feat: 实现基础设施层
axios 请求封装及七个业务模块 API，Pinia 状态管理（auth/system/models/tools），Mock 适配器与数据，以及流式对话、轮询、倒计时组合式函数。
This commit is contained in:
caoxiaozhu
268
frontend/src/mock/adapter.ts
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268
frontend/src/mock/adapter.ts
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/**
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* Axios Mock Adapter
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* 拦截所有 API 请求并返回 mock 数据
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* 通过 URL + method 路由到对应的 mock 响应
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*/
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import type { AxiosInstance, AxiosRequestConfig } from 'axios'
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import {
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mockLoginOk,
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mockHealth,
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mockSystemInfo,
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mockModels,
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mockTrainedModels,
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mockLocalModels,
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mockDatasets,
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mockFineTuneList,
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mockCompareList,
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mockEvalList,
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mockDimensions,
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mockLogFiles,
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mockTrainingLogFiles,
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mockLogContent,
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} from './data'
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/** 模拟网络延迟 */
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function delay(ms = 200): Promise<void> {
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return new Promise((resolve) => setTimeout(resolve, ms))
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}
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/** 构造 Axios 风格的成功响应 */
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function ok(data: any, config: AxiosRequestConfig = {}) {
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return {
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data: { code: 0, message: 'ok', data },
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status: 200,
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statusText: 'OK',
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headers: {},
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config,
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}
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}
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/** 构造失败响应 */
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function fail(message: string, code = 500, config: AxiosRequestConfig = {}) {
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return {
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data: { code, message },
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status: code,
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statusText: message,
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headers: {},
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config,
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}
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}
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/** 简易 URL 解析（去掉 baseURL 前缀） */
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function stripBaseURL(url: string): string {
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// url 可能带查询参数
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const [path] = url.split('?')
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return path
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}
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/** 通过路径 + method 匹配 mock 响应 */
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async function handleMock(config: AxiosRequestConfig) {
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await delay(150) // 模拟网络延迟
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const url = stripBaseURL(config.url || '')
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const method = (config.method || 'get').toLowerCase()
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const params = config.params || {}
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const body = typeof config.data === 'string' ? safeJSON(config.data) : config.data || {}
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// ==================== 认证 ====================
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if (url === '/login' && method === 'post') {
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if (body.username === 'admin' && body.password === 'admin') {
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return ok(mockLoginOk.data)
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}
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return fail('账号或密码错误', 401)
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}
