feat: 模型推理异步加载与对话链路修复，同步基线

模型推理全异步化改造： - 计算节点 InferenceSession 改为后台线程异步加载模型，load 立即返回，加载期间事件循环保持响应（/inference/status 与 /health 不阻塞） - 后端模型加载改为异步派发 + 轮询对账器（reconcile_inference_loads），任务状态由 starting 自动推进到 ready/error，解决多节点启动超时（timeout of 120000ms exceeded） - 推理删除/卸载改为任务感知 + 短超时，删除先删记录再 best-effort 卸载，不再被不可达节点阻塞；同节点新模型替换旧任务标记失效 - 流式对话透传 task_id/node_id 路由到真正加载模型的算力节点， useStreamChat 解析 SSE 错误帧以干净文案展示 - 对话历史按任务 id 本地持久化，退出重进可恢复；移除页脚提示文本 - 新增后端推理异步加载与计算节点异步状态机单元测试 Co-Authored-By: Claude <noreply@anthropic.com>
2026-08-04 16:59:34 +08:00
parent 250e060271
commit 0271942ba5
21 changed files with 1272 additions and 245 deletions
--- a/frontend/src/api/modules/compare.ts
+++ b/frontend/src/api/modules/compare.ts
@@ -1,6 +1,8 @@
 import { get, post, del } from '../request'
 import type { CompareTask, CompareModelRef } from '@/types'

+const INFERENCE_START_TIMEOUT_MS = 15 * 60 * 1000
+
 /** 推理/对比任务列表 */
 export const getCompareList = () => get<CompareTask[]>('/model-compare')

@@ -12,7 +14,7 @@ export const createCompare = (data: Partial<CompareTask>) =>
  post<{ id: string | number }>('/model-compare', data)

 /** 删除任务 */
-export const deleteCompare = (id: string | number) => del(`/model-compare/${id}`)
+export const deleteCompare = (id: string | number) => del(`/model-compare/${id}`, undefined, { timeout: 60_000 })

 /** 更新任务加载状态 */
 export const updateLoadStatus = (id: string | number, load_status: any) =>
@@ -34,7 +36,8 @@ export const stopModelByPid = (pid: number) =>
  post('/model-compare/stop-by-pid', { pid })

 /** 加载任务 */
-export const loadCompare = (id: string | number) => post(`/model-compare/${id}/load`)
+export const loadCompare = (id: string | number) =>
+  post(`/model-compare/${id}/load`, undefined, { timeout: INFERENCE_START_TIMEOUT_MS })

 /** 卸载任务 */
 export const unloadCompare = (id: string | number) => post(`/model-compare/${id}/unload`)
@@ -81,6 +84,10 @@ export const streamChatReal = (data: any): Promise<Response> => {
      temperature: data.temperature ?? 0.7,
      top_p: data.top_p ?? 0.95,
      max_tokens: data.max_tokens ?? 2048,
+      // 透传 task_id/node_id，让后端按 load_status 路由到真正加载了模型的算力节点，
+      // 避免在多节点时回退到“第一个在线节点”导致连接失败
+      task_id: data.task_id,
+      node_id: data.node_id,
    }),
  })
 }
@@ -94,8 +101,8 @@ export const batchChat = (data: any) => post('/model-chat/batch', data)
 /** 本地 transformers 模型对话 */
 export const localChat = (data: any) => post('/model-chat/local/chat', data)

-/** 预加载本地模型（模型加载耗时长，超时 5 分钟） */
-export const preloadLocalModel = (data: any) => post('/model-chat/local/preload', data, { timeout: 300000 })
+/** 预加载本地模型（模型加载耗时长，超时 15 分钟） */
+export const preloadLocalModel = (data: any) => post('/model-chat/local/preload', data, { timeout: INFERENCE_START_TIMEOUT_MS })

-/** 预加载已训练模型（超时 5 分钟） */
-export const preloadTrainedModel = (data: any) => post('/model-chat/trained/preload', data, { timeout: 300000 })
+/** 预加载已训练模型（超时 15 分钟） */
+export const preloadTrainedModel = (data: any) => post('/model-chat/trained/preload', data, { timeout: INFERENCE_START_TIMEOUT_MS })
--- a/frontend/src/api/modules/fineTune.ts
+++ b/frontend/src/api/modules/fineTune.ts
@@ -1,6 +1,16 @@
 import { get, post, put, del } from '../request'
 import type { FineTuneStartPayload, FineTuneTask, TrainingProgress, LogContent } from '@/types'

+export interface FineTuneMetricPoint {
+  step: number
+  epoch?: number | null
+  loss?: number | null
+  grad_norm?: number | null
+  learning_rate?: number | null
+  raw?: string
+  create_time?: string
+}
+
 export interface TrainingDiagnostic {
  level: string
  title: string
@@ -80,6 +90,10 @@ export const getFineTuneLogs = (
  params: { tail_lines?: number; offset?: number; limit?: number } = {},
 ) => get<LogContent & { job_id?: string; source?: string }>(`/fine-tune/${id}/logs`, params)

