{
  "text": "我們現在看到的這個就是CM3.827B精華版。它的特點就是模型體積被壓縮的很小,但是它的性能或者智商並沒有下降。\n就是普通電腦都能運行它,然後它一個月是有82萬的下載將近。\n我們來看一下它的文件。它的文件都是很小的,它是被壓縮了,但是它的智商或者性能並沒有被壓縮的。\n可以看到它最大是12GB\n然后最小就只有8GB\n然后普通电脑也能够运营它\n然后它是支持多模态的\n下面一个多模态文件\n它是能够解析图片的\n然后我已经把它安装到我的电脑上\n然后安装到Hermes的Engine里面去\n然后我们可以看到这边\n这个模型就是13.827B\n然后GSQRCO就是精华版\n我们也问他一些问题\n比如说你好你是什么大模型\n我给他发送\n他回路了他说我是Hermeson\n然后他模型是千万3.827B\n然后我再让他识别一张图片内容\n因为他是支持多模态的\n让他识别这一张图片吧\n那么可见他识别的内容是千万大模型发布宣传横幅\n然后左偷的文图是千万3.827B\n然后进化版来了\n然后小铁铁铁不降至多模型多模态\n然后他是都能够分析出来的\n然后背景色啥啥都是非常准确的\n然后我再让他写一个ATMARO\n看它的效果怎么样\n内容是X超哥的简单单页\n我们可以看到它已经在桌面上创建了\n让我们双击打开它看一下效果\n这就是一个很简单单页\n做的效果还是不错的\n非常漂亮\n大家好我是超哥\n今天给大家分享CM3.827B\nGSQ加RCO AI模型本地步骤教程\n它最大的特点就是模型体积小\n能力强运行美感低\n在保持模型能力的同时\n大幅压缩模型体积\n让普通电脑也有机会进行27B大模型\n同时还支持131K上下文\n多茅态图像理解和MTP加速技术\n还可以接入Hermes Engine\n下面开始教程\n我也把所有用的材料\n放到我的博客的网址里\n我们首先来看一下我这个电脑环境\n大家可以参考一下\n下面第一步我们是需要下载大模型\n然后我提供了三个下载地址\n第一个是官方的\n我们可以看一下\n这个是官方的\n点开\n进来之后这就是它的所有的模型都在这了\n然后如果你这个网址打不开的话\n也可以用夸克跟蓄力网牌下载\n我也都放到里面去了\n让我们开看一下\n这是它的大模型\n然后这边有它的介绍\n我们点这里\n可以切换到它的官方介绍\n这是它的官方介绍\n让我们再点这个Fuse\n就是它的文件\n模型文件了\n那就这些\n我们可以看一下上面是具体的模型\n这个带-MTP的\n它是有加速技术的\n它不带-MTP\n它就是没有加速技术\n它会小一点点\n有小一点点而已\n我们尽量是用带加速的\n带-MTP的\n然后最下面这个是多模态文件\n就是理解图像的\n这边有我写了一个具体的选择\n你根据自己的电脑情况进行选择就行了\n然后就下载\n先我们一定要把这个多模态下载\n不然它这个多么太迟前不了的\n点这个下载\n然后我就下载第一个吧\n这边下一个因为只是做演示\n然后我们等它下载完\n它是比较大的\n然后下载的时候\n我来推做一个小广告\n然后我的X超和推特\n希望大家能关注一下\n打开看一下\n这我的推特账号\n有推特的可以关注一下\n模型已经下载好了\n我们再来到我的博客\n然后第二步是我们需要下载并配置\n这是Lama.cpp\n然后这是Lama.cpp的下载地址\n也是三个\n第一个是官方的Headahub下载地址\n第二个是Quack给迅雷的\n如果是Headahub打不开的话\n可以用Quack网盘跟迅雷网盘进行下载\n我就用这个官方的\n点击打开\n打开之后\n我们点击这个进行的版本号\n点击这个版本号之后\n这就是它的下载链接\n我们可以看到这边是有MacOS版本\n然后有Winux版本\n因为我是Windows我就下载Windows的\n然后它是有很多电接的Windows的\n我们可以对比一下\n这边是Windows的各个不同的版本的对应\n然后给你自己的显卡跟CPU进行选择\n因为我是英伟达显卡\n我下这两个就行了\n我选择一个就行了\n然后这个13的\ncoda13\n然后这个要下载\n然后后面这个DLS也要下载\n点击下载\n我们可以看到已经下载好了\n这两个压缩包\n一个是前面的正式的程序\n后面是dlls\n这两个都要下载的前后两个\n我们可以打开看一下\n然后这是两个模型\n一个多模态\n一个正常的模型文件\n这两个就是lama.cpp的程序\n然后先把这个两个压缩包都给它解压出来\n然后打开看一下\n这个是dll的\n然后给它剪切\n复制剪切到我们的LAMA主程序里面来\n这个只要它的主程序给它剪切过来\n这个是已经空了\n然后这给它删了吧\n这两个压缩包给它删了\n这个就是LAMA的主程序\n然后我给它命名一下吧\n这个太长了\n然后现在就好了\n我们就先不要管它\n然后把这两个给它关掉\n然后我们再看下面\n下面是需要把模型\n接入到lama.cpp里面\n我们才能运行它\n是把下载的大模型放到lama.cpp程序的game目录下的modus目录\n然后这个目录我们是需要自行创建的\n我们先来到lama.cpp的程序的game目录下\n这个就是刚刚我命名的这个\n然后打开它就是game目录\n然后我们需要创建一个文件夹\n文件夹就是modus文件夹\n然后我们复制一下名字\n然后给它点击空白处UG\n给它清点一个文件夹\n明天给大家粘贴进去\n就这个目录我们已经创建好了\n然后我们需要把大模型放到这个目录里面来\n然后给大家来到这边\n剪切这两个模型\n一个多模的一个正式的\n然后给它剪切到这个modus目录来就行了\n然后这个LAMA主程序我们放哪都行啊\n随便放\n只要你的盘够用就行\n下面我们需要配置启动脚本\nlama.cpp gamelo创建启动脚本\n命名成启动.bat\n也可以命名成1.bat都行\n然后我们需要创建这个启动脚本\n然后我先复制这个名字吧\n启动.bat 复制\n然后来到lama主程序来\n然后右击空白处\n然后给它新建一个文本文档先\n然后我们把这个文本文档给它重义命名一下\n后面的.tlt也要给它删了\n上讲之后我们把启动点BAT给它粘贴进来\n这就是一个BAT文件了\n让给它确定一下\n现在已经改好了\n这边前面变成一个小尺\n说明它就是命名成功了\n让我们右击\n右击它用文明打开\n我们还要均匀修改\n打开入里面是啥都没有的\n是空白的\n我们需要把我们的这个\n启动脚本给它复制粘贴进去\n让我们点击这边的copy\n让给它粘贴过来\n监贴到启动.bat里面来\n这就是它的启动脚本\n我们可以看一下这边\n这里\n其实我这边也有写的\n这边是代码说明\n有一个模型名是我们要改的\n这个端口号\n如果你本身端口号\n跟他自跟我这个启动脚本的端口号冲突的话\n你要改一下\n然后-c就是上下文长度\n我们来到启动脚本这边\n这个就是模型名称\n我们要改成我们自己的模型名称\n你要使用的模型名称\n比如说我刚刚下载的是\n然后把这个模型名给它删了\n把真正的模型名称给它复制过来就行了\n然后后面这个是多模态文件\n这个我们就不要动它了\n多模态\n但是你一定要下载这个多模态才行\n这个文件\n然后后面这个\n这个是我们的端口号8085\n如果你冲突的话\n就是你改一下别的\n如果不冲突\n你就默然用8085就行了\n然后后面这个是上下文长度\n现在我默认给它是6万\n如果你觉得小你可以自己改再改大一些就行了\n让我们现在给它改好了\n给它保存就行了\n保存之后\n我们就可以启动它了\n我们来到