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if (url === '/web-log' && method === 'post') {
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return ok({ received: true })
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}
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// ==================== 系统监控 ====================
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if (url === '/health' && method === 'get') return ok(mockHealth)
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if (url === '/system-info' && method === 'get') return ok(mockSystemInfo)
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// ==================== 模型管理 ====================
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if (url === '/model-manage' && method === 'get') return ok(mockModels)
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if (url === '/model-manage/local-models' && method === 'get') return ok(mockLocalModels)
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if (url === '/model-manage/trained-models' && method === 'get') return ok(mockTrainedModels)
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if (url === '/model-manage/merge' && method === 'post') {
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return ok({ merged: true, path: '/data/saves/' + body.model_name + '-merged' })
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}
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// 模型详情 / 编辑 / 删除 / 用途更新
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let m = url.match(/^\/model-manage\/([^/]+)$/)
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if (m && method === 'get') {
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const id = m[1]
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const found = mockModels.find((x) => String(x.id) === id || x.name === id)
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return found ? ok(found) : fail('模型不存在', 404)
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}
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m = url.match(/^\/model-manage\/([^/]+)$/)
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if (m && (method === 'put' || method === 'delete')) {
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return ok({ id: m[1], ...body })
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}
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m = url.match(/^\/model-manage\/name\/([^/]+)$/)
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if (m && method === 'get') {
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const name = decodeURIComponent(m[1])
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const found = mockModels.find((x) => x.name === name)
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return found ? ok(found) : fail('模型不存在', 404)
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}
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m = url.match(/^\/model-manage\/trained-models\/([^/]+)$/)
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if (m && method === 'delete') {
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return ok({ deleted: m[1] })
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}
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m = url.match(/^\/model-manage\/([^/]+)\/purpose$/)
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if (m && method === 'put') {
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return ok({ id: m[1], purpose: body.purpose })
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}
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// ==================== 数据集 ====================
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if (url === '/dataset-manage' && method === 'get') return ok(mockDatasets)
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if (url === '/dataset-manage' && method === 'post') {
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const newId = Math.max(...mockDatasets.map((d) => Number(d.id))) + 1
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return ok({ id: newId })
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}
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m = url.match(/^\/dataset-manage\/([^/]+)$/)
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if (m && method === 'get') {
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const found = mockDatasets.find((x) => String(x.id) === m[1])
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return found ? ok(found) : fail('数据集不存在', 404)
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}
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if (m && (method === 'put' || method === 'delete')) {
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return ok({ id: m[1] })
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}
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m = url.match(/^\/dataset-manage\/upload\/([^/]+)$/)
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if (m && method === 'post') return ok({ uploaded: true })
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m = url.match(/^\/dataset-manage\/preview\/([^/]+)$/)
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if (m && method === 'get') return ok(mockLogContent)
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// ==================== 训练任务 ====================