+/** 获取训练指标曲线数据 */
+export const getFineTuneMetrics = (id: string | number) =>
+  get<FineTuneMetricPoint[]>(`/fine-tune/${id}/metrics`)
+
 /** 启动 TensorBoard */
 export const startTensorboard = () => post('/fine-tune/tensorboard/start')

--- a/frontend/src/api/modules/model.ts
+++ b/frontend/src/api/modules/model.ts
@@ -88,10 +88,14 @@ export const updateModelPurpose = (id: string | number, purpose: string) =>

 /** 合并 LoRA 权重 */
 export const mergeModel = (data: {
+  trained_model_id?: string | number
  model_name: string
  train_method: string
  base_model_path: string
-}) => post('/model-manage/merge', data)
+  adapter_path?: string
+  compute_node_id?: string
+  output_model_name?: string
+}) => post('/model-manage/merge', data, { timeout: 15 * 60 * 1000 })

 /** 导出已训练模型权重 */
 export const exportModelUrl = (modelName: string) =>
--- a/frontend/src/composables/useStreamChat.ts
+++ b/frontend/src/composables/useStreamChat.ts
@@ -44,6 +44,24 @@ export function useStreamChat() {
  })
  const loading = ref(false)

+  /** 从 SSE 帧中提取错误信息（后端/计算节点错误以 data: {"error": "..."} 形式下发） */
+  function extractSseError(buffer: string): string | null {
+    const trimmed = buffer.trim()
+    if (!trimmed.startsWith('data: ')) return null
+    const lines = trimmed.split(/\r?\n/)
+    for (let i = lines.length - 1; i >= 0; i--) {
+      const line = lines[i].trim()
+      if (!line.startsWith('data: ')) continue
+      try {
+        const obj = JSON.parse(line.slice(6))
+        if (obj && typeof obj.error === 'string' && obj.error) return obj.error
+      } catch {
+        /* 非 JSON 的 data 行忽略 */
+      }
+    }
+    return trimmed
+  }
+
  /** 从内容中解析 think 标签 */
  function parseContent(content: string) {
    const thinkRegex = /<think>([\s\S]*?)(<\/think>)?/g
@@ -121,6 +139,16 @@ export function useStreamChat() {
      }

      // 最终更新
+      // 若整段响应是 SSE 错误帧，提取 error 字段以干净文案展示
+      const sseError = extractSseError(buffer)
+      if (sseError) {
+        message.value.isThinking = false
+        message.value.isStreaming = false
+        message.value.done = true
+        message.value.error = sseError
+        message.value.displayContent = sseError
+        return
+      }
      const parsed = parseContent(buffer)
      message.value.thinkContent = parsed.think
      message.value.displayContent = parsed.display
--- a/frontend/src/types/index.ts
+++ b/frontend/src/types/index.ts
@@ -34,6 +34,10 @@ export interface TrainedModel {
  name: string
  train_methods?: TrainMethod[]
  base_model_path?: string
+  artifact_dir?: string
+  adapter_path?: string
+  compute_node_id?: string
+  compute_node_name?: string
  create_time?: string
  merged?: boolean
  merging?: boolean
@@ -212,6 +216,9 @@ export interface LoadedModel {
  status?: string
  pid?: number
  port?: number
+  node_id?: string
+  node_name?: string
+  error?: string
 }

 export interface CompareTask {
@@ -230,6 +237,8 @@ export interface CompareModelRef {
  model_name: string
  model_path: string
  gpu_id: number
+  node_id?: string
+  node_name?: string
  source?: string
  port?: number
 }
--- a/frontend/src/views/inference/InferenceChatView.vue
+++ b/frontend/src/views/inference/InferenceChatView.vue
@@ -1,10 +1,10 @@
 <script setup lang="ts">
-import { ref, reactive, nextTick, onMounted, watch } from 'vue'
+import { ref, reactive, nextTick, onMounted, onUnmounted, watch } from 'vue'
 import { useRoute, useRouter } from 'vue-router'
 import { ElMessage } from 'element-plus'
 import MarkdownView from '@/components/MarkdownView.vue'
 import { useStreamChat } from '@/composables/useStreamChat'
-import { getCompare } from '@/api/modules/compare'
+import { getCompare, getLoadStatus } from '@/api/modules/compare'
 import type { CompareTask, LoadedModel } from '@/types'

 const route = useRoute()
@@ -37,6 +37,10 @@ const contentRef = ref<HTMLElement>()
 let activeAssistant: ChatMessage | null = null
 /** 设置面板抽屉 */
 const showSettings = ref(false)
+/** 模型仍在加载中（直接 URL 进入 chat 时兜底轮询就绪状态） */
+const taskLoading = ref(false)
+const taskError = ref('')
+let statusTimer: ReturnType<typeof setInterval> | null = null

