refactor: 调优创建提取表单模型

将默认参数、命令构建、payload 构造逻辑抽离为 fineTuneFormModel.ts，新增 FineTuneStartPayload 类型约束启动训练接口，FineTuneCreateView 瘦身为视图层，列表微调，回归脚本适配。
2026-07-13 15:28:17 +08:00
parent bff07b7a31
commit 735a8a71f5
6 changed files with 262 additions and 170 deletions
--- a/frontend/scripts/regression-fine-tune-create-ui.mjs
+++ b/frontend/scripts/regression-fine-tune-create-ui.mjs
@@ -1,10 +1,12 @@
 import { readFileSync } from 'node:fs'
 import { resolve } from 'node:path'
 import { fileURLToPath } from 'node:url'
+import ts from 'typescript'

 const root = resolve(fileURLToPath(new URL('..', import.meta.url)))
 const source = readFileSync(resolve(root, 'src/views/fine-tune/FineTuneCreateView.vue'), 'utf8')
 const modelDialogSource = readFileSync(resolve(root, 'src/components/ModelSelectDialog.vue'), 'utf8')
+const formModelSource = readFileSync(resolve(root, 'src/views/fine-tune/fineTuneFormModel.ts'), 'utf8')

 function assert(condition, message) {
  if (!condition) {
@@ -24,11 +26,32 @@ assert(modelDialogSource.includes('width="860px"'), 'Model dialog should use a c
 assert(modelDialogSource.includes('model-series-list'), 'Model dialog should include a model series list column')
 assert(modelDialogSource.includes('model-version-list'), 'Model dialog should include a snapshot/version list column')
 assert(modelDialogSource.includes('handleConfirm'), 'Model dialog should confirm the selected model before updating the form')
-assert(source.includes("auto_merge: false"), 'Auto merge should default to disabled')
+assert(formModelSource.includes('auto_merge: false'), 'Auto merge should default to disabled')
 assert(source.includes('v-if="form.train_type === \'SFT\'"'), 'Merge model settings should only be visible for SFT')
 assert(source.includes('content-position="left">合并模型'), 'SFT form should include a merge model section below data configuration')
 assert(source.includes('v-model="form.auto_merge"'), 'Merge model section should provide an auto merge selector')
 assert(source.includes('label="自动合并权重并保存"'), 'Auto merge selector should use a clear visible label')
-assert((source.match(/auto_merge: form\.train_type === 'SFT' && form\.auto_merge/g) || []).length === 2, 'Auto merge should be sent for SFT when creating and starting the task')
+assert(formModelSource.includes("auto_merge: form.train_type === 'SFT' && form.auto_merge"), 'Auto merge should be normalized by the shared payload builder')
+assert(source.includes('const payload = buildFineTunePayload(form, selectedGpus.value)'), 'Create and start should share one normalized payload')
+assert(source.includes('startFineTune({ ...payload, task_id: taskId })'), 'Start request should reuse the normalized payload')
+assert(source.includes('Object.assign(form, DEFAULT_TRAINING_PARAMS)'), 'Reset should reuse the canonical defaults')
+assert(!source.includes('const taskData = {'), 'The duplicated create payload should be removed')
+assert(!source.includes('const createRes: any'), 'Create response should use the API return type')
+assert(!source.includes('(check as any).exists'), 'Name-check response should use its API return type')
+assert(source.includes('任务名校验失败'), 'Name-check failures should be visible and block submission')
+
+const runnableSource = ts.transpileModule(
+  formModelSource.replace("import type { FineTuneStartPayload, FineTuneTask } from '@/types'", ''),
+  { compilerOptions: { module: ts.ModuleKind.ESNext, target: ts.ScriptTarget.ES2022 } },
+).outputText
+const model = await import(`data:text/javascript;base64,${Buffer.from(runnableSource).toString('base64')}`)
+const defaults = model.createDefaultFineTuneForm()
+const customized = { ...defaults, train_type: 'DPO', train_method: 'full', auto_merge: true, quantization_bit: 4 }
+const payload = model.buildFineTunePayload(customized, [0, 2])
+assert(payload.auto_merge === false, 'Non-SFT tasks must never enable auto merge')
+assert(payload.quantization_bit === 0, 'Full fine-tuning must never send QLoRA quantization')
+assert(payload.gpus.join(',') === '0,2', 'Selected GPUs should be preserved in the shared payload')
+const resetDefaults = { ...model.DEFAULT_TRAINING_PARAMS }
+assert(resetDefaults.lora_alpha === defaults.lora_alpha, 'Reset and initial defaults must share LoRA values')

