{
  "text": "Hello 今天咱们来聊点超有意思的AI圈新鲜事\n绝对能刷新你之前对大模型的刻板印象\n今天我们来聊聊一个有点意思的模型\nMindforge 27B\n听到名字里的27B\n你可能会觉得这不就是个小参数模型吗\n毕竟现在动辄千亿参数的AI模型满天飞\n27B顶多算个小个子\n但就是这个小个子\n在编程能力上却干翻了不少大块头\n在编程测试基准Program Bench上\n它的第一次尝试通过率\nPasit-E达到了49.51%\n比DeepSeek第四代专业版的47.80%还高\n甚至逼近了Cloud Opus 4.7的51.38%\n要知道Cloud Opus 4.7可是公认的编程高手\n而Mindforge 27B只用了1001条数据训练\n这到底是咋做到的\n咱们今天就来扒一扒它背后的技术细节\n首先得说\n这个模型的核心不在参数多\n而在数据精\n传统的大模型训练编程能力\n基本都是海量堆数据\nGitHub上几百万个项目\n几十亿行代码片段\n甚至包括各种开源库的函数定义\n注释\n用法视力\n一股脑往模型里塞\n这种训练方式确实能让模型\n学会写代码的语法\n比如怎么定义变量\n写循环\n掉裤\n但有个大问题\n它学到的是局部技能\n而不是工程思维\n就像你让一个只会被单词的人写小说\n他可能词都认识\n但写不出连贯的故事\nMindforge 27B完全反其道而行之\n它没用海量代码片段\n而是用了1001条完整开发轨迹\n啥叫完整开发轨迹\n咱们打个比方\n你让一个程序员开发一个电商订单管理系统\n传统数据可能只给他订单创建\n这个函数的代码片段\n或者库存检查的一段逻辑\n但完整开发轨迹\n是从老板提需求开始\n到程序员分析需求\n设计系统架构\n画流程图\n写代码\n测bug\n改bug\n最后上线\n整个过程每一步的对话\n思考\n代码修改\n错误反馈\n全都有记录\n具体来说\n这1001条轨迹\n平均每条有181.6轮对话\n这不是简单的问答\n而是像真实开发团队里的协作\n比如第一轮\n用户也就是需求方说\n我们需要一个订单系统\n要支持多种支付方式\n还要能实时查库存\n模型作为开发者\n不会直接写代码\n而是先问细节\n支付方式需要支持支付宝\n微信还是银行卡\n实时查库存是扣库存钱查\n还是扣库存实查\n订单取消后\n库存要不要回退\n用户回答后\n模型会接着设计架构\n我们可以分订单服务\n支付服务\n库存服务三个模块\n用消息对列结偶\n然后写代码的时候\n模型会写订单表的创建语句\n写接口定义\n写业务逻辑\n写完跑测试\n比如发现高并发时\n库存扣减不对\n模型会根据错误日志分析原因\n应该是没加分布是锁\n导致两个请求\n同时读到库存为一\n都扣了\n然后修改代码\n加锁\n再测试\n直到通过\n你看\n这181.6轮对话里\n每一轮都包含三类信息\n上下文\n之前的需求讨论\n架构设计\n动作\n写了什么代码\n改了哪里\n反馈\n测试报了什么错\n用户提了什么意见\n模型训练的时候\n就是学习这三者之间的因果链\n根据上下文\n应该做什么动作\n做了动作会得到什么反馈\n得到反馈后该怎么调整\n这比单纯学代码片段\n长什么样\n高明太多了\n他学到的是\n开发代码的全流程逻辑\n也就是工程思维\n那这些轨迹数据是怎么来的\n研究团队从开源项目的\n完整开发历史里挖出来的\n比如Github上一些知名项目的\nissue讨论区\nPolRequest的修改记录\n代码评审的评论\n甚至是开发者之间的聊天记录\n当然脱敏了\n他们把这些非结构化的数据\n整理成结构化的对话\n代码\n反馈链\n每条轨迹都像一部开发日志\n记录了一个功能\n从无到有的全过程\n比如某个轨迹\n可能是开发一个\n用户登陆健全功能\n从最开始讨论\n用JWT还是Session\n到设计Token刷新机制\n到写登陆接口代码\n到测试发现\nToken过期时间太短\n再到调整过期时间\n加刷新接口\n最后上线\n这些细节\n传统代码数据里\n根本不会保留\n接下来是训练策略\n研究团队用了\n英国语言建模\n但不是普通的英国建模\n普通建模是\n把一段文本拆成Token\n让模型预测下一个Token\n而MindForge 27B\n把轨迹拆成片段\n每个片段\n可能是用户需求\n模型回复\n代码快\n错误日志\n然后让模型学习在什么上下文下\n该生成什么类型的片段\n比如前面是用户提了一个bug反馈\n模型就该预测错误分析和代码修改片段\n而不是直接生成代码片段\n这种结构化预测让模型更理解开发流程的节奏\n还有一个关键点\n模型在训练时会模拟错误\n传统训练里\n代码数据大多是正确的\n比如开源项目的最终代码\n模型很少看到错误代码和修复过程\n但MindForge 27B的轨迹里\n包含大量错误修复的循环\n比如模型写了一段代码\n测试爆空指针异常\n然后模型会分析哪个变量可能为空\n修改代码加判空\n再测试\n可能又爆类型不匹配\n再调整\n这种试错过程\n让模型学会了如何调试\n这不是靠记住常见bug\n而是掌握了排查问题的思路\n就像一个老程序员\n不是背会了所有bug的解法\n而是掌握了排查问题的思路\n那效果为啥这么好\n咱们再看实验细节\nProgramBench是一个很严格的编程测试集\n里面全是真实的工程任务\n比如实现一个分布式缓存系统\n优化一个数据库查询\n写一个微服务的网关\n而不是简单的写个排序算法\n它的PASATD指标\n就是模型第一次生成的代码\n直接通过所有测试用力的比例\n这个指标比PASAT10\n尝试十次通过更靠谱\n因为实际开发中\n你不可能让模型试十次\n要的是第一次就尽量对\nMindforge 27B的PASAT1是49.51%\n意味着它几乎一半的任务\n第一次生成的代码就能跑通\n而Deepseek V4 Pro用了海量代码数据训练\n参数更大\n但PASAT1只有47.80%\nClaude Opus 4.7虽然是顶尖模型\n但也只比它高1.87个百分点\n这说明啥\n说明在编程这种强工程属性的任务里\n知道怎么开发\n比知道多少代码片段更重要\n更有意思的是\n研究团队做了个对比实验\n用同样的27B模型\n一组用1001条完整轨迹训练\n另一组用100万条代码片段\n相当于传统数据量训练\n结果\n轨迹训练的模型\n在Program Bench上\nPASAT1是49.51%\n而海量片段训练的模型\n只有32.7%\n差了快17个百分点\n这直接证明\n高质量的全流程数据\n比海量的局部数据有效得多\n那这对中小团队意味着啥\n以前大家觉得\n要做个能写代码的AI\n得有千亿参数\n得有几十万块GPU\n得收集TB级的数据\n中小团队根本玩不起\n但Mindforge 27B告诉我们\n不用\n你只需要花精力整理\n几百上千条高质量的开发轨迹\n用个小参数模型\n比如27B\n几张V100卡就能训练\n就能做出接近顶尖大模型的效果\n这对资源有限的团队来说\n简直是福音\n不用卷算力\n不用卷数据量\n卷数据质量就行了\n比如一个小团队想做一个\n金融代码生成的专业模型\n他们不用去爬Github上所有代码\n只需要找几个资深金融工程师\n记录他们开发风控系统\n交易接口的全过程\n整理成几百条轨迹\n就能训练出一个懂金融业务\n懂工程逻辑的模型\n这种模型虽然参数小\n但比那些啥都懂一点的大模型\n在实际金融场景里更靠谱\n最后总结一下\nMindforge 27B的成功\n其实打破了一个长期存在的迷思\n编程能力等于参数规模\n它证明\n在编程这种需要强逻辑\n强工程思维的领域\n数据的质量\n是不是全流程\n是不是包含试错过程\n是不是有完整上下文\n比数据量更重要\n就像教徒弟\n你让他看一万行代码片段\n不如让他跟着你完整做十个项目\n因为前者只能让他记住怎么写\n后者能让他学会怎么做\n未来\n可能会有更多这种小而美的专业模型出现\n他们不需要千亿参数\n只需要几千条高质量的领域轨迹\n就能在特定领域达到顶尖水平\n这对AI的普及来说是件好事\n毕竟不是每个团队都有谷歌\nOpenAI的资源\n但每个团队都可能拥有高质量的领域数据\nMindforge 27B算是给我们开了个好投",
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      "text": "Hello 今天咱们来聊点超有意思的AI圈新鲜事",
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      "text": "绝对能刷新你之前对大模型的刻板印象",
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      "text": "今天我们来聊聊一个有点意思的模型",
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      "text": "Mindforge 27B",
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      "text": "听到名字里的27B",
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      "text": "你可能会觉得这不就是个小参数模型吗",
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      "text": "毕竟现在动辄千亿参数的AI模型满天飞",
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      "text": "27B顶多算个小个子",
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      "text": "但就是这个小个子",
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    {
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      "text": "在编程能力上却干翻了不少大块头",
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      "text": "在编程测试基准Program Bench上",
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      "text": "它的第一次尝试通过率",
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      "text": "Pasit-E达到了49.51%",
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      "text": "比DeepSeek第四代专业版的47.80%还高",
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      "text": "甚至逼近了Cloud Opus 4.7的51.38%",
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      "text": "要知道Cloud Opus 4.7可是公认的编程高手",
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      "text": "而Mindforge 27B只用了1001条数据训练",
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      "text": "这到底是咋做到的",
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      "text": "咱们今天就来扒一扒它背后的技术细节",
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      "text": "首先得说",
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      "text": "这个模型的核心不在参数多",
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      "text": "而在数据精",
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      "text": "传统的大模型训练编程能力",
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      "text": "基本都是海量堆数据",
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      "text": "GitHub上几百万个项目",
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      "text": "几十亿行代码片段",
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      "text": "甚至包括各种开源库的函数定义",
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      "text": "注释",
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      "text": "用法视力",
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      "text": "一股脑往模型里塞",
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      "text": "这种训练方式确实能让模型",
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      "text": "学会写代码的语法",
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      "text": "比如怎么定义变量",