拉玛主程序\n然后启动点BAT\n这个我们双击打开进行启动\n我们可以看一下已经启动成功了\n当看到这个段号后面有127的0.0.1的时候就启动成功了\n然后我们按住键盘左下角的Kintry键\n然后再点击这个链接就能打开了\n你也可以在我这边点击这个链接是一样的\n这个端号要对\n要是你自己的才行\n点击一样可以打开的\n但是我测试一下\n比如说八个你好\n你是什么大模型\n他说我签位\n然后速度是50tok每秒\n然后这个是\n这边是有个句子的模型信息的\n这句子的模型信息\n这个就是我们现在正在用的\n它是支持多模态的\n因为我们有多模态文件在里面\n比如我上传个图片给他\n添加个图片\n然后识别图片内容\n我们可以看到它运行完了\n它是每秒也是50Token\n然后这是它识别到的图片内容\n是比较准确的\n它是能够识别图片的\n这是多模态\n然后我们这个可以给它关掉\n因为我们要对接到Hermes的A进里面去\n然后我说一下这个中端是不能关掉的\n你关掉它就没办法用了\n因为它靠这个中端的\n如果你重启电脑之后\n你也要再开启这个中端才能用\n就是点击微软起动点BAT就行了\n让我们现在看一下紫模对接到荷免声音镜里面去\n荷免声音镜下载地址是这个\n我们可以点开\n点开之后\n点击这个下载就行了\n这个是他默认显示的就是windows\n点击\n进行下载\n这边我允许一下\n他比较小的已经下载好了\n让我点击进行安装\n打开之后我们点这边的安装就行了\n我们可以看到他已经装好了\n让我们点这边的开始\n现在已经好了\n我们点这边的稍后再选择提供方\n先给它打开\n然后打开之后我们把这个给它关掉\n关掉之后我们下面要开始配置它了\n然后再打开HermicEinge\n然后点这边的齿轮打开\n打开之后我们选择\n左边这边有个提供方点击\n点击之后下面有一个APM秘钥点击\n点击之后我们找到下面下面有一个OpenAI\n我们往阿拉找一下\n这个有个openaiapi点击\n点击之后这有两个要填\n一个是密钥\n另外一个就是url\n这个密钥我们就随便填就行了\n因为它是本地模型\n12345\n然后保存\nurl我们用这个\n这个url\n我博客这边的\n然后这个8085\n你就\n你没改的话就是8085\n你改了端口号的话\n你就是你自己改的那个端口号\n我们复制一下\n然后给它粘贴过来\n然后点保存\n现在就已经保存成功了\n让我们点这边的模型\n然后这边有一个选择\n现在是自动\n然后我们点一下\n这边有一个open AI\n选中它\n然后这我们点应用\n现在保错\n然后我们选一下这个模型\n下面有我们刚刚的这个选择的模型\n然后选中它\n7213.827B\n然后再点应用\n然后现在就成功了\n我们给它关掉\n关掉之后我们可以看到这边对话框这边\n默认就选中了723.827BGSQRCO这个模型\n我们问一下看它能不能用\n比如说发个你好\n你是什么模型\n我们可以看到它回复了\n然后它说明它是能够正常连上的\n然后我们再选择一张图片\n因为它这是多模态的\n看它能不能识别到图片\n还是选择这张图吧\n我们可以看到他已经分析完了\n他说他是主题是7W3.827B\n他之前是太大了\n然后他自动给压缩了\n这Hermicene Geek还是挺智能的\n然后我提醒一下大家\n大家用的时候不管用Hermicene Geek\n还是他这个\n还是用这个网址进行使用这个模型\n我们都不要把这个终端给关掉\n你关掉就没办法用了\n好吧\n今天这段就到这吧\n如果大家有什么问题的话\n可以在视频下方的频道去留言交流\n谢谢大家的观看",
  "language": "zh",
  "duration": 819.01425,
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    {
      "start": 0.0,
      "end": 11.0,
      "text": "我們現在看到的這個就是CM3.827B精華版。它的特點就是模型體積被壓縮的很小,但是它的性能或者智商並沒有下降。",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.19703535600142044,
      "no_speech_prob": 9.531210022617476e-12,
      "compression_ratio": 1.4147286821705427
    },
    {
      "start": 11.0,
      "end": 16.0,
      "text": "就是普通電腦都能運行它,然後它一個月是有82萬的下載將近。",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.19703535600142044,
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      "start": 16.0,
      "end": 24.0,
      "text": "我們來看一下它的文件。它的文件都是很小的,它是被壓縮了,但是它的智商或者性能並沒有被壓縮的。",
      "chunk": 0,
      "language": "zh",
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      "start": 24.0,
      "end": 26.26,
      "text": "可以看到它最大是12GB",
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      "text": "然后最小就只有8GB",
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      "start": 28.6,
      "end": 30.36,
      "text": "然后普通电脑也能够运营它",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.27463569641113283,
      "no_speech_prob": 3.0205254963888706e-11,
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      "start": 30.36,
      "end": 31.76,
      "text": "然后它是支持多模态的",
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      "end": 33.14,
      "text": "下面一个多模态文件",
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      "text": "它是能够解析图片的",
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      "start": 34.76,
      "end": 37.96,
      "text": "然后我已经把它安装到我的电脑上",