 console.log('fine-tune create UI regression checks passed')
--- a/frontend/src/api/modules/fineTune.ts
+++ b/frontend/src/api/modules/fineTune.ts
@@ -1,5 +1,5 @@
 import { get, post, put, del } from '../request'
-import type { FineTuneTask, TrainingProgress } from '@/types'
+import type { FineTuneStartPayload, FineTuneTask, TrainingProgress } from '@/types'

 /** 训练任务列表 */
 export const getFineTuneList = () => get<FineTuneTask[]>('/fine-tune')
@@ -16,7 +16,7 @@ export const createFineTune = (data: Partial<FineTuneTask>) =>
  post<{ id: string | number }>('/fine-tune', data)

 /** 启动训练（第二步） */
-export const startFineTune = (data: any) => post('/fine-tune/start', data)
+export const startFineTune = (data: FineTuneStartPayload) => post('/fine-tune/start', data)

 /** 更新训练任务 */
 export const updateFineTune = (id: string | number, data: Partial<FineTuneTask>) =>
--- a/frontend/src/types/index.ts
+++ b/frontend/src/types/index.ts
@@ -139,6 +139,37 @@ export interface FineTuneTask {
  create_time?: string
 }

+export type FineTuneStartPayload = Omit<
+  FineTuneTask,
+  'id' | 'status' | 'progress' | 'process_id' | 'train_duration' | 'create_time'
+> & {
+  task_id: string | number
+  description: string
+  template: string
+  train_method: TrainMethodType
+  train_dataset_id: string | number
+  auto_merge: boolean
+  output_model_name: string
+  gpus: number[]
+  batch_size: number
+  learning_rate: number
+  n_epochs: number
+  save_steps: number
+  lr_scheduler_type: string
+  max_length: number
+  warmup_ratio: number
+  weight_decay: number
+  lora_alpha: number
+  lora_dropout: number
+  lora_rank: number
+  quantization_bit: number
+  export_quantized: boolean
+  quant_method: string
+  quant_bits: number
+  quant_group_size: number
+  export_format: string
+}
+
 export interface TrainingProgress {
  status?: string
  progress?: number
--- a/frontend/src/views/fine-tune/FineTuneCreateView.vue
+++ b/frontend/src/views/fine-tune/FineTuneCreateView.vue
@@ -14,6 +14,14 @@ import { getModelList } from '@/api/modules/model'
 import { getDatasetList } from '@/api/modules/dataset'
 import { getSystemInfo } from '@/api/modules/system'
 import { TEMPLATE_GROUPS, LR_SCHEDULER_OPTIONS, QUANTIZATION_BIT_OPTIONS, QUANT_METHOD_OPTIONS, GGUF_FORMAT_OPTIONS } from '@/constants'
+import {
+  DEFAULT_TRAINING_PARAMS,
+  buildFineTuneCommand,
+  buildFineTunePayload,
+  createDefaultFineTuneForm,
+  toCreateFineTunePayload,
+} from './fineTuneFormModel'
+import type { FineTuneFormModel } from './fineTuneFormModel'
 import type { ModelItem, DatasetItem, GpuInfo } from '@/types'

 const router = useRouter()
@@ -26,36 +34,7 @@ const gpus = ref<GpuInfo[]>([])
 const selectedGpus = ref<number[]>([])
 const modelDialogVisible = ref(false)

