipeline B — direct calling uses public inference and does not go through your deployment instance.)

bash

bl deploy <modal> create \
  --model <model-name> \              # Fine-tuned output name (Pipeline A) or base model name (Pipeline B)
  --name <display-name> \
  --plan <lora|ptu|mu> \              # See explanation below
  --output json

Modality segment (
```
text
```
/
```
audio
```
/
```
image
```
) must match the modality of the deployed model; the flags of the three modality subcommands are identical.
```
--model
```
: Pass
```
finetuned_output
```
from Step 2 for Pipeline A; directly pass the base model name for Pipeline B.
```
--plan
```
:
- Default
```
lora
```
  (billed by token) for text fine-tuned models,
```
mu
```
  is also available.
- Audio TTS fine-tuned models only support
  mu
  (do not support
```
lora
```
  /
```
ptu
```
  ), require
```
--deploy-spec
```
  +
```
--capacity
```
  .
- Image generation fine-tuned models only support
  lora
  (do not support
```
mu
```
  /
```
ptu
```
  ).
- Base models in Pipeline B usually only support
```
ptu
```
  /
```
mu
```
  , do not support
  lora
  .
- Required parameters and billing details for each plan see
```
references/deploy.md
```
  .
```
--deploy-spec
```
: Deployment specification for mu plan (e.g.,
```
dps-20260521172224-1vabse
```
), automatically matched from the catalog if omitted.
Unsure which plans are supported: Use
```
bl deploy models --source custom
```
for Pipeline A,
```
bl deploy models --source base
```
for Pipeline B, select from the returned
```
plans
```
.

⚠️ Top-Frequency Error Pitfall:
--model
has different meanings in
deploy <modal> create
and inference commands —

deploy <modal> create --model

passes the exported model name,

output.deployed_model

returned in the response is the deployment instance ID, and the inference command

--model

must use

deployed_model

, do not reuse the exported model name. See

references/deploy.md

Record from the response:

output.deployed_model

第 6 步：等待部署就绪（异步）

Step 6: Wait for Deployment Readiness (Asynchronous)

Monitor command: bash <本技能目录>/scripts/wait.sh deploy <DEPLOYED_MODEL>

<本技能目录>

同第 3 步。每 15s 轮询，

RUNNING

即就绪（

FAILED

STOPPED

终止）。

⚠️ 避坑：状态传播延迟。 部署刚到

RUNNING

时立即调用，可能短暂返回 404

Model not exist

——这是服务端状态传播延迟，不是用错模型名。

bl deploy get

也可能还显示

PENDING

。约 1 分钟内会稳定，遇 404 等十几秒重试即可；若持续 404，先核对用的是

deployed_model

而非微调输出名。

Monitor command: bash <skill-directory>/scripts/wait.sh deploy <DEPLOYED_MODEL>

<skill-directory>

is the same as in Step 3. Polls every 15s,

RUNNING

means ready (terminate if

FAILED

STOPPED

⚠️ Pitfall Avoidance: Status Propagation Delay. Immediately calling the model when deployment just reaches

RUNNING

may briefly return 404

Model not exist

— this is server-side status propagation delay, not an incorrect model name.

bl deploy get

may also still show

PENDING

. It will stabilize within about 1 minute; if you encounter 404, wait ten seconds and retry; if it persists, first check if you are using

deployed_model

instead of the fine-tuned output name.

第 7 步：调用与交付

Step 7: Inference and Delivery

推理命令因模态不同：

文本模型：

bash

bl text chat --model <DEPLOYED_MODEL> --message "你的问题"

音频 TTS 模型：

bash

bl speech synthesize --model <DEPLOYED_MODEL> --voice default --text "你要合成的文本" --out result.mp3

图像生成模型（异步 API，需触发词）：

bash

undefined

Inference commands vary by modality:

Text models:

bash

bl text chat --model <DEPLOYED_MODEL> --message "Your question"

Audio TTS models:

bash

bl speech synthesize --model <DEPLOYED_MODEL> --voice default --text "Text you want to synthesize" --out result.mp3

Image generation models (asynchronous API, requires trigger words):

bash

undefined

详见 references/image.md —— 需 X-DashScope-Async 头 + prompt 含触发词

See references/image.md — requires X-DashScope-Async header + prompt containing trigger words


> `--model` 必须用 `deploy <模态> create` 响应中的 `deployed_model`，**不是**微调输出名。音频 TTS 的 `--voice` 固定为 `default`。图像生成需异步调用且 prompt 包含触发词。详见对应模态参考文件。

向用户交付时给出：
- **部署实例 id**（`deployed_model`）——调用用它，不是微调输出名
- 一条可直接运行的推理示例命令
- 常用运维命令：`bl deploy get --deployed-model <id>` 查状态；`bl deploy delete --deployed-model <id>` 删除部署；`bl finetune list` 查历史任务。


> `--model` must use `deployed_model` from the response of `deploy <modal> create`, **not** the fine-tuned output name. `--voice` for audio TTS is fixed to `default`. Image generation requires asynchronous calling and prompts containing trigger words. See corresponding modality reference files.

