feat: update model deploy

Author: gujieye

packages/cli/src/commands.ts

@@ -52,7 +52,9 @@ import {
   datasetGet,
   datasetDelete,
   datasetValidate,
-  finetuneCreate,
+  finetuneTextCreate,
+  finetuneAudioCreate,
+  finetuneImageCreate,
   finetuneList,
   finetuneGet,
   finetuneCancel,
@@ -62,7 +64,9 @@ import {
   finetuneExport,
   finetuneWatch,
   finetuneCapability,
-  deployCreate,
+  deployTextCreate,
+  deployAudioCreate,
+  deployImageCreate,
   deployList,
   deployGet,
   deployModels,
@@ -133,7 +137,9 @@ export const commands: Record<string, AnyCommand> = {
   "dataset get": datasetGet,
   "dataset delete": datasetDelete,
   "dataset validate": datasetValidate,
-  "finetune create": finetuneCreate,
+  "finetune text create": finetuneTextCreate,
+  "finetune audio create": finetuneAudioCreate,
+  "finetune image create": finetuneImageCreate,
   "finetune list": finetuneList,
   "finetune get": finetuneGet,
   "finetune cancel": finetuneCancel,
@@ -143,7 +149,9 @@ export const commands: Record<string, AnyCommand> = {
   "finetune export": finetuneExport,
   "finetune watch": finetuneWatch,
   "finetune capability": finetuneCapability,
-  "deploy create": deployCreate,
+  "deploy text create": deployTextCreate,
+  "deploy audio create": deployAudioCreate,
+  "deploy image create": deployImageCreate,
   "deploy list": deployList,
   "deploy get": deployGet,
   "deploy models": deployModels,

packages/cli/tests/e2e/deploy.e2e.test.ts

@@ -22,14 +22,15 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: deploy (offline)", () => {
   });
 
   test("deploy create --help 正常退出并展示必填项", async () => {
-    const { stderr, exitCode } = await runCli(["deploy", "create", "--help"]);
+    const { stderr, exitCode } = await runCli(["deploy", "text", "create", "--help"]);
     expect(exitCode, stderr).toBe(0);
     expect(stderr).toMatch(/--model|--name/i);
   });
 
   test("deploy create --dry-run 构造 lora 部署请求体", async () => {
     const { stdout, stderr, exitCode } = await runCli([
       "deploy",
+      "text",
       "create",
       "--model",
       "qwen-plus-2025-12-01",
@@ -59,6 +60,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: deploy (offline)", () => {
   test("deploy create --plan mu --deploy-spec --dry-run 透传 deploy_spec", async () => {
     const { stdout, stderr, exitCode } = await runCli([
       "deploy",
+      "text",
       "create",
       "--model",
       "qwen3-8b",
@@ -85,6 +87,28 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: deploy (offline)", () => {
     expect(data.body.capacity).toBe(2);
   });
 
+  test("deploy audio create --dry-run 构造 lora 部署请求体", async () => {
+    // deploy create does not inspect modality; the split only changes the path.
+    // The audio subcommand shares the full flag surface and run logic.
+    const { stdout, stderr, exitCode } = await runCli([
+      "deploy",
+      "audio",
+      "create",
+      "--model",
+      "my-cosyvoice-ft",
+      "--name",
+      "my-tts",
+      "--dry-run",
+      "--output",
+      "json",
+    ]);
+    expect(exitCode, stderr).toBe(0);
+    const data = parseStdoutJson<{ action: string; body: { plan: string; name: string } }>(stdout);
+    expect(data.action).toBe("deploy.create");
+    expect(data.body.plan).toBe("lora");
+    expect(data.body.name).toBe("my-tts");
+  });
+
   test("deploy scale --dry-run 转发 capacity", async () => {
     const { stdout, stderr, exitCode } = await runCli([
       "deploy",

packages/cli/tests/e2e/finetune.e2e.test.ts

@@ -23,14 +23,15 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
   });
 
   test("finetune create --help 正常退出并展示必填项", async () => {
-    const { stderr, exitCode } = await runCli(["finetune", "create", "--help"]);
+    const { stderr, exitCode } = await runCli(["finetune", "text", "create", "--help"]);
     expect(exitCode, stderr).toBe(0);
     expect(stderr).toMatch(/--model|--datasets/i);
   });
 