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if (url === '/fine-tune' && method === 'get') return ok(mockFineTuneList)
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if (url === '/fine-tune' && method === 'post') {
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return ok({ id: Math.floor(Math.random() * 10000) + 100 })
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}
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if (url === '/fine-tune/start' && method === 'post') {
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return ok({ started: true })
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}
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if (url === '/fine-tune/check-name' && method === 'get') {
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return ok({ exists: false })
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}
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m = url.match(/^\/fine-tune\/progress\/([^/]+)$/)
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if (m && method === 'get') {
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const task = mockFineTuneList.find((t) => String(t.id) === m[1])
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if (!task) return fail('任务不存在', 404)
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if (task.status === 'running') {
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return ok({
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status: task.status,
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progress: task.progress ?? 0,
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step: `${Math.floor((task.progress ?? 0) * 10)}/1000`,
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speed: '1.23s/it',
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eta: '00:23:45',
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})
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}
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return ok({ status: task.status, progress: task.progress ?? 0 })
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}
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m = url.match(/^\/fine-tune\/([^/]+)$/)
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if (m && method === 'get') {
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const found = mockFineTuneList.find((x) => String(x.id) === m[1])
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return found ? ok(found) : fail('任务不存在', 404)
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}
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m = url.match(/^\/fine-tune\/stop\/([^/]+)$/)
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if (m && method === 'post') return ok({ stopped: true })
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m = url.match(/^\/fine-tune\/([^/]+)$/)
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if (m && (method === 'put' || method === 'delete')) {
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return ok({ id: m[1] })
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}
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if (url === '/fine-tune/tensorboard/start' && method === 'post') {
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return ok({ url: 'http://10.10.10.77:6006' })
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}
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// ==================== 模型推理/对比 ====================
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if (url === '/model-compare' && method === 'get') return ok(mockCompareList)
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if (url === '/model-compare' && method === 'post') {
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return ok({ id: Math.floor(Math.random() * 10000) + 100 })
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}
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m = url.match(/^\/model-compare\/([^/]+)$/)
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if (m && method === 'get') {
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const found = mockCompareList.find((x) => String(x.id) === m[1])
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return found ? ok(found) : fail('任务不存在', 404)
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}
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if (m && method === 'delete') return ok({ deleted: m[1] })
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m = url.match(/^\/model-compare\/([^/]+)\/load-status$/)
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if (m && method === 'get') {
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return ok({ all_ready: true, loaded_models: [] })
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}
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if (m && method === 'post') return ok({ updated: true })
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m = url.match(/^\/model-compare\/([^/]+)\/load$/)
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if (m && method === 'post') return ok({ loading: true })
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m = url.match(/^\/model-compare\/([^/]+)\/unload$/)
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if (m && method === 'post') return ok({ stopped: true })