-const form = reactive({
-  name: '',
-  description: '',
-  train_type: 'SFT' as 'SFT' | 'DPO' | 'CPT',
-  base_model: '' as string | number,
-  template: 'qwen',
-  train_method: 'lora' as 'lora' | 'full',
-  train_dataset_id: '' as string | number,
-  auto_merge: false,
-  // 训练参数
-  batch_size: 1,
-  learning_rate: 0.0001,
-  n_epochs: 1,
-  save_steps: 100,
-  lr_scheduler_type: 'cosine',
-  max_length: 512,
-  warmup_ratio: 0.05,
-  weight_decay: 0.01,
-  // LoRA 参数
-  lora_alpha: 16, // 修复原项目 lora_alpha 默认值不一致 bug
-  lora_dropout: 0.1,
-  lora_rank: 8,
-  // 量化参数
-  quantization_bit: 0, // 训练时量化（QLoRA）：0=不量化
-  export_quantized: false, // 训练后是否导出量化模型
-  quant_method: 'bnb', // 导出量化方法
-  quant_bits: 4, // 导出量化位数
-  quant_group_size: 128, // 分组大小（GPTQ/AWQ）
-  export_format: 'Q4_K_M', // GGUF 导出格式
-})
+const form = reactive(createDefaultFineTuneForm())

 const rules: FormRules = {
  name: [
@@ -78,45 +57,8 @@ const selectedModel = computed(() => models.value.find((model) => model.id === f

 const modelDialogTitle = computed(() => selectedModel.value?.name || '')

-/** 训练命令实时预览 */
-const commandPreview = computed(() => {
-  const gpuIds = selectedGpus.value.length ? selectedGpus.value.join(',') : '0'
-  let cmd = `CUDA_VISIBLE_DEVICES=${gpuIds} llamafactory-cli train \\\n`
-  cmd += `  --stage ${form.train_type === 'DPO' ? 'dpo' : form.train_type === 'CPT' ? 'cpt' : 'sft'} \\\n`
-  cmd += `  --do_train \\\n`
-  cmd += `  --model_name_or_path <base_model_path> \\\n`
-  cmd += `  --dataset <dataset> \\\n`
-  cmd += `  --template ${form.template} \\\n`
-  cmd += `  --finetuning_type ${form.train_method} \\\n`
-  cmd += `  --output_dir ./saves/${form.name || 'output'} \\\n`
-  cmd += `  --per_device_train_batch_size ${form.batch_size} \\\n`
-  cmd += `  --learning_rate ${form.learning_rate} \\\n`
-  cmd += `  --num_train_epochs ${form.n_epochs} \\\n`
-  cmd += `  --save_steps ${form.save_steps} \\\n`
-  cmd += `  --lr_scheduler_type ${form.lr_scheduler_type} \\\n`
-  cmd += `  --cutoff_len ${form.max_length} \\\n`
-  cmd += `  --warmup_ratio ${form.warmup_ratio} \\\n`
-  cmd += `  --weight_decay ${form.weight_decay}`
-  if (showLoraParams.value) {
-    cmd += ` \\\n  --lora_alpha ${form.lora_alpha}`
-    cmd += ` \\\n  --lora_dropout ${form.lora_dropout}`
-    cmd += ` \\\n  --lora_rank ${form.lora_rank}`
-  }
-  if (showLoraParams.value && form.quantization_bit) {
-    cmd += ` \\\n  --quantization_bit ${form.quantization_bit}`
-  }
-  if (form.export_quantized) {
-    const method = form.quant_method
-    const bits = form.quant_bits
-    cmd += ` \\\n  # 训练后导出量化模型：${method} ${bits}bit`
-    if (method === 'gguf') {
-      cmd += ` \\\n  # export_format=${form.export_format}`
-    } else if (method === 'gptq' || method === 'awq') {
-      cmd += ` \\\n  # group_size=${form.quant_group_size}`
-    }
-  }
-  return cmd
-})
+/** 训练命令与提交载荷共用同一份表单模型。 */
+const commandPreview = computed(() => buildFineTuneCommand(form, selectedGpus.value))