 /** 获取任务信息，定位已启动的模型（mock 模式跳过） */
 async function loadTask() {
@@ -45,6 +49,14 @@ async function loadTask() {
    task.value = await getCompare(taskId)
    const models = parseLoadedModels(task.value)
    if (models[0]?.model_name) modelName.value = models[0].model_name
+    // 恢复本地保存的历史对话
+    restoreHistory()
+    // 模型仍在上次加载中：启动轮询等待就绪
+    if (models.some((m) => m.status === 'starting')) {
+      taskLoading.value = true
+      await pollTaskStatus()
+      statusTimer = setInterval(pollTaskStatus, 3000)
+    }
  } catch {
    // ignore
  }
@@ -60,6 +72,80 @@ function parseLoadedModels(t: CompareTask | null): LoadedModel[] {
  }
 }

+/** 对话历史本地持久化（按任务 id 存储，退出重进可恢复） */
+const STORAGE_PREFIX = 'ygft_chat_history_'
+
+function historyKey(id: string | number): string {
+  return `${STORAGE_PREFIX}${id}`
+}
+
+function saveHistory() {
+  if (isMock) return
+  try {
+    const snapshot = messages.value.map((m) => ({
+      role: m.role,
+      content: m.content,
+      think: m.think,
+      done: true,
+    }))
+    localStorage.setItem(historyKey(taskId), JSON.stringify(snapshot))
+  } catch {
+    // 存储失败忽略
+  }
+}
+
+function restoreHistory() {
+  if (isMock) return
+  try {
+    const raw = localStorage.getItem(historyKey(taskId))
+    if (!raw) return
+    const parsed = JSON.parse(raw)
+    if (Array.isArray(parsed)) {
+      messages.value = parsed.map((m) => ({
+        role: m.role === 'user' ? 'user' : 'assistant',
+        content: m.content || '',
+        think: m.think || '',
+        isThinking: false,
+        isStreaming: false,
+        done: true,
+      }))
+    }
+  } catch {
+    // 恢复失败忽略
+  }
+}
+
+/** 停止就绪状态轮询 */
+function stopStatusPolling() {
+  if (statusTimer) {
+    clearInterval(statusTimer)
+    statusTimer = null
+  }
+}
+
+/** 轮询任务加载状态：starting → ready/error */
+async function pollTaskStatus() {
+  try {
+    const st = await getLoadStatus(taskId)
+    const items = st.loaded_models || []
+    const anyReady = items.some((m) => m.status === 'ready' || m.status === 'running')
+    const anyError = items.some((m) => m.status === 'error')
+    if (anyReady) {
+      taskLoading.value = false
+      taskError.value = ''
+      stopStatusPolling()
+    } else if (anyError) {
+      taskLoading.value = false
+      taskError.value = items.find((m) => m.status === 'error')?.error || '模型加载失败'
+      stopStatusPolling()
+    } else {
+      taskLoading.value = true
+    }
+  } catch {
+    // 轮询失败忽略，下次再试
+  }
+}
+
 async function handleSend() {
  const question = inputQuestion.value.trim()
  if (!question || loading.value) return
@@ -76,6 +162,7 @@ async function handleSend() {
    done: false,
  })
  messages.value.push(assistantMsg)
+  saveHistory()

  inputQuestion.value = ''
  await nextTick()
@@ -85,6 +172,7 @@ async function handleSend() {
  // mock 模式：直接用假数据逐字填充
  if (isMock) {
    await mockReply(assistantMsg, question)
+    saveHistory()
    return
  }

@@ -94,6 +182,7 @@ async function handleSend() {
  await send(
    {
      model_path: route.query.model_path as string || '',
+      task_id: taskId,
      system_prompt: systemPrompt.value,
      user_question: question,
      temperature: temperature.value,
@@ -111,6 +200,7 @@ async function handleSend() {
  assistantMsg.done = true
  activeAssistant = null
  reset()
+  saveHistory()
  await nextTick()
  scrollToBottom()
 }
@@ -169,6 +259,11 @@ function handleNewChat() {
  activeAssistant = null
  messages.value = []
  reset()
+  try {
+    localStorage.removeItem(historyKey(taskId))
+  } catch {
+    // 忽略
+  }
 }

 /** 输入框自适应高度 */
@@ -185,6 +280,7 @@ function resetInputHeight() {
 }

 onMounted(loadTask)
+onUnmounted(stopStatusPolling)
 </script>

 <template>
@@ -252,6 +348,12 @@ onMounted(loadTask)