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m = url.match(/^\/model-compare\/([^/]+)\/start-model$/)
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if (m && method === 'post') {
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return ok({ pid: 12345 + Math.floor(Math.random() * 100), port: 18000 + Math.floor(Math.random() * 1000) })
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}
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if (url === '/model-compare/all/stop-all' && method === 'post') return ok({ stopped: true })
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if (url === '/model-compare/stop-by-pid' && method === 'post') return ok({ stopped: true })
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if (url === '/model-compare/chat-with-port' && method === 'post') {
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// 模拟推理回答（用于对比结果页）
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const userQ = body?.messages?.find((m: any) => m.role === 'user')?.content || '你的问题'
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const answer = `这是一个针对「${userQ.slice(0, 30)}」的模拟回答。\n\n在真实环境中，对比结果页会通过端口代理调用对应模型服务，返回该模型的真实推理结果。\n\n模型参数量、温度、Top-p 等参数都会影响输出内容。\n\n- 模型：${body?.model_name || '未知'}\n- 端口：${body?.port || '-'}\n- 参数：temperature=${body?.temperature ?? 0.7}, max_tokens=${body?.max_tokens ?? 2048}`
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return ok({ response: answer, content: answer })
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}
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if (url === '/model-compare/stream-chat' && method === 'post') {
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// 模拟流式对话（前端 useStreamChat 会按块读取）
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const userQ = body?.user_question || ''
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const sysPrompt = body?.system_prompt || ''
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const answer = `${sysPrompt ? '【系统提示】' + sysPrompt.slice(0, 50) + '\n\n' : ''}关于「${userQ}」的回答：\n\n这是一段**模拟流式输出**。在真实部署中，后端会通过 SSE/WebSocket 逐字推送 token。\n\n## 模型参数\n- 温度（temperature）：${body?.temperature ?? 0.7}\n- 最大长度（max_tokens）：${body?.max_tokens ?? 2048}\n\n## 思考过程\n让我先分析这个问题...\n- 识别用户意图\n- 检索相关知识\n- 生成回答\n\n回答已生成。以上为前端 Mock 演示内容。`
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return ok({ response: answer })
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}
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if (url === '/model-compare/test-stream' && method === 'post') {
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return ok({ response: '测试流式输出' })
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}
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if (url === '/model-chat/batch' && method === 'post') return ok({ responses: [] })
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if (url === '/model-chat/local/chat' && method === 'post') return ok({ response: '本地模型回复' })
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if (url === '/model-chat/local/preload' && method === 'post') return ok({ loaded: true })
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if (url === '/model-chat/trained/preload' && method === 'post') return ok({ loaded: true })
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// ==================== 模型评测 ====================
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if (url === '/model-eval' && method === 'get') return ok(mockEvalList)
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if (url === '/model-eval' && method === 'delete') return ok({ deleted: true })
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if (url === '/model-eval/start' && method === 'post') return ok({ task_id: Math.floor(Math.random() * 1000) + 1 })
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if (url === '/dimension' && method === 'get') return ok(mockDimensions)
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if (url === '/dimension' && method === 'post') {
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return ok({ id: Math.floor(Math.random() * 1000) + 100 })
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}
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m = url.match(/^\/dimension\/([^/]+)$/)
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if (m && method === 'get') {
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const found = mockDimensions.find((x) => String(x.id) === m[1])
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return found ? ok(found) : fail('维度不存在', 404)
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}
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if (m && (method === 'put' || method === 'delete')) {
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return ok({ id: m[1] })
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}
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// ==================== 日志 ====================
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if (url === '/log-files' && method === 'get') return ok(mockLogFiles)
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if (url === '/log-content' && method === 'get') return ok(mockLogContent)
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if (url === '/training-log-files' && method === 'get') return ok(mockTrainingLogFiles)