When delivering to users, provide:
- **Deployment instance ID** (`deployed_model`) — use this for inference, not the fine-tuned output name
- One directly executable inference example command
- Common O&M commands: `bl deploy get --deployed-model <id>` to check status; `bl deploy delete --deployed-model <id>` to delete deployment; `bl finetune list` to check historical tasks.

收尾提示

Closing Tips

闲置计费与删除：
```
lora
```
闲置一般不计费；
```
mu
```
/
```
ptu
```
闲置也计费，不用要清理，且
```
bl deploy delete
```
有状态约束（CLI 无 stop 命令）。细则见
```
references/deploy.md
```
。
复用数据集：多次训练同一数据时，先
```
bl dataset upload
```
拿 file-id，再用
```
--datasets <file-id>
```
避免重复上传。
效果不好：优先加数据（量与质量），其次调
```
n-epochs
```
/
```
learning-rate
```
，最后才考虑全参
```
sft
```
。小数据集（<100 条）效果上限有限，要管理预期。

Idle Charges and Deletion:
```
lora
```
generally has no charge when idle;
```
mu
```
/
```
ptu
```
are charged even when idle, clean them up when not in use, and
```
bl deploy delete
```
has status constraints (CLI has no stop command). Details see
```
references/deploy.md
```
.
Reuse Datasets: When training with the same data multiple times, first run
```
bl dataset upload
```
to get a file-id, then use
```
--datasets <file-id>
```
to avoid repeated uploads.
Poor Performance: Prioritize adding data (quantity and quality), then adjust
```
n-epochs
```
/
```
learning-rate
```
, and finally consider full-parameter
```
sft
```
. Small datasets (<100 entries) have limited performance upper limits, manage expectations accordingly.

bailian-train-deploy

Original

Translation

百炼模型训练→部署闭环 (
`bl`
)

Bailian Model Training → Deployment Loop (
`bl`
)

写操作护栏（创建前必读）

Write Operation Guardrails (Read Before Creation)

反触发表（不归本 skill 的意图，附完整命令）

Anti-Trigger List (Intents Not Covered by This Skill, with Complete Commands)

反幻觉清单

Anti-Hallucination Checklist

前置检查（动作流起点）

Pre-checks (Start of Action Flow)

第 1 步：准备数据集

Step 1: Prepare Dataset

第 2 步：创建微调任务

Step 2: Create Fine-tuning Task

第 3 步：等待训练完成（异步）

Step 3: Wait for Training Completion (Asynchronous)

第 4 步：导出最佳模型（通常可跳过）

Step 4: Export Best Model (Usually Skippable)

第 5 步：创建部署

Step 5: Create Deployment

第 6 步：等待部署就绪（异步）

Step 6: Wait for Deployment Readiness (Asynchronous)

第 7 步：调用与交付

Step 7: Inference and Delivery

详见 references/image.md —— 需 X-DashScope-Async 头 + prompt 含触发词

See references/image.md — requires X-DashScope-Async header + prompt containing trigger words

收尾提示

Closing Tips

bailian-train-deploy

Original

Translation

百炼模型训练→部署闭环 (bl)

Bailian Model Training → Deployment Loop (bl)

写操作护栏（创建前必读）

Write Operation Guardrails (Read Before Creation)

反触发表（不归本 skill 的意图，附完整命令）

Anti-Trigger List (Intents Not Covered by This Skill, with Complete Commands)

反幻觉清单

Anti-Hallucination Checklist

前置检查（动作流起点）

Pre-checks (Start of Action Flow)

第 1 步：准备数据集

Step 1: Prepare Dataset

第 2 步：创建微调任务

Step 2: Create Fine-tuning Task

第 3 步：等待训练完成（异步）

Step 3: Wait for Training Completion (Asynchronous)

第 4 步：导出最佳模型（通常可跳过）

Step 4: Export Best Model (Usually Skippable)

第 5 步：创建部署

Step 5: Create Deployment

第 6 步：等待部署就绪（异步）

Step 6: Wait for Deployment Readiness (Asynchronous)

第 7 步：调用与交付

Step 7: Inference and Delivery

详见 references/image.md —— 需 X-DashScope-Async 头 + prompt 含触发词

See references/image.md — requires X-DashScope-Async header + prompt containing trigger words

收尾提示

Closing Tips

百炼模型训练→部署闭环 (
`bl`
)

Bailian Model Training → Deployment Loop (
`bl`
)