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      "text": "写循环",
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      "text": "掉裤",
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      "text": "但有个大问题",
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      "text": "它学到的是局部技能",
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      "text": "而不是工程思维",
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      "text": "就像你让一个只会被单词的人写小说",
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      "text": "但写不出连贯的故事",
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      "text": "Mindforge 27B完全反其道而行之",
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      "text": "它没用海量代码片段",
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      "text": "而是用了1001条完整开发轨迹",
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      "text": "啥叫完整开发轨迹",
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      "text": "咱们打个比方",
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      "text": "你让一个程序员开发一个电商订单管理系统",
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      "text": "这个函数的代码片段",
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      "text": "或者库存检查的一段逻辑",
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      "text": "但完整开发轨迹",
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      "text": "是从老板提需求开始",
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      "text": "画流程图",
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      "text": "具体来说",
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      "text": "这1001条轨迹",
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      "text": "这不是简单的问答",
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      "text": "而是像真实开发团队里的协作",
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      "text": "模型作为开发者",
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      "text": "用户回答后",
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      "text": "我们可以分订单服务",
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      "text": "支付服务",
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      "text": "库存服务三个模块",
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      "text": "然后写代码的时候",
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      "text": "写完跑测试",
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      "text": "导致两个请求",
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      "text": "都扣了",
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      "text": "然后修改代码",
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      "text": "再测试",
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      "text": "直到通过",
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      "text": "你看",
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      "text": "这181.6轮对话里",
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      "text": "每一轮都包含三类信息",
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      "text": "上下文",
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      "text": "之前的需求讨论",
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      "text": "动作",
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      "text": "写了什么代码",
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      "text": "改了哪里",
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      "text": "反馈",
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      "text": "测试报了什么错",
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      "text": "用户提了什么意见",
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      "text": "就是学习这三者之间的因果链",
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      "text": "根据上下文",
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      "text": "应该做什么动作",
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      "text": "做了动作会得到什么反馈",
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      "text": "这比单纯学代码片段",
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      "text": "长什么样",
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      "text": "高明太多了",
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      "text": "他学到的是",
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      "text": "开发代码的全流程逻辑",
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      "text": "也就是工程思维",
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      "text": "那这些轨迹数据是怎么来的",
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    {
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      "text": "研究团队从开源项目的",
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    {
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      "text": "完整开发历史里挖出来的",
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    {
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      "text": "比如Github上一些知名项目的",
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      "text": "issue讨论区",
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    {
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      "text": "PolRequest的修改记录",
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      "text": "代码评审的评论",
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    {
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      "text": "甚至是开发者之间的聊天记录",
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    {
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      "text": "当然脱敏了",
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    {
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      "text": "他们把这些非结构化的数据",
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    {
      "id": 139,
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      "text": "整理成结构化的对话",
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    {
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      "text": "代码",
      "tokens": [
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      "no_speech_prob": 1.878600400240238e-11,
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    {
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      "text": "反馈链",
      "tokens": [
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    },
    {
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      "text": "每条轨迹都像一部开发日志",
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    {
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      "text": "记录了一个功能",