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if (url === '/training-log-content' && method === 'get') return ok(mockLogContent)
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// 未匹配的请求 → 兜底返回空成功（避免阻断 UI）
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console.warn('[Mock] 未匹配路由:', method.toUpperCase(), url, params)
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return ok({ mocked: true, url, method, params, body })
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}
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function safeJSON(str: string) {
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try {
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return JSON.parse(str)
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} catch {
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return {}
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}
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}
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/** 给 axios instance 安装 mock adapter */
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export function installMockAdapter(instance: AxiosInstance) {
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instance.defaults.adapter = async (config: AxiosRequestConfig) => {
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try {
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const response = await handleMock(config)
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return response
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} catch (e: any) {
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return fail(e.message || 'Mock 错误', 500, config)
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}
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}
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}
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230
frontend/src/mock/data.ts
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230
frontend/src/mock/data.ts
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/**
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* 全量 Mock 数据
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* 为前端开发提供不依赖后端的模拟数据
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*/
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import type {
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FineTuneTask,
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ModelItem,
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TrainedModel,
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DatasetItem,
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CompareTask,
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EvalTask,
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Dimension,
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SystemInfo,
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HealthMetrics,
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LogFile,
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TrainingLogFile,
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LogContent,
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} from '@/types'
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// ============ 认证 ============
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export const mockLoginOk = { code: 0, message: 'ok', data: { token: 'mock-token' } }
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// ============ 系统监控 ============
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export const mockHealth: HealthMetrics = {
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cpu_percent: 32,
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memory_percent: 58,
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disk_percent: 45,
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}
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export const mockSystemInfo: SystemInfo = {
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cpu: {
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percent: 32,
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cores: 8,
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percents: [25, 38, 42, 30, 28, 36, 40, 18],
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},
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memory: {
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used_gb: 9.2,
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total_gb: 16,
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percent: 58,
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available_gb: 6.8,
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cached_gb: 2.3,
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},
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disk: {
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used_gb: 230,
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total_gb: 512,
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percent: 45,
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},
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gpu: [
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{ name: 'NVIDIA A800', gpu_percent: 0, memory_used_gb: 0.0, memory_total_gb: 80, temperature: 32, power_w: 38, fan_speed: 0, clock_mhz: 1410, driver_version: '535.86.10' },