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      "end": 40.06,
      "text": "然后安装到Hermes的Engine里面去",
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      "text": "然后我们可以看到这边",
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      "text": "这个模型就是13.827B",
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      "text": "然后GSQRCO就是精华版",
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      "text": "我们也问他一些问题",
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      "text": "比如说你好你是什么大模型",
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      "start": 50.94,
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      "text": "我给他发送",
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      "text": "他回路了他说我是Hermeson",
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      "start": 55.7,
      "end": 57.8,
      "text": "然后他模型是千万3.827B",
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      "start": 57.8,
      "end": 61.1,
      "text": "然后我再让他识别一张图片内容",
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      "text": "因为他是支持多模态的",
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      "start": 62.8,
      "end": 64.7,
      "text": "让他识别这一张图片吧",
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      "start": 68.3,
      "end": 72.0,
      "text": "那么可见他识别的内容是千万大模型发布宣传横幅",
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      "start": 72.0,
      "end": 75.4,
      "text": "然后左偷的文图是千万3.827B",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.27057290227158265,
      "no_speech_prob": 2.068591131720865e-11,
      "compression_ratio": 1.426356589147287
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    {
      "start": 75.4,
      "end": 76.5,
      "text": "然后进化版来了",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.27057290227158265,
      "no_speech_prob": 2.068591131720865e-11,
      "compression_ratio": 1.426356589147287
    },
    {
      "start": 76.5,
      "end": 79.3,
      "text": "然后小铁铁铁不降至多模型多模态",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.27057290227158265,
      "no_speech_prob": 2.068591131720865e-11,
      "compression_ratio": 1.426356589147287
    },
    {
      "start": 79.3,
      "end": 81.5,
      "text": "然后他是都能够分析出来的",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.27057290227158265,
      "no_speech_prob": 2.068591131720865e-11,
      "compression_ratio": 1.426356589147287
    },
    {
      "start": 81.5,
      "end": 83.74,
      "text": "然后背景色啥啥都是非常准确的",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.2596433484876478,
      "no_speech_prob": 2.358623192588727e-11,
      "compression_ratio": 1.3172804532577904
    },
    {
      "start": 84.26,
      "end": 85.74,
      "text": "然后我再让他写一个ATMARO",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.2596433484876478,
      "no_speech_prob": 2.358623192588727e-11,
      "compression_ratio": 1.3172804532577904
    },
    {
      "start": 85.74,
      "end": 86.94,
      "text": "看它的效果怎么样",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.2596433484876478,
      "no_speech_prob": 2.358623192588727e-11,
      "compression_ratio": 1.3172804532577904
    },
    {
      "start": 86.94,
      "end": 89.06,