    <!-- 输入栏 -->
    <footer class="chat-input-container">
+      <div v-if="taskLoading" class="loading-hint">
+        <i class="fa fa-spinner fa-spin" style="margin-right: 6px" />模型加载中，就绪后即可对话...
+      </div>
+      <div v-else-if="taskError" class="loading-hint error">
+        <i class="fa fa-exclamation-circle" style="margin-right: 6px" />{{ taskError }}
+      </div>
      <div class="chat-input-inner">
        <button class="clear-btn" title="清空对话" @click="handleNewChat">
          <i class="fa fa-eraser" />
@@ -261,22 +363,21 @@ onMounted(loadTask)
            v-model="inputQuestion"
            class="input-box"
            rows="1"
-            :disabled="loading"
+            :disabled="loading || taskLoading"
            placeholder="给模型发送消息..."
            @keydown.enter.exact.prevent="handleSend"
            @input="autoResize"
          />
          <button
            class="send-btn"
-            :class="{ active: inputQuestion.trim() && !loading }"
-            :disabled="!inputQuestion.trim() || loading"
+            :class="{ active: inputQuestion.trim() && !loading && !taskLoading }"
+            :disabled="!inputQuestion.trim() || loading || taskLoading"
            @click="handleSend"
          >
            <i class="fa fa-arrow-up" />
          </button>
        </div>
      </div>
-      <div class="footer-hint">内容由 AI 生成，请仔细甄别。</div>
    </footer>

    <!-- 设置抽屉（系统提示词等） -->
@@ -703,9 +804,18 @@ onMounted(loadTask)
  }
 }

-.footer-hint {
-  margin-top: 12px;
-  font-size: 12px;
-  color: #9ca3af;
+.loading-hint {
+  margin-bottom: 10px;
+  padding: 6px 14px;
+  font-size: 13px;
+  color: #b45309;
+  background: #fef3c7;
+  border-radius: 8px;
+  text-align: center;
+
+  &.error {
+    color: #b91c1c;
+    background: #fee2e2;
+  }
 }
 </style>
--- a/frontend/src/views/inference/InferenceCreateView.vue
+++ b/frontend/src/views/inference/InferenceCreateView.vue
@@ -1,12 +1,12 @@
 <script setup lang="ts">
-import { ref, reactive, computed, onMounted } from 'vue'
+import { ref, reactive, computed, onMounted, watch } from 'vue'
 import { useRouter } from 'vue-router'
 import { ElMessage, type FormInstance, type FormRules } from 'element-plus'
 import PageCard from '@/components/PageCard.vue'
 import { getModelList, getTrainedModels } from '@/api/modules/model'
 import { getSystemInfo } from '@/api/modules/system'
 import { getComputeNodes, type ComputeNode } from '@/api/modules/compute'
-import { createCompare, preloadLocalModel, preloadTrainedModel } from '@/api/modules/compare'
+import { createCompare, loadCompare } from '@/api/modules/compare'
 import type { ModelItem, TrainedModel, GpuInfo } from '@/types'

 const router = useRouter()
@@ -27,6 +27,8 @@ interface SelectableModel {
  name: string
  source: 'database' | 'trained'
  model_path: string
+  compute_node_id?: string
+  compute_node_name?: string
  merged?: boolean
  merging?: boolean
  disabled?: boolean
@@ -51,6 +53,8 @@ const trainedOptions = computed<SelectableModel[]>(() =>
    name: m.name,
    source: 'trained',
    model_path: m.merged_path || m.base_model_path || '',
+    compute_node_id: m.compute_node_id,
+    compute_node_name: m.compute_node_name,
    merged: m.merged,
    merging: m.merging,
    disabled: m.merged === false,
@@ -80,7 +84,7 @@ const form = reactive({
  /** 选中的模型 key（单选） */
  model_key: '',
  /** 使用的 GPU */
-  gpu_id: 0,
+  gpu_key: '',
 })

 const rules: FormRules = {
@@ -90,6 +94,13 @@ const rules: FormRules = {

 /** 当前选中的模型对象 */
 const selectedModel = computed(() => modelMap.value[form.model_key])
+const selectedGpu = computed(() => idleGpus.value.find((g) => `${g.node_id || ''}:${g.id ?? 0}` === form.gpu_key))
+
+watch(selectedModel, (model) => {
+  if (!model?.compute_node_id) return
+  const gpu = idleGpus.value.find((item) => item.node_id === model.compute_node_id)
+  if (gpu) form.gpu_key = `${gpu.node_id || ''}:${gpu.id ?? 0}`
+})

 async function handleSubmit() {
  if (!formRef.value) return
@@ -101,62 +112,47 @@ async function handleSubmit() {
      return
    }
    submitting.value = true
-    startupStatus.value = '正在启动模型服务...'
+    startupStatus.value = '正在创建推理任务...'
    try {
-      // Step 1: 将模型加载到算力节点
-      const preloadPayload = {
-        model_name_or_path: m.model_path,
-        model_name: m.name,
-        template: 'qwen',
-      }
-      let preloadResult: any
-      if (m.source === 'trained') {
-        preloadResult = await preloadTrainedModel(preloadPayload)
-      } else {
-        preloadResult = await preloadLocalModel(preloadPayload)
-      }
-
-      if (preloadResult && (preloadResult as any).error) {
-        ElMessage.warning(`模型加载失败：${(preloadResult as any).error}`)
-        submitting.value = false
-        startupStatus.value = ''
+      if (!m.model_path) {
+        ElMessage.warning('当前模型未配置算力节点可访问路径，请先在模型管理中维护模型路径')
        return
      }