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{ name: 'NVIDIA A800', gpu_percent: 28, memory_used_gb: 22.5, memory_total_gb: 80, temperature: 52, power_w: 165, fan_speed: 32, clock_mhz: 1410, driver_version: '535.86.10' },
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{ name: 'NVIDIA A800', gpu_percent: 73, memory_used_gb: 58.7, memory_total_gb: 80, temperature: 68, power_w: 320, fan_speed: 58, clock_mhz: 1410, driver_version: '535.86.10' },
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{ name: 'NVIDIA A800', gpu_percent: 0, memory_used_gb: 0.0, memory_total_gb: 80, temperature: 34, power_w: 42, fan_speed: 0, clock_mhz: 1410, driver_version: '535.86.10' },
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{ name: 'NVIDIA A800', gpu_percent: 46, memory_used_gb: 36.8, memory_total_gb: 80, temperature: 60, power_w: 210, fan_speed: 42, clock_mhz: 1410, driver_version: '535.86.10' },
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{ name: 'NVIDIA A800', gpu_percent: 0, memory_used_gb: 0.0, memory_total_gb: 80, temperature: 33, power_w: 40, fan_speed: 0, clock_mhz: 1410, driver_version: '535.86.10' },
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{ name: 'NVIDIA A800', gpu_percent: 85, memory_used_gb: 68.2, memory_total_gb: 80, temperature: 73, power_w: 365, fan_speed: 62, clock_mhz: 1410, driver_version: '535.86.10' },
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{ name: 'NVIDIA A800', gpu_percent: 15, memory_used_gb: 12.0, memory_total_gb: 80, temperature: 45, power_w: 95, fan_speed: 22, clock_mhz: 1410, driver_version: '535.86.10' },
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],
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network: {
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download_mb: 1024,
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upload_mb: 256,
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},
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system: {
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uptime_seconds: 86400 * 3 + 3600 * 7 + 1800,
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process_count: 256,
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os: 'Ubuntu 22.04 LTS',
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},
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}
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// ============ 模型管理 ============
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export const mockModels: ModelItem[] = [
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{ id: 1, name: 'Qwen2.5-7B-Instruct', type: 'LLM', purpose: 'training', model_source: 'local', description: 'Qwen2.5 7B 指令微调基座', path: '/data/models/qwen2.5-7b', create_time: '2025-12-10T08:30:00Z' },
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{ id: 2, name: 'Qwen2.5-14B-Instruct', type: 'LLM', purpose: 'training', model_source: 'local', description: 'Qwen2.5 14B 指令微调基座', path: '/data/models/qwen2.5-14b', create_time: '2025-12-12T10:15:00Z' },
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{ id: 3, name: 'Llama3-8B-Instruct', type: 'LLM', purpose: 'inference', model_source: 'local', description: 'Llama3 8B 推理模型', path: '/data/models/llama3-8b', create_time: '2025-12-15T14:20:00Z' },
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{ id: 4, name: 'DeepSeek-V2-Lite', type: 'LLM', purpose: 'inference', model_source: 'local', description: 'DeepSeek V2 Lite', path: '/data/models/deepseek-v2-lite', create_time: '2026-01-05T09:00:00Z' },
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{ id: 5, name: 'GPT-4o', type: 'LLM', purpose: 'evaluation', model_source: 'api', description: 'OpenAI GPT-4o 在线模型', api_url: 'https://api.openai.com/v1', api_key: 'sk-***', online_model_name: 'gpt-4o', create_time: '2026-01-08T11:30:00Z' },
|
||||
{ id: 6, name: 'Claude-3.5-Sonnet', type: 'LLM', purpose: 'evaluation', model_source: 'api', description: 'Anthropic Claude 3.5 Sonnet', api_url: 'https://api.anthropic.com', api_key: 'sk-***', online_model_name: 'claude-3-5-sonnet-20241022', create_time: '2026-01-10T16:45:00Z' },
|
||||
{ id: 7, name: 'BGE-large-zh', type: 'Embedding', purpose: 'inference', model_source: 'local', description: '中文 embedding 模型', path: '/data/models/bge-large-zh', create_time: '2026-01-12T13:00:00Z' },
|
||||
]
|
||||
|
||||
export const mockTrainedModels: { models: TrainedModel[] } = {
|
||||
models: [
|
||||
{ id: 1, name: 'qwen-ft-finance-001', train_methods: [{ name: 'lora' }], base_model_path: '/data/models/qwen2.5-7b', merged: true, merging: false, merged_path: '/data/saves/qwen-ft-finance-001-merged', create_time: '2026-01-15T10:30:00Z' },
|
||||
{ id: 2, name: 'qwen-ft-legal-002', train_methods: [{ name: 'lora' }], base_model_path: '/data/models/qwen2.5-7b', merged: false, merging: true, create_time: '2026-01-18T14:20:00Z' },
|
||||
{ id: 3, name: 'llama3-ft-customer-service', train_methods: [{ name: 'qlora' }], base_model_path: '/data/models/llama3-8b', merged: true, merging: false, merged_path: '/data/saves/llama3-ft-customer-service-merged', create_time: '2026-01-22T09:45:00Z' },
|
||||
{ id: 4, name: 'qwen-ft-medical-003', train_methods: [{ name: 'lora' }], base_model_path: '/data/models/qwen2.5-14b', merged: false, merging: false, create_time: '2026-02-01T16:10:00Z' },