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      "no_speech_prob": 1.878600400240238e-11,
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    {
      "id": 144,
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      "text": "从无到有的全过程",
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    {
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      "text": "比如某个轨迹",
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      "no_speech_prob": 1.878600400240238e-11,
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    {
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      "text": "可能是开发一个",
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    {
      "id": 147,
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      "text": "用户登陆健全功能",
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    {
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      "text": "从最开始讨论",
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    {
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      "text": "用JWT还是Session",
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    },
    {
      "id": 150,
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      "text": "到设计Token刷新机制",
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    {
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      "text": "到写登陆接口代码",
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    {
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      "text": "到测试发现",
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    {
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      "text": "Token过期时间太短",
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    {
      "id": 154,
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      "text": "再到调整过期时间",
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    {
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      "text": "加刷新接口",
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    {
      "id": 156,
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      "text": "最后上线",
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    {
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      "text": "这些细节",
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    {
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      "text": "传统代码数据里",
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    {
      "id": 159,
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      "text": "根本不会保留",
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    {
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      "text": "接下来是训练策略",
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    {
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      "text": "研究团队用了",
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    {
      "id": 162,
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      "text": "英国语言建模",
      "tokens": [
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      "temperature": 0,
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    },
    {
      "id": 163,
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      "start": 262.2,
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      "text": "但不是普通的英国建模",
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      "temperature": 0,
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    {
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      "text": "普通建模是",
      "tokens": [
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      "compression_ratio": 1.2450331125827814,
      "no_speech_prob": 1.9608425994022127e-11,
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    {
      "id": 165,
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      "start": 265.3,
      "end": 266.94,
      "text": "把一段文本拆成Token",
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      "temperature": 0,
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    {
      "id": 166,
      "seek": 24958,
      "start": 266.94,
      "end": 268.90000000000003,
      "text": "让模型预测下一个Token",
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      "temperature": 0,
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    {
      "id": 167,
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      "text": "而MindForge 27B",
      "tokens": [
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    {
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      "text": "把轨迹拆成片段",
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    {
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      "text": "每个片段",
      "tokens": [
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      "temperature": 0,
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    {
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      "text": "可能是用户需求",
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    {
      "id": 171,
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      "text": "模型回复",
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    {
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      "text": "代码快",
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    {
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      "text": "错误日志",
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    {
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      "text": "然后让模型学习在什么上下文下",
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      "text": "该生成什么类型的片段",
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    {
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      "text": "比如前面是用户提了一个bug反馈",
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      "text": "模型就该预测错误分析和代码修改片段",
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    {
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      "text": "而不是直接生成代码片段",
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    {
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      "text": "这种结构化预测让模型更理解开发流程的节奏",
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      "text": "还有一个关键点",
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      "text": "模型在训练时会模拟错误",
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      "text": "传统训练里",
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      "temperature": 0,
      "avg_logprob": -0.07240762758017773,
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      "text": "代码数据大多是正确的",