      "text": "内容是X超哥的简单单页",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.2596433484876478,
      "no_speech_prob": 2.358623192588727e-11,
      "compression_ratio": 1.3172804532577904
    },
    {
      "start": 89.34,
      "end": 91.3,
      "text": "我们可以看到它已经在桌面上创建了",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.2596433484876478,
      "no_speech_prob": 2.358623192588727e-11,
      "compression_ratio": 1.3172804532577904
    },
    {
      "start": 91.3,
      "end": 93.26,
      "text": "让我们双击打开它看一下效果",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.2596433484876478,
      "no_speech_prob": 2.358623192588727e-11,
      "compression_ratio": 1.3172804532577904
    },
    {
      "start": 93.26,
      "end": 94.74,
      "text": "这就是一个很简单单页",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.2596433484876478,
      "no_speech_prob": 2.358623192588727e-11,
      "compression_ratio": 1.3172804532577904
    },
    {
      "start": 95.06,
      "end": 96.1,
      "text": "做的效果还是不错的",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.2596433484876478,
      "no_speech_prob": 2.358623192588727e-11,
      "compression_ratio": 1.3172804532577904
    },
    {
      "start": 96.1,
      "end": 96.74,
      "text": "非常漂亮",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.2596433484876478,
      "no_speech_prob": 2.358623192588727e-11,
      "compression_ratio": 1.3172804532577904
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    {
      "start": 97.62,
      "end": 98.5,
      "text": "大家好我是超哥",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.2596433484876478,
      "no_speech_prob": 2.358623192588727e-11,
      "compression_ratio": 1.3172804532577904
    },
    {
      "start": 98.5,
      "end": 100.94,
      "text": "今天给大家分享CM3.827B",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.2596433484876478,
      "no_speech_prob": 2.358623192588727e-11,
      "compression_ratio": 1.3172804532577904
    },
    {
      "start": 101.18,
      "end": 104.34,
      "text": "GSQ加RCO AI模型本地步骤教程",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.2596433484876478,
      "no_speech_prob": 2.358623192588727e-11,
      "compression_ratio": 1.3172804532577904
    },
    {
      "start": 104.58,
      "end": 106.62,
      "text": "它最大的特点就是模型体积小",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.2596433484876478,
      "no_speech_prob": 2.358623192588727e-11,
      "compression_ratio": 1.3172804532577904
    },
    {
      "start": 106.62,
      "end": 108.26,
      "text": "能力强运行美感低",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.2596433484876478,
      "no_speech_prob": 2.358623192588727e-11,
      "compression_ratio": 1.3172804532577904
    },
    {
      "start": 108.62,
      "end": 110.3,
      "text": "在保持模型能力的同时",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.2596433484876478,
      "no_speech_prob": 2.358623192588727e-11,
      "compression_ratio": 1.3172804532577904
    },
    {
      "start": 110.3,
      "end": 111.9,
      "text": "大幅压缩模型体积",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.23577242119367733,
      "no_speech_prob": 2.636280256596013e-11,
      "compression_ratio": 1.2713864306784661