|
||||
],
|
||||
}
|
||||
|
||||
export const mockLocalModels = {
|
||||
models: [
|
||||
{ path: '/data/models/qwen2.5-7b', name: 'Qwen2.5-7B-Instruct' },
|
||||
{ path: '/data/models/qwen2.5-14b', name: 'Qwen2.5-14B-Instruct' },
|
||||
{ path: '/data/models/llama3-8b', name: 'Llama3-8B-Instruct' },
|
||||
{ path: '/data/models/deepseek-v2-lite', name: 'DeepSeek-V2-Lite' },
|
||||
{ path: '/data/models/bge-large-zh', name: 'BGE-large-zh' },
|
||||
],
|
||||
}
|
||||
|
||||
// ============ 数据集 ============
|
||||
export const mockDatasets: DatasetItem[] = [
|
||||
{ id: 1, name: '金融问答-训练集', type: 'train', storage_type: 'local', size: '128 MB', count: 8560, description: '金融领域问答对', create_time: '2025-12-20T08:00:00Z' },
|
||||
{ id: 2, name: '法律文书-训练集', type: 'train', storage_type: 'local', size: '256 MB', count: 15230, description: '法律文书数据集', create_time: '2025-12-25T10:30:00Z' },
|
||||
{ id: 3, name: '客服对话-训练集', type: 'train', storage_type: 'minio', size: '512 MB', count: 24500, description: '客服对话记录', create_time: '2026-01-05T14:20:00Z' },
|
||||
{ id: 4, name: '金融评测集', type: 'eval', storage_type: 'local', size: '32 MB', count: 1200, description: '金融领域评测', create_time: '2026-01-10T09:15:00Z' },
|
||||
{ id: 5, name: '通用能力评测', type: 'eval', storage_type: 'local', size: '64 MB', count: 3500, description: '通用能力评测数据集', create_time: '2026-01-12T11:30:00Z' },
|
||||
{ id: 6, name: '医疗问答-训练集', type: 'train', storage_type: 'local', size: '180 MB', count: 9800, description: '医疗问答对', create_time: '2026-02-01T15:00:00Z' },
|
||||
]
|
||||
|
||||
// ============ 训练任务 ============
|
||||
export const mockFineTuneList: FineTuneTask[] = [
|
||||
{ id: 1, name: 'finance-sft-001', description: '金融领域 SFT 训练', status: 'completed', train_type: 'SFT', train_method: 'lora', template: 'qwen', base_model: 1, train_dataset_id: 1, gpus: [0], progress: 100, train_duration: '2小时18分钟', create_time: '2026-01-15T08:00:00Z' },
|
||||
{ id: 2, name: 'legal-sft-002', description: '法律文书 SFT', status: 'completed', train_type: 'SFT', train_method: 'lora', template: 'qwen', base_model: 1, train_dataset_id: 2, gpus: [1], progress: 100, train_duration: '1小时46分钟', create_time: '2026-01-18T10:00:00Z' },
|
||||
{ id: 3, name: 'medical-cpt-001', description: '医疗领域继续预训练', status: 'running', train_type: 'CPT', train_method: 'lora', template: 'qwen2_5', base_model: 2, train_dataset_id: 6, gpus: [0, 1], progress: 64, train_duration: '36分钟', create_time: '2026-02-05T09:00:00Z' },
|
||||
{ id: 4, name: 'service-dpo-001', description: '客服对话偏好训练', status: 'pending', train_type: 'DPO', train_method: 'lora', template: 'qwen', base_model: 1, train_dataset_id: 3, gpus: [2], progress: 0, train_duration: '-', create_time: '2026-02-08T14:00:00Z' },
|
||||
{ id: 5, name: 'finance-sft-002', description: '金融领域二轮微调', status: 'failed', train_type: 'SFT', train_method: 'lora', template: 'qwen', base_model: 1, train_dataset_id: 1, gpus: [3], progress: 32, train_duration: '18分钟', create_time: '2026-02-10T11:00:00Z' },
|
||||
{ id: 6, name: 'general-sft-001', description: '通用能力微调', status: 'completed', train_type: 'SFT', train_method: 'full', template: 'llama3', base_model: 3, train_dataset_id: 3, gpus: [0, 2], progress: 100, train_duration: '3小时05分钟', create_time: '2026-02-12T13:00:00Z' },
|
||||
]
|
||||
|
||||
// ============ 模型推理/对比 ============
|
||||
export const mockCompareList: CompareTask[] = [
|
||||
{
|
||||
id: 1,
|
||||
name: '金融问答对比',
|
||||
model_name: '金融问答对比',
|
||||
description: '对比基座模型与微调模型',
|
||||
status: 'loaded',
|
||||
models: JSON.stringify([
|
||||
{ model_id: 1, model_name: 'Qwen2.5-7B-Instruct', model_path: '/data/models/qwen2.5-7b', gpu_id: 0, source: 'database', port: 18001 },
|
||||
{ model_id: 101, model_name: 'qwen-ft-finance-001', model_path: '/data/saves/qwen-ft-finance-001-merged', gpu_id: 1, source: 'trained', port: 18002 },
|
||||
]),
|
||||
load_status: JSON.stringify({
|
||||
loaded_models: [
|
||||
{ model_id: 1, model_name: 'Qwen2.5-7B-Instruct', status: 'ready', pid: 12345, port: 18001 },
|
||||
{ model_id: 101, model_name: 'qwen-ft-finance-001', status: 'ready', pid: 12346, port: 18002 },
|
||||
],
|
||||
}),
|
||||
create_time: '2026-02-15T10:00:00Z',
|
||||
},
|
||||
{
|
||||
id: 2,
|
||||
name: '客服场景推理',
|
||||
model_name: '客服场景推理',
|
||||
description: '客服对话推理测试',
|
||||
status: 'pending',
|
||||
models: JSON.stringify([{ model_id: 3, model_name: 'Llama3-8B-Instruct', model_path: '/data/models/llama3-8b', gpu_id: 2, source: 'database' }]),
|
||||
load_status: JSON.stringify({ loaded_models: [] }),
|
||||
create_time: '2026-02-18T11:00:00Z',
|
||||
},
|
||||
{
|
||||
id: 3,
|
||||
name: '法律文书推理',
|
||||
model_name: '法律文书推理',
|
||||
description: '法律文书推理测试',
|
||||
status: 'loaded',
|
||||
models: JSON.stringify([{ model_id: 102, model_name: 'qwen-ft-legal-002', model_path: '/data/saves/qwen-ft-legal-002-merged', gpu_id: 3, source: 'trained' }]),
|
||||
load_status: JSON.stringify({
|
||||
loaded_models: [
|
||||
{ model_id: 102, model_name: 'qwen-ft-legal-002', status: 'ready', pid: 12350, port: 18003 },
|
||||
],
|
||||
}),
|
||||
create_time: '2026-02-20T14:00:00Z',
|
||||
},
|
||||
]
|
||||
|
||||
// ============ 模型评测 ============
|
||||
export const mockEvalList: EvalTask[] = [
|