-      // Step 2: 创建推理任务记录
+      // Step 1: 创建推理任务记录
      const taskResult = await createCompare({
        name: form.name || m.name,
        description: form.description,
+        status: 'pending',
        models: [
          {
            model_id: String(m.id),
            model_name: m.name,
            model_path: m.model_path,
            source: m.source,
-            gpu_id: form.gpu_id,
+            gpu_id: selectedGpu.value?.id ?? 0,
+            node_id: selectedGpu.value?.node_id || m.compute_node_id,
+            node_name: selectedGpu.value?.node_name || m.compute_node_name,
          },
        ],
      })
      const taskId = taskResult?.id || 'unknown'

-      ElMessage.success('模型已启动')
-      router.push({
-        path: `/model-inference/chat/${taskId}`,
-        query: {
-          model: m.name,
-          source: m.source,
-          model_path: m.model_path,
-        },
-      })
+      // Step 2: 统一通过推理任务加载接口异步派发模型加载，状态会落到列表记录中。
+      startupStatus.value = '正在启动模型服务，首次加载可能需要数分钟...'
+      const loadResult: any = await loadCompare(taskId)
+      if (loadResult?.status === 'failed' || loadResult?.error) {
+        ElMessage.warning(`模型加载失败：${loadResult?.error || '请检查算力节点日志'}`)
+        router.push('/model-inference')
+        return
+      }
+
+      // 加载为异步派发，回到列表页可看到“启动中 → 已就绪”的状态流转
+      ElMessage.success('模型加载中，就绪后即可对话')
+      router.push('/model-inference')
    } catch (e: any) {
-      // 真实 API 失败时回退到 mock 模式（方便无算力节点的开发调试）
-      const m = selectedModel.value!
      const reason = e?.message || e?.toString() || '未知错误'
-      ElMessage.warning(`推理服务启动失败：${reason}，进入 mock 演示模式`)
-      router.push({
-        path: '/model-inference/chat/mock',
-        query: { model: m.name },
-      })
+      ElMessage.warning(`推理服务启动失败：${reason}`)
    } finally {
      submitting.value = false
      startupStatus.value = ''
@@ -181,7 +177,10 @@ async function loadData() {
    gpus.value = sys?.gpu || []
    computeNodes.value = nodes || []
    // 默认选中第一个空闲 GPU
-    if (idleGpus.value.length > 0) form.gpu_id = idleGpus.value[0].id ?? 0
+    if (idleGpus.value.length > 0) {
+      const firstGpu = idleGpus.value[0]
+      form.gpu_key = `${firstGpu.node_id || ''}:${firstGpu.id ?? 0}`
+    }
  } catch {
    // ignore
  }
@@ -229,12 +228,12 @@ onMounted(loadData)
      </el-form-item>

      <el-form-item label="GPU">
-        <el-select v-model="form.gpu_id" style="width: 400px">
+        <el-select v-model="form.gpu_key" style="width: 400px">
          <el-option
            v-for="g in idleGpus"
-            :key="g.id ?? 0"
-            :label="`${g.name} (GPU${g.id ?? 0}) [空闲]`"
-            :value="g.id ?? 0"
+            :key="`${g.node_id || ''}:${g.id ?? 0}`"
+            :label="`${g.node_name || g.node_code || '算力节点'} / ${g.name} (GPU${g.id ?? 0}) [空闲]`"
+            :value="`${g.node_id || ''}:${g.id ?? 0}`"
          />
        </el-select>
      </el-form-item>
--- a/frontend/src/views/inference/InferenceListView.vue
+++ b/frontend/src/views/inference/InferenceListView.vue
@@ -92,11 +92,9 @@ async function handleUnload(row: any) {
  loadData()
 }

-/** 删除（先释放算力节点再删除记录） */
+/** 删除（后端删除内部会 best-effort 释放算力节点，这里直接删记录） */
 async function handleDelete(row: any) {
  await ElMessageBox.confirm('确定要删除该推理记录吗？将先释放算力节点再删除。', '确认删除', { type: 'warning' })
-  // 先释放算力节点上的模型
-  await unloadCompare(row.id).catch(() => {})
  await deleteCompare(row.id)
  dataList.value = dataList.value.filter((item) => item.id !== row.id)
  await loadData(true)
--- a/frontend/src/views/model/MergeWeightsView.vue
+++ b/frontend/src/views/model/MergeWeightsView.vue
@@ -18,9 +18,12 @@ const trainedModels = ref<TrainedModel[]>([])
 const currentModel = computed(() => trainedModels.value.find((m) => m.name === modelName.value))

 const form = reactive({
+  trained_model_id: '',
  model_name: modelName.value,
  train_method: method.value,
  base_model_path: '',
+  adapter_path: '',
+  compute_node_id: '',
 })