 /** GPU 多选切换 */
 function toggleGpu(index: number) {
@@ -140,31 +82,26 @@ function handleModelConfirm(modelId: string | number) {
 }

 function resetParams() {
-  Object.assign(form, {
-    batch_size: 1,
-    learning_rate: 0.0001,
-    n_epochs: 1,
-    save_steps: 100,
-    lr_scheduler_type: 'cosine',
-    max_length: 512,
-    warmup_ratio: 0.05,
-    weight_decay: 0.01,
-    lora_alpha: 16,
-    lora_dropout: 0.1,
-    lora_rank: 8,
-    quantization_bit: 0,
-    export_quantized: false,
-    quant_method: 'bnb',
-    quant_bits: 4,
-    quant_group_size: 128,
-    export_format: 'Q4_K_M',
-  })
+  Object.assign(form, DEFAULT_TRAINING_PARAMS)
 }

 const isParamsExpanded = ref(false)

+interface ParameterDefinition {
+  key: keyof FineTuneFormModel
+  name: string
+  desc: string
+  hint: string
+  type: 'number' | 'select'
+  min?: number
+  max?: number
+  step?: number
+  precision?: number
+  options?: Array<{ label: string; value: string | number }>
+}
+
 const allParams = computed(() => {
-  const params = [
+  const params: ParameterDefinition[] = [
    { key: 'batch_size', name: 'batch_size', desc: '批次大小，代表模型训练过程中，模型更新一次参数所需要的数据样本数。', hint: '[1, 64], step:1', type: 'number', min: 1, max: 64, step: 1 },
    { key: 'learning_rate', name: 'learning_rate', desc: '学习率，代表每次更新数据的增量参数权重比例。', hint: '[0.000001, 1]', type: 'number', min: 0.000001, max: 1, step: 0.00001, precision: 6 },
    { key: 'n_epochs', name: 'n_epochs', desc: '循环次数，代表模型训练过程中模型学习数据集的次数，可理解为看几遍数据，一般建议的范围是 1-3 遍即可，可依据需求进行调整', hint: '[1, 100], step:1', type: 'number', min: 1, max: 100, step: 1 },
@@ -178,7 +115,7 @@ const allParams = computed(() => {
    params.push(
      { key: 'lora_alpha', name: 'lora_alpha', desc: 'LoRA 缩放系数。', hint: '16/32/64/128', type: 'select', options: [{ label: '16', value: 16 }, { label: '32', value: 32 }, { label: '64', value: 64 }, { label: '128', value: 128 }] },
      { key: 'lora_rank', name: 'lora_rank', desc: 'LoRA 秩大小，控制低秩矩阵的维度。', hint: '8/16/32/64', type: 'select', options: [{ label: '8', value: 8 }, { label: '16', value: 16 }, { label: '32', value: 32 }, { label: '64', value: 64 }] },
-      { key: 'lora_dropout', name: 'lora_dropout', desc: 'LoRA 层的 dropout 比例。', hint: '[0, 1]', type: 'number', min: 0, max: 1, step: 0.05, precision: 2 }
+      { key: 'lora_dropout', name: 'lora_dropout', desc: 'LoRA 层的 dropout 比例。', hint: '[0, 1]', type: 'number', min: 0, max: 1, step: 0.05, precision: 2 },
    )
  }
  return params
@@ -188,6 +125,15 @@ const visibleParams = computed(() => {
  return isParamsExpanded.value ? allParams.value : allParams.value.slice(0, 3)
 })