||||
{ id: 1, eval_task_name: '金融模型评测-v1', model_name: 'qwen-ft-finance-001', dataset: '金融评测集', metric: 'accuracy', score: 87.5, status: 'completed', create_time: '2026-02-01T10:00:00Z' },
|
||||
{ id: 2, eval_task_name: '客服模型评测-v1', model_name: 'llama3-ft-customer-service', dataset: '通用能力评测', metric: 'rouge-1', score: 0.82, status: 'completed', create_time: '2026-02-05T11:00:00Z' },
|
||||
{ id: 3, eval_task_name: '基线对比评测', model_name: 'Qwen2.5-7B-Instruct', dataset: '金融评测集', metric: 'accuracy', score: 72.3, status: 'running', create_time: '2026-02-10T09:00:00Z' },
|
||||
]
|
||||
|
||||
export const mockDimensions: Dimension[] = [
|
||||
{ id: 1, name: '回答准确性', type: 'classification', description: '评估模型回答是否准确', eval_model: 'GPT-4o', eval_method: 'standard', eval_prompt: '# 角色\n你是专业的评估专家...', is_active: true, is_default: true, create_time: '2025-12-01T08:00:00Z' },
|
||||
{ id: 2, name: '综合评分', type: 'metric', description: '0-5 分综合评分', eval_model: 'Claude-3.5-Sonnet', eval_method: 'metric_standard', eval_prompt: '# 角色\n你是专业评分专家...', is_active: true, is_default: false, score_min: 0, score_max: 5, pass_threshold: 3.5, create_time: '2025-12-05T09:00:00Z' },
|
||||
{ id: 3, name: '语义相似度', type: 'metric', description: '生成文本与参考答案的语义相似度', eval_model: 'GPT-4o', eval_method: 'semantic', eval_prompt: '# 角色\n你是语义相似度评估专家...', is_active: true, is_default: false, score_min: 0, score_max: 1, pass_threshold: 0.7, create_time: '2025-12-08T10:00:00Z' },
|
||||
{ id: 4, name: 'BLEU-4 相似度', type: 'text_similarity', description: '使用 BLEU-4 评估文本相似度', is_active: true, is_default: false, eval_method: ['bleu_4'], bleu_n: 4, output_precision: 3, create_time: '2025-12-10T11:00:00Z' },
|
||||
{ id: 5, name: 'ROUGE 多指标', type: 'text_similarity', description: 'ROUGE-1/2/4 多指标评估', is_active: false, is_default: false, eval_method: ['rouge_1', 'rouge_2', 'rouge_4'], bleu_n: 1, output_precision: 3, create_time: '2025-12-12T13:00:00Z' },
|
||||
]
|
||||
|
||||
// ============ 日志 ============
|
||||
export const mockLogFiles: LogFile[] = [
|
||||
{ file: 'system-2026-02-15.log', name: '系统日志-2026-02-15', size: '2.3 MB' },
|
||||
{ file: 'error-2026-02-15.log', name: '错误日志-2026-02-15', size: '156 KB' },
|
||||
{ file: 'system-2026-02-14.log', name: '系统日志-2026-02-14', size: '3.1 MB' },
|
||||
]
|
||||
|
||||
export const mockTrainingLogFiles: TrainingLogFile[] = [
|
||||
{ file: 'qwen-ft-finance-001_pid12345.log', name: 'finance-sft-001', size: '4.5 MB', pid: 12345, date: '2026-02-15' },
|
||||
{ file: 'llama3-ft-customer-service_pid12346.log', name: 'service-dpo-001', size: '2.1 MB', pid: 12346, date: '2026-02-18' },
|
||||
{ file: 'qwen-ft-legal-002_pid12350.log', name: 'legal-sft-002', size: '5.8 MB', pid: 12350, date: '2026-02-20' },
|
||||
]
|
||||
|
||||
const fakeLogLines = [
|
||||
"[2026-02-15 08:30:12] INFO: Loading model from /data/models/qwen2.5-7b",
|
||||
"[2026-02-15 08:30:13] INFO: Loading dataset finance-train-001 (8560 samples)",
|
||||
"[2026-02-15 08:30:15] INFO: Training started with batch_size=8, learning_rate=2e-5",
|
||||
"[2026-02-15 08:32:45] INFO: {'loss': 2.341, 'grad_norm': 1.234, 'learning_rate': 1.95e-05, 'epoch': 0.05}",
|
||||
"[2026-02-15 08:34:15] INFO: {'loss': 1.892, 'grad_norm': 0.987, 'learning_rate': 1.88e-05, 'epoch': 0.10}",
|
||||
"[2026-02-15 08:35:42] INFO: {'loss': 1.543, 'grad_norm': 0.876, 'learning_rate': 1.79e-05, 'epoch': 0.15}",
|
||||
"[2026-02-15 08:37:10] INFO: {'loss': 1.287, 'grad_norm': 0.765, 'learning_rate': 1.70e-05, 'epoch': 0.20}",
|
||||
"[2026-02-15 08:38:55] INFO: {'loss': 1.056, 'grad_norm': 0.654, 'learning_rate': 1.60e-05, 'epoch': 0.25}",
|
||||
"[2026-02-15 08:40:30] WARN: Gradient norm exceeds threshold (0.654 > 0.5)",
|
||||
"[2026-02-15 08:42:00] INFO: {'loss': 0.892, 'grad_norm': 0.543, 'learning_rate': 1.50e-05, 'epoch': 0.30}",
|
||||
"[2026-02-15 08:45:15] INFO: Saved checkpoint to /data/checkpoints/finance-sft-001-step-100",
|
||||
"[2026-02-15 08:46:30] INFO: {'loss': 0.754, 'grad_norm': 0.432, 'learning_rate': 1.40e-05, 'epoch': 0.35}",
|
||||
"[2026-02-15 08:48:00] INFO: {'loss': 0.623, 'grad_norm': 0.398, 'learning_rate': 1.30e-05, 'epoch': 0.40}",
|
||||
"[2026-02-15 08:50:15] INFO: {'loss': 0.512, 'grad_norm': 0.345, 'learning_rate': 1.20e-05, 'epoch': 0.45}",
|
||||
"[2026-02-15 08:52:30] ERROR: Failed to save model: No space left on device",
|
||||
"[2026-02-15 08:52:31] INFO: Retrying save with compressed format...",
|
||||
"[2026-02-15 08:53:00] INFO: Model saved successfully (size: 14.2 GB)",
|
||||
"[2026-02-15 08:55:00] INFO: {'loss': 0.421, 'grad_norm': 0.298, 'learning_rate': 1.10e-05, 'epoch': 0.50}",
|
||||
"[2026-02-15 08:57:30] INFO: {'loss': 0.356, 'grad_norm': 0.256, 'learning_rate': 1.00e-05, 'epoch': 0.55}",
|
||||
"[2026-02-15 09:00:00] INFO: Training completed successfully",
|
||||
"",
|
||||
"***** train metrics *****",
|
||||
" epoch = 1.0",
|
||||
" total_flos = 1234567890",
|
||||
" train_loss = 0.342",
|
||||
" train_runtime = 1785.2",
|
||||
" train_samples_per_second = 4.79",
|
||||
" train_steps_per_second = 0.60",
|
||||
"***** train metrics end *****",
|
||||
]
|
||||
|
||||
export const mockLogContent: LogContent = {
|
||||
file: 'system-2026-02-15.log',
|
||||
size: '2.3 MB',
|
||||
content: fakeLogLines.join('\n'),
|
||||
}
|
||||
Reference in New Issue
Block a user