 async function loadModel() {
@@ -28,23 +31,30 @@ async function loadModel() {
    const res = await getTrainedModels()
    trainedModels.value = res?.models || []
    const target = trainedModels.value.find((m) => m.name === modelName.value)
+    form.trained_model_id = target?.id == null ? '' : String(target.id)
    form.base_model_path = target?.base_model_path || ''
+    form.adapter_path = target?.artifact_dir || target?.adapter_path || target?.merged_path || ''
+    form.compute_node_id = target?.compute_node_id || ''
  } catch {
    // ignore
  }
 }

 async function handleMerge() {
-  if (!form.model_name || !form.base_model_path) {
+  if (!form.model_name || !form.base_model_path || !form.adapter_path) {
    ElMessage.warning('缺少模型信息')
    return
  }
  merging.value = true
  try {
    await mergeModel({
+      trained_model_id: form.trained_model_id || form.model_name,
      model_name: form.model_name,
      train_method: form.train_method,
      base_model_path: form.base_model_path,
+      adapter_path: form.adapter_path,
+      compute_node_id: form.compute_node_id,
+      output_model_name: `${form.model_name}-merged`,
    })
    ElMessage.success('合并成功')
    router.push('/model-manage')
@@ -82,6 +92,9 @@ onMounted(loadModel)
      <el-form-item label="基座模型路径">
        <el-input v-model="form.base_model_path" placeholder="基座模型路径" />
      </el-form-item>
+      <el-form-item label="Adapter 路径">
+        <el-input v-model="form.adapter_path" placeholder="LoRA Adapter 权重目录" />
+      </el-form-item>

      <el-form-item>
        <el-button type="primary" :loading="merging" @click="handleMerge">
--- a/frontend/src/views/system/TrainingLogView.vue
+++ b/frontend/src/views/system/TrainingLogView.vue
@@ -8,13 +8,14 @@ import TrainingTaskOverview from './training-log/TrainingTaskOverview.vue'
 import { usePolling } from '@/composables/usePolling'
 import '@/plugins/echarts-training-log'
 import { useModelsStore } from '@/stores/models'
-import { getFineTune, getFineTuneDiagnostics, getFineTuneLogs, type TrainingDiagnostic } from '@/api/modules/fineTune'
+import { getFineTune, getFineTuneDiagnostics, getFineTuneLogs, getFineTuneMetrics, type TrainingDiagnostic } from '@/api/modules/fineTune'
 import { getTrainingLogFiles, getTrainingLogContent } from '@/api/modules/log'
 import { getDataset } from '@/api/modules/dataset'
 import { getSystemInfo } from '@/api/modules/system'
 import { TRAIN_TYPE_MAP, TRAIN_METHOD_MAP } from '@/constants'
 import {
  buildMetricChartOption,
+  metricsFromApi,
  parseTrainingLog,
  resolveTrainingLogFile,
 } from './training-log/trainingLogModel'
@@ -42,6 +43,7 @@ const loading = ref(true)

 // 训练指标数据（ECharts 接收 number[]，下标即 step）
 const metricData = reactive({
+  steps: [] as number[],
  loss: [] as number[],
  gradNorm: [] as number[],
  lr: [] as number[],
@@ -62,9 +64,9 @@ const gpuExpanded = ref(false)
 let refreshInFlight = false

 /** 三个曲线的 ECharts 配置（响应式，数据变化自动重绘） */
-const lossChartOption = computed(() => buildMetricChartOption('Loss', metricData.loss, '#4f46e5'))
-const gradChartOption = computed(() => buildMetricChartOption('Grad Norm', metricData.gradNorm, '#3b82f6'))
-const lrChartOption = computed(() => buildMetricChartOption('Learning Rate', metricData.lr, '#14b8a6', true))
+const lossChartOption = computed(() => buildMetricChartOption('Loss', metricData.loss, metricData.steps, '#4f46e5'))
+const gradChartOption = computed(() => buildMetricChartOption('Grad Norm', metricData.gradNorm, metricData.steps, '#3b82f6'))
+const lrChartOption = computed(() => buildMetricChartOption('Learning Rate', metricData.lr, metricData.steps, '#14b8a6', true))
 const baseModelName = computed(() => task.value?.base_model != null
  ? modelsStore.getModelName(task.value.base_model)
  : '未配置')
@@ -77,10 +79,10 @@ const trainingMethodName = computed(() => task.value?.train_method
 const taskGpuLabel = computed(() => task.value?.gpus?.length
  ? task.value.gpus.map((gpuId) => `GPU ${gpuId}`).join('、')
  : '未配置')
-const latestLoss = computed(() => metricData.loss[metricData.loss.length - 1])
-const latestGradNorm = computed(() => metricData.gradNorm[metricData.gradNorm.length - 1])
-const latestLearningRate = computed(() => metricData.lr[metricData.lr.length - 1])
-const latestEpoch = computed(() => metricData.epoch[metricData.epoch.length - 1])
+const latestLoss = computed(() => lastFinite(metricData.loss))
+const latestGradNorm = computed(() => lastFinite(metricData.gradNorm))
+const latestLearningRate = computed(() => lastFinite(metricData.lr))
+const latestEpoch = computed(() => lastFinite(metricData.epoch))
 const logLineCount = computed(() => logContent.value ? logContent.value.split(/\r?\n/).length : 0)
 const taskGpuItems = computed<TaskGpuItem[]>(() => (task.value?.gpus ?? []).map((gpuId) => {
  const index = Number(gpuId)
@@ -123,7 +125,7 @@ const gpuRefreshState = computed(() => {
  })
  return gpuLoadError.value
    ? `更新失败 · 最后更新 ${updateTime}`
-    : `${updateTime} 更新 · 每 5 秒刷新`
+    : `${updateTime} 更新 · 每 3 秒刷新`
 })