+function parameterValue(key: keyof FineTuneFormModel) {
+  const value = form[key]
+  return typeof value === 'boolean' ? Number(value) : value
+}
+
+function updateParameterValue(key: keyof FineTuneFormModel, value: string | number | undefined) {
+  if (value !== undefined) Reflect.set(form, key, value)
+}
+
 async function loadModels() {
  try {
    models.value = (await getModelList()) || []
@@ -225,88 +171,32 @@ async function handleSubmit() {
    }
    submitting.value = true
    try {
-      // 任务名查重
-      const check = await checkFineTuneName(form.name).catch(() => ({ exists: false }))
-      if ((check as any).exists) {
+      let check: { exists: boolean }
+      try {
+        check = await checkFineTuneName(form.name)
+      } catch {
+        ElMessage.error('任务名校验失败，请稍后重试')
+        return
+      }
+      if (check.exists) {
        ElMessage.error('任务名称已存在，请更换')
-        submitting.value = false
        return
      }

-      // 第一步：创建任务记录
-      const taskData = {
-        name: form.name,
-        description: form.description,
-        base_model: form.base_model,
-        template: form.template,
-        train_type: form.train_type,
-        train_method: form.train_method,
-        gpus: selectedGpus.value,
-        train_dataset_id: form.train_dataset_id,
-        auto_merge: form.train_type === 'SFT' && form.auto_merge,
-        output_model_name: form.name,
-        batch_size: form.batch_size,
-        learning_rate: form.learning_rate,
-        n_epochs: form.n_epochs,
-        save_steps: form.save_steps,
-        lr_scheduler_type: form.lr_scheduler_type,
-        max_length: form.max_length,
-        warmup_ratio: form.warmup_ratio,
-        weight_decay: form.weight_decay,
-        lora_alpha: form.lora_alpha,
-        lora_dropout: form.lora_dropout,
-        lora_rank: form.lora_rank,
-        quantization_bit: form.train_method === 'lora' ? form.quantization_bit : 0,
-        export_quantized: form.export_quantized,
-        quant_method: form.export_quantized ? form.quant_method : '',
-        quant_bits: form.export_quantized ? form.quant_bits : 0,
-        quant_group_size: form.export_quantized ? form.quant_group_size : 0,
-        export_format: form.export_quantized && form.quant_method === 'gguf' ? form.export_format : '',
-        status: 'pending',
-        progress: 0,
-      }
-      const createRes: any = await createFineTune(taskData)
-      const taskId = createRes?.id || createRes
+      const payload = buildFineTunePayload(form, selectedGpus.value)
+      const createRes = await createFineTune(toCreateFineTunePayload(payload))
+      const taskId = createRes.id