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      "text": "比如开源项目的最终代码",
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      "temperature": 0,
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      "text": "模型很少看到错误代码和修复过程",
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    {
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      "text": "但MindForge 27B的轨迹里",
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      "temperature": 0,
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      "compression_ratio": 1.3395638629283488,
      "no_speech_prob": 1.8017750486043482e-11,
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    {
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      "text": "包含大量错误修复的循环",
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      "temperature": 0,
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    {
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      "text": "比如模型写了一段代码",
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    {
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      "text": "测试爆空指针异常",
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    {
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      "text": "然后模型会分析哪个变量可能为空",
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      "text": "修改代码加判空",
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    {
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      "text": "再测试",
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    {
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      "text": "可能又爆类型不匹配",
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    {
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      "text": "再调整",
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      "no_speech_prob": 1.8017750486043482e-11,
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    {
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      "text": "这种试错过程",
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      "no_speech_prob": 1.8017750486043482e-11,
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    {
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      "text": "让模型学会了如何调试",
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    {
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      "text": "这不是靠记住常见bug",
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    {
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      "text": "而是掌握了排查问题的思路",
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    },
    {
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      "text": "就像一个老程序员",
      "tokens": [
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      "temperature": 0,
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    {
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      "text": "不是背会了所有bug的解法",
      "tokens": [
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    {
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      "text": "而是掌握了排查问题的思路",
      "tokens": [
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      "no_speech_prob": 1.8017750486043482e-11,
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    {
      "id": 202,
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      "text": "那效果为啥这么好",
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    {
      "id": 203,
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      "start": 334.78,
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      "text": "咱们再看实验细节",
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    {
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      "text": "ProgramBench是一个很严格的编程测试集",
      "tokens": [
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    {
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      "text": "里面全是真实的工程任务",
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    {
      "id": 206,
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      "text": "比如实现一个分布式缓存系统",
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      "temperature": 0,
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    {
      "id": 207,
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      "text": "优化一个数据库查询",
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    {
      "id": 208,
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      "text": "写一个微服务的网关",
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    {
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      "text": "而不是简单的写个排序算法",
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    {
      "id": 210,
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      "text": "它的PASATD指标",
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    {
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      "text": "就是模型第一次生成的代码",
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      "text": "直接通过所有测试用力的比例",
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      "text": "这个指标比PASAT10",
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    {
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      "text": "尝试十次通过更靠谱",
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    {
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      "text": "因为实际开发中",
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    {
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      "text": "你不可能让模型试十次",
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    {
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      "text": "要的是第一次就尽量对",
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    {
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      "text": "Mindforge 27B的PASAT1是49.51%",
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    {
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      "text": "意味着它几乎一半的任务",
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    {
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      "text": "第一次生成的代码就能跑通",
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    {
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      "text": "而Deepseek V4 Pro用了海量代码数据训练",
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    {
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      "text": "参数更大",
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      "text": "但PASAT1只有47.80%",