 function formatMetric(value?: number, scientific = false) {
@@ -131,6 +133,13 @@ function formatMetric(value?: number, scientific = false) {
  return scientific ? value.toExponential(2) : value.toFixed(4).replace(/0+$/, '').replace(/\.$/, '')
 }

+function lastFinite(values: number[]) {
+  for (let index = values.length - 1; index >= 0; index -= 1) {
+    if (Number.isFinite(values[index])) return values[index]
+  }
+  return undefined
+}
+
 function safePercent(value?: number) {
  return Math.round(Math.min(100, Math.max(0, Number(value || 0))))
 }
@@ -216,6 +225,7 @@ const isLoraMethod = computed(() =>
 function applyLogContent(content: string) {
  const parsed = parseTrainingLog(content)
  logContent.value = content
+  metricData.steps = parsed.metrics.steps
  metricData.loss = parsed.metrics.loss
  metricData.gradNorm = parsed.metrics.gradNorm
  metricData.lr = parsed.metrics.lr
@@ -223,6 +233,26 @@ function applyLogContent(content: string) {
  Object.assign(summary, parsed.summary)
 }

+function applyMetricData(metrics = { steps: [] as number[], loss: [] as number[], gradNorm: [] as number[], lr: [] as number[], epoch: [] as number[] }) {
+  metricData.steps = metrics.steps
+  metricData.loss = metrics.loss
+  metricData.gradNorm = metrics.gradNorm
+  metricData.lr = metrics.lr
+  metricData.epoch = metrics.epoch
+}
+
+async function loadMetrics(currentTask: FineTuneTask) {
+  try {
+    const points = await getFineTuneMetrics(currentTask.id)
+    const parsed = metricsFromApi(points || [])
+    if (parsed.loss.length || parsed.gradNorm.length || parsed.lr.length) {
+      applyMetricData(parsed)
+    }
+  } catch {
+    // 日志解析结果会作为兜底曲线数据。
+  }
+}
+
 async function loadLog(currentTask: FineTuneTask) {
  try {
    const runtime = await getFineTuneLogs(currentTask.id, { tail_lines: 800 })
@@ -277,6 +307,7 @@ async function refreshAll() {
      ? loadDataset(currentTask.train_dataset_id)
      : Promise.resolve()
    await Promise.all([datasetPromise, loadLog(currentTask), loadGpuStatus(), loadDiagnostics(currentTask)])
+    await loadMetrics(currentTask)
  } finally {
    loading.value = false
    refreshInFlight = false
@@ -523,7 +554,7 @@ onMounted(async () => {

    <!-- 训练曲线 -->
    <PageCard class="metrics-panel" title="训练曲线" subtitle="持续监控模型收敛情况与学习率变化">
-      <template #extra><span class="refresh-state">每 5 秒刷新</span></template>
+      <template #extra><span class="refresh-state">每 3 秒刷新</span></template>
      <div class="chart-list" aria-label="训练指标曲线">
        <section class="chart-section">
          <div class="chart-section-header">
@@ -560,7 +591,7 @@ onMounted(async () => {

    <!-- 原始日志 -->
    <PageCard class="log-card" title="训练日志" subtitle="查看训练任务的原始运行输出">
-      <template #extra><span class="log-meta">{{ logLineCount }} 行 · 每 5 秒刷新</span></template>
+      <template #extra><span class="log-meta">{{ logLineCount }} 行 · 每 3 秒刷新</span></template>
      <pre class="log-pre">{{ logContent || '暂无日志' }}</pre>
    </PageCard>
    </template>
--- a/frontend/src/views/system/training-log/trainingLogModel.ts
+++ b/frontend/src/views/system/training-log/trainingLogModel.ts
@@ -1,7 +1,9 @@
 import type { EChartsOption } from 'echarts'
 import type { FineTuneTask, TrainingLogFile } from '@/types'
+import type { FineTuneMetricPoint } from '@/api/modules/fineTune'

 export interface TrainingMetricData {
+  steps: number[]
  loss: number[]
  gradNorm: number[]
  lr: number[]
@@ -26,7 +28,7 @@ function escapeRegExp(value: string) {
 }

 function extractNumber(source: string, key: string) {
-  const match = source.match(new RegExp(`['"]?${escapeRegExp(key)}['"]?\\s*:\\s*(${NUMBER_SOURCE})`, 'i'))
+  const match = source.match(new RegExp(`['"]?${escapeRegExp(key)}['"]?\\s*(?:=|:)\\s*(${NUMBER_SOURCE})`, 'i'))
  return match ? Number(match[1]) : undefined
 }