-      // 第二步：启动训练
      try {
-        await startFineTune({
-          task_id: taskId,
-          name: form.name,
-          base_model: form.base_model,
-          template: form.template,
-          train_type: form.train_type,
-          train_method: form.train_method,
-          train_dataset_id: form.train_dataset_id,
-          auto_merge: form.train_type === 'SFT' && form.auto_merge,
-          output_model_name: form.name,
-          gpus: selectedGpus.value,
-          batch_size: form.batch_size,
-          learning_rate: form.learning_rate,
-          n_epochs: form.n_epochs,
-          save_steps: form.save_steps,
-          lr_scheduler_type: form.lr_scheduler_type,
-          max_length: form.max_length,
-          warmup_ratio: form.warmup_ratio,
-          weight_decay: form.weight_decay,
-          lora_alpha: form.lora_alpha,
-          lora_dropout: form.lora_dropout,
-          lora_rank: form.lora_rank,
-          quantization_bit: form.train_method === 'lora' ? form.quantization_bit : 0,
-          export_quantized: form.export_quantized,
-          quant_method: form.export_quantized ? form.quant_method : '',
-          quant_bits: form.export_quantized ? form.quant_bits : 0,
-          quant_group_size: form.export_quantized ? form.quant_group_size : 0,
-          export_format: form.export_quantized && form.quant_method === 'gguf' ? form.export_format : '',
-        })
+        await startFineTune({ ...payload, task_id: taskId })
        ElMessage.success('训练任务已创建并启动')
-      } catch (e) {
-        // 启动失败，回写状态
+      } catch {
        await updateFineTune(taskId, { status: 'failed' })
+        ElMessage.error('任务已创建，但训练启动失败')
      }
      router.push('/fine-tune')
    } catch {
-      // ignore
+      ElMessage.error('训练任务创建失败，请稍后重试')
    } finally {
      submitting.value = false
    }
@@ -427,15 +317,17 @@ onMounted(() => {
                <div class="param-col config">
                  <el-input-number 
                    v-if="param.type === 'number'" 
-                    v-model="(form as any)[param.key]" 
+                    :model-value="Number(parameterValue(param.key))"
                    :min="param.min" :max="param.max" :step="param.step" :precision="param.precision" 
                    controls-position="right"
                    style="width: 200px"
+                    @update:model-value="updateParameterValue(param.key, $event)"
                  />
                  <el-select 
                    v-else-if="param.type === 'select'" 
-                    v-model="(form as any)[param.key]" 
+                    :model-value="parameterValue(param.key)"
                    style="width: 200px"
+                    @update:model-value="updateParameterValue(param.key, $event)"
                  >
                    <el-option v-for="o in param.options" :key="o.value" :label="o.label" :value="o.value" />
                  </el-select>
--- a/frontend/src/views/fine-tune/FineTuneListView.vue
+++ b/frontend/src/views/fine-tune/FineTuneListView.vue
@@ -37,7 +37,7 @@ const filteredList = computed(() => {
    if (filters.value.trainType.length && !filters.value.trainType.includes(row.train_type)) {
      return false
    }
-    if (filters.value.trainMethod.length && !filters.value.trainMethod.includes(row.train_method)) {
+    if (filters.value.trainMethod.length && (!row.train_method || !filters.value.trainMethod.includes(row.train_method))) {
      return false
    }
    return true
--- a/frontend/src/views/fine-tune/fineTuneFormModel.ts
+++ b/frontend/src/views/fine-tune/fineTuneFormModel.ts
@@ -0,0 +1,146 @@
+import type { FineTuneStartPayload, FineTuneTask } from '@/types'
+
+export type FineTuneFormModel = {
+  name: string
+  description: string
+  train_type: 'SFT' | 'DPO' | 'CPT'
+  base_model: string | number
+  template: string
+  train_method: 'lora' | 'full'
+  train_dataset_id: string | number
+  auto_merge: boolean
+  batch_size: number
+  learning_rate: number
+  n_epochs: number
+  save_steps: number
+  lr_scheduler_type: string
+  max_length: number
+  warmup_ratio: number
+  weight_decay: number