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    {
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      "text": "Claude Opus 4.7虽然是顶尖模型",
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    {
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      "text": "但也只比它高1.87个百分点",
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      "text": "这说明啥",
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      "text": "说明在编程这种强工程属性的任务里",
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    {
      "id": 228,
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      "text": "知道怎么开发",
      "tokens": [
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    {
      "id": 229,
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      "text": "比知道多少代码片段更重要",
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      "temperature": 0,
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    {
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      "text": "更有意思的是",
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      "compression_ratio": 1.25,
      "no_speech_prob": 1.4515293481065505e-11,
      "source": "whisper"
    },
    {
      "id": 231,
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      "text": "研究团队做了个对比实验",
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      "temperature": 0,
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    {
      "id": 232,
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      "start": 396.67999999999995,
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      "text": "用同样的27B模型",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.08772536829898232,
      "compression_ratio": 1.25,
      "no_speech_prob": 1.4515293481065505e-11,
      "source": "whisper"
    },
    {
      "id": 233,
      "seek": 38628,
      "start": 398.53999999999996,
      "end": 401.28,
      "text": "一组用1001条完整轨迹训练",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.4515293481065505e-11,
      "source": "whisper"
    },
    {
      "id": 234,
      "seek": 38628,
      "start": 401.28,
      "end": 403.73999999999995,
      "text": "另一组用100万条代码片段",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.08772536829898232,
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    },
    {
      "id": 235,
      "seek": 38628,
      "start": 403.73999999999995,
      "end": 405.67999999999995,
      "text": "相当于传统数据量训练",
      "tokens": [
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      "temperature": 0,
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      "source": "whisper"
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    {
      "id": 236,
      "seek": 38628,
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      "end": 406.32,
      "text": "结果",
      "tokens": [
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      "no_speech_prob": 1.4515293481065505e-11,
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    {
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      "text": "轨迹训练的模型",
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      "id": 238,
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      "text": "在Program Bench上",
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      "text": "PASAT1是49.51%",
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      "text": "而海量片段训练的模型",
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      "temperature": 0,
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      "text": "只有32.7%",
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    {
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      "text": "差了快17个百分点",
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      "text": "这直接证明",
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      "text": "高质量的全流程数据",
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      "text": "比海量的局部数据有效得多",
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      "text": "那这对中小团队意味着啥",
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      "text": "以前大家觉得",
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      "text": "要做个能写代码的AI",
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    {
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      "text": "得有千亿参数",
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      "text": "得有几十万块GPU",
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      "text": "得收集TB级的数据",
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      "text": "中小团队根本玩不起",
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      "text": "但Mindforge 27B告诉我们",
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      "text": "不用",
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      "text": "你只需要花精力整理",
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      "text": "几百上千条高质量的开发轨迹",
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      "text": "比如27B",
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      "text": "几张V100卡就能训练",
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      "text": "就能做出接近顶尖大模型的效果",
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      "text": "这对资源有限的团队来说",
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      "text": "简直是福音",
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      "text": "不用卷算力",
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      "text": "不用卷数据量",
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      "text": "卷数据质量就行了",
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      "text": "比如一个小团队想做一个",
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      "text": "金融代码生成的专业模型",
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      "text": "他们不用去爬Github上所有代码",
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      "text": "只需要找几个资深金融工程师",
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      "text": "记录他们开发风控系统",
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    {
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      "text": "交易接口的全过程",
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    {
      "id": 272,
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      "text": "整理成几百条轨迹",
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    {
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      "text": "就能训练出一个懂金融业务",
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