@@ -57,24 +59,44 @@ export function resolveTrainingLogFile(

 /** 解析日志中的逐步指标。字段顺序和常见数值格式均不受限制。 */
 export function parseTrainingMetrics(text: string): TrainingMetricData {
-  const metrics: TrainingMetricData = { loss: [], gradNorm: [], lr: [], epoch: [] }
-  const blocks = text.match(/\{[^{}\r\n]*\}/g) || []
+  const metrics: TrainingMetricData = { steps: [], loss: [], gradNorm: [], lr: [], epoch: [] }
+  const candidates = text
+    .split(/\r?\n/)
+    .flatMap((line) => {
+      const blocks = line.match(/\{[^{}\r\n]*\}/g)
+      return blocks?.length ? blocks.map((block) => `${line} ${block}`) : [line]
+    })

-  for (const block of blocks) {
-    const loss = extractNumber(block, 'loss')
-    const gradNorm = extractNumber(block, 'grad_norm')
-    const learningRate = extractNumber(block, 'learning_rate')
-    const epoch = extractNumber(block, 'epoch')
-    if (loss == null || gradNorm == null || learningRate == null) continue
-    metrics.loss.push(loss)
-    metrics.gradNorm.push(gradNorm)
-    metrics.lr.push(learningRate)
-    if (epoch != null) metrics.epoch.push(epoch)
+  for (const [index, line] of candidates.entries()) {
+    const loss = extractNumber(line, 'loss')
+    const gradNorm = extractNumber(line, 'grad_norm')
+    const learningRate = extractNumber(line, 'learning_rate')
+    const epoch = extractNumber(line, 'epoch')
+    if (loss == null && gradNorm == null && learningRate == null) continue
+    metrics.steps.push(extractNumber(line, 'step') ?? metrics.steps.length + index + 1)
+    metrics.loss.push(loss ?? Number.NaN)
+    metrics.gradNorm.push(gradNorm ?? Number.NaN)
+    metrics.lr.push(learningRate ?? Number.NaN)
+    metrics.epoch.push(epoch ?? Number.NaN)
  }

  return metrics
 }

+export function metricsFromApi(points: FineTuneMetricPoint[]): TrainingMetricData {
+  const metrics: TrainingMetricData = { steps: [], loss: [], gradNorm: [], lr: [], epoch: [] }
+  for (const [index, point] of points.entries()) {
+    const hasMetric = point.loss != null || point.grad_norm != null || point.learning_rate != null
+    if (!hasMetric) continue
+    metrics.steps.push(Number(point.step || index + 1))
+    metrics.loss.push(point.loss == null ? Number.NaN : Number(point.loss))
+    metrics.gradNorm.push(point.grad_norm == null ? Number.NaN : Number(point.grad_norm))
+    metrics.lr.push(point.learning_rate == null ? Number.NaN : Number(point.learning_rate))
+    metrics.epoch.push(point.epoch == null ? Number.NaN : Number(point.epoch))
+  }
+  return metrics
+}
+
 /** 每次都返回新对象，日志截断或切换时不会残留上一轮汇总。 */
 export function parseTrainingSummary(text: string): TrainingSummary {
  const emptySummary: TrainingSummary = { epoch: '', trainLoss: '', runtime: '' }
@@ -102,11 +124,23 @@ export function parseTrainingLog(text: string): ParsedTrainingLog {
 export function buildMetricChartOption(
  label: string,
  data: number[],
+  steps: number[],
  color: string,
  logScale = false,
 ): EChartsOption {
+  const visibleData = data.map((value) => (Number.isFinite(value) ? value : null))
  return {
    grid: { top: 24, right: 20, bottom: 56, left: 56 },
+    graphic: visibleData.some((value) => value != null)
+      ? []
+      : [
+          {
+            type: 'text',
+            left: 'center',
+            top: 'middle',
+            style: { text: '暂无训练指标数据', fill: '#94a3b8', fontSize: 13 },
+          },
+        ],
    tooltip: {
      trigger: 'axis',
      axisPointer: { type: 'cross' },
@@ -116,6 +150,7 @@ export function buildMetricChartOption(
    },
    xAxis: {
      type: 'category',
+      data: steps.map((step, index) => (Number.isFinite(step) ? String(step) : String(index + 1))),
      boundaryGap: false,
      name: 'Step',
      nameTextStyle: { color: '#94a3b8', fontSize: 11 },
@@ -142,7 +177,7 @@ export function buildMetricChartOption(
      {
        name: label,
        type: 'line',
-        data,
+        data: visibleData,
        smooth: true,
        symbol: 'none',
        lineStyle: { width: 2, color },