+  lora_alpha: number
+  lora_dropout: number
+  lora_rank: number
+  quantization_bit: number
+  export_quantized: boolean
+  quant_method: string
+  quant_bits: number
+  quant_group_size: number
+  export_format: string
+}
+
+export const DEFAULT_TRAINING_PARAMS = {
+  batch_size: 1,
+  learning_rate: 0.0001,
+  n_epochs: 1,
+  save_steps: 100,
+  lr_scheduler_type: 'cosine',
+  max_length: 512,
+  warmup_ratio: 0.05,
+  weight_decay: 0.01,
+  lora_alpha: 16,
+  lora_dropout: 0.1,
+  lora_rank: 8,
+  quantization_bit: 0,
+  export_quantized: false,
+  quant_method: 'bnb',
+  quant_bits: 4,
+  quant_group_size: 128,
+  export_format: 'Q4_K_M',
+} as const
+
+export function createDefaultFineTuneForm(): FineTuneFormModel {
+  return {
+    name: '',
+    description: '',
+    train_type: 'SFT',
+    base_model: '',
+    template: 'qwen',
+    train_method: 'lora',
+    train_dataset_id: '',
+    auto_merge: false,
+    ...DEFAULT_TRAINING_PARAMS,
+  }
+}
+
+export function buildFineTunePayload(
+  form: FineTuneFormModel,
+  gpus: number[],
+): Omit<FineTuneStartPayload, 'task_id'> {
+  return {
+    name: form.name,
+    description: form.description,
+    base_model: form.base_model,
+    template: form.template,
+    train_type: form.train_type,
+    train_method: form.train_method,
+    gpus: [...gpus],
+    train_dataset_id: form.train_dataset_id,
+    auto_merge: form.train_type === 'SFT' && form.auto_merge,
+    output_model_name: form.name,
+    batch_size: form.batch_size,
+    learning_rate: form.learning_rate,
+    n_epochs: form.n_epochs,
+    save_steps: form.save_steps,
+    lr_scheduler_type: form.lr_scheduler_type,
+    max_length: form.max_length,
+    warmup_ratio: form.warmup_ratio,
+    weight_decay: form.weight_decay,
+    lora_alpha: form.lora_alpha,
+    lora_dropout: form.lora_dropout,
+    lora_rank: form.lora_rank,
+    quantization_bit: form.train_method === 'lora' ? form.quantization_bit : 0,
+    export_quantized: form.export_quantized,
+    quant_method: form.export_quantized ? form.quant_method : '',
+    quant_bits: form.export_quantized ? form.quant_bits : 0,
+    quant_group_size: form.export_quantized ? form.quant_group_size : 0,
+    export_format: form.export_quantized && form.quant_method === 'gguf' ? form.export_format : '',
+  }
+}
+
+export function buildFineTuneCommand(form: FineTuneFormModel, gpus: number[]) {
+  const gpuIds = gpus.length ? gpus.join(',') : '0'
+  const stage = form.train_type === 'DPO' ? 'dpo' : form.train_type === 'CPT' ? 'cpt' : 'sft'
+  const lines = [
+    `CUDA_VISIBLE_DEVICES=${gpuIds} llamafactory-cli train`,
+    `  --stage ${stage}`,
+    '  --do_train',
+    '  --model_name_or_path <base_model_path>',
+    '  --dataset <dataset>',
+    `  --template ${form.template}`,
+    `  --finetuning_type ${form.train_method}`,
+    `  --output_dir ./saves/${form.name || 'output'}`,
+    `  --per_device_train_batch_size ${form.batch_size}`,
+    `  --learning_rate ${form.learning_rate}`,
+    `  --num_train_epochs ${form.n_epochs}`,
+    `  --save_steps ${form.save_steps}`,
+    `  --lr_scheduler_type ${form.lr_scheduler_type}`,
+    `  --cutoff_len ${form.max_length}`,
+    `  --warmup_ratio ${form.warmup_ratio}`,
+    `  --weight_decay ${form.weight_decay}`,
+  ]
+
+  if (form.train_method === 'lora') {
+    lines.push(
+      `  --lora_alpha ${form.lora_alpha}`,
+      `  --lora_dropout ${form.lora_dropout}`,
+      `  --lora_rank ${form.lora_rank}`,
+    )
+    if (form.quantization_bit) lines.push(`  --quantization_bit ${form.quantization_bit}`)
+  }
+
+  if (form.export_quantized) {
+    lines.push(`  # 训练后导出量化模型：${form.quant_method} ${form.quant_bits}bit`)
+    if (form.quant_method === 'gguf') lines.push(`  # export_format=${form.export_format}`)
+    if (form.quant_method === 'gptq' || form.quant_method === 'awq') {
+      lines.push(`  # group_size=${form.quant_group_size}`)
+    }
+  }
+
+  return lines.join(' \\\n')
+}
+
+export function toCreateFineTunePayload(
+  payload: Omit<FineTuneStartPayload, 'task_id'>,
+): Partial<FineTuneTask> {
+  return { ...payload, status: 'pending', progress: 0 }
+}