   test("finetune create --dry-run 构造 SFT 默认请求体", async () => {
     const { stdout, stderr, exitCode } = await runCli([
       "finetune",
+      "text",
       "create",
       "--model",
       "qwen3-8b",
@@ -64,6 +65,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
   test("finetune create --dry-run 转发训练类型与超参", async () => {
     const { stdout, stderr, exitCode } = await runCli([
       "finetune",
+      "text",
       "create",
       "--model",
       "qwen3-8b",
@@ -125,6 +127,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
     async (cliType, serverType) => {
       const { stdout, stderr, exitCode } = await runCli([
         "finetune",
+        "text",
         "create",
         "--model",
         "qwen3-8b",
@@ -146,6 +149,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
   test("finetune create --training-type 拒绝不支持的训练类型值", async () => {
     const { stdout, stderr, exitCode } = await runCli([
       "finetune",
+      "text",
       "create",
       "--model",
       "qwen3-8b",
@@ -164,6 +168,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
     const localPath = join(cliPackageRoot, "tests", "e2e", ".dataset-valid.jsonl");
     const { stdout, stderr, exitCode } = await runCli([
       "finetune",
+      "text",
       "create",
       "--model",
       "qwen3-8b",
@@ -194,6 +199,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
   test("finetune create --datasets 为空时拒绝", async () => {
     const { stdout, stderr, exitCode } = await runCli([
       "finetune",
+      "text",
       "create",
       "--model",
       "qwen3-8b",
@@ -214,6 +220,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
     const localPath = join(cliPackageRoot, "tests", "e2e", ".dataset-valid.jsonl");
     const { stdout, stderr, exitCode } = await runCli([
       "finetune",
+      "text",
       "create",
       "--model",
       "qwen3-8b",
@@ -235,6 +242,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
     const localPath = join(cliPackageRoot, "tests", "e2e", ".dataset-valid.jsonl");
     const { stdout, stderr, exitCode } = await runCli([
       "finetune",
+      "text",
       "create",
       "--model",
       "qwen3-8b",
@@ -276,6 +284,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
   test("finetune create --dry-run 解析多 datasets 中的空白", async () => {
     const { stdout, stderr, exitCode } = await runCli([
       "finetune",
+      "text",
       "create",
       "--model",
       "qwen3-8b",
@@ -291,6 +300,60 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (offline)", () => {
     }>(stdout);
     expect(data.body.training_file_ids).toEqual(["file-a", "file-b"]);
   });
+
+  test("finetune audio create --dry-run 用 sft-lora 默认 + audio 超参", async () => {
+    // Audio has no --training-type flag; the command fixes modality=audio and
+    // defaults to sft-lora (efficient_sft) with the fixed CosyVoice hyper-params.
+    const { stdout, stderr, exitCode } = await runCli([
+      "finetune",
+      "audio",
+      "create",
+      "--model",
+      "cosyvoice-v3-flash",
+      "--datasets",
+      "file-audio",
+      "--dry-run",
+      "--output",
+      "json",
+    ]);
+    expect(exitCode, stderr).toBe(0);
+    const data = parseStdoutJson<{
+      action: string;
+      body: { training_type: string; hyper_parameters: Record<string, unknown> };
+    }>(stdout);
+    expect(data.action).toBe("finetune.create");
+    expect(data.body.training_type).toBe("efficient_sft");
+    // Audio TTS defaults are fixed (not the text n_epochs/batch_size surface).
+    expect(data.body.hyper_parameters.lm_max_epoch).toBeDefined();
+  });
+
+  test("finetune audio create --help 不暴露文本超参 flag", async () => {
+    // Audio models don't consume --training-type / --n-epochs / --batch-size /
+    // --learning-rate / --max-length, so those flags are not offered.
+    const { stderr, exitCode } = await runCli(["finetune", "audio", "create", "--help"]);
+    expect(exitCode, stderr).toBe(0);
+    expect(stderr).toMatch(/--model|--datasets/i);
+    expect(stderr).not.toMatch(/--training-type|--n-epochs|--batch-size|--max-length/);
+  });
+
+  test("finetune image create --dry-run 用 sft-lora 默认", async () => {
+    const { stdout, stderr, exitCode } = await runCli([
+      "finetune",
+      "image",
+      "create",
+      "--model",
+      "wan2.7-image-pro",
+      "--datasets",
+      "file-image",
+      "--dry-run",
+      "--output",
+      "json",
+    ]);
+    expect(exitCode, stderr).toBe(0);
+    const data = parseStdoutJson<{ action: string; body: { training_type: string } }>(stdout);
+    expect(data.action).toBe("finetune.create");
+    expect(data.body.training_type).toBe("efficient_sft");
+  });
 });
 
 describe.skipIf(!isDashScopeE2EReady())("e2e: finetune (DashScope)", () => {