    },
    {
      "start": 112.14,
      "end": 114.9,
      "text": "让普通电脑也有机会进行27B大模型",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.23577242119367733,
      "no_speech_prob": 2.636280256596013e-11,
      "compression_ratio": 1.2713864306784661
    },
    {
      "start": 115.1,
      "end": 117.62,
      "text": "同时还支持131K上下文",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.23577242119367733,
      "no_speech_prob": 2.636280256596013e-11,
      "compression_ratio": 1.2713864306784661
    },
    {
      "start": 117.62,
      "end": 120.26,
      "text": "多茅态图像理解和MTP加速技术",
      "chunk": 0,
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      "avg_logprob": -0.23577242119367733,
      "no_speech_prob": 2.636280256596013e-11,
      "compression_ratio": 1.2713864306784661
    },
    {
      "start": 120.54,
      "end": 121.94,
      "text": "还可以接入Hermes Engine",
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      "no_speech_prob": 2.636280256596013e-11,
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    {
      "start": 121.94,
      "end": 123.1,
      "text": "下面开始教程",
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    {
      "start": 123.46,
      "end": 124.54,
      "text": "我也把所有用的材料",
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    {
      "start": 124.54,
      "end": 126.02,
      "text": "放到我的博客的网址里",
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    {
      "start": 126.46,
      "end": 129.62,
      "text": "我们首先来看一下我这个电脑环境",
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    },
    {
      "start": 129.98,
      "end": 131.58,
      "text": "大家可以参考一下",
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      "no_speech_prob": 2.636280256596013e-11,
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    },
    {
      "start": 132.26,
      "end": 134.78,
      "text": "下面第一步我们是需要下载大模型",
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    },
    {
      "start": 134.78,
      "end": 137.14,
      "text": "然后我提供了三个下载地址",
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    {
      "start": 137.14,
      "end": 138.26,
      "text": "第一个是官方的",
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    {
      "start": 138.26,
      "end": 139.1,
      "text": "我们可以看一下",
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    {
      "start": 139.1,
      "end": 140.64,
      "text": "这个是官方的",
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    {
      "start": 140.64,
      "end": 141.92,
      "text": "点开",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.27088647959183676,
      "no_speech_prob": 2.1840279584850464e-11,
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    {
      "start": 141.92,
      "end": 145.76,
      "text": "进来之后这就是它的所有的模型都在这了",
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    },
    {
      "start": 145.76,
      "end": 148.3,
      "text": "然后如果你这个网址打不开的话",
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      "avg_logprob": -0.27088647959183676,
      "no_speech_prob": 2.1840279584850464e-11,
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    {
      "start": 148.3,
      "end": 150.66,
      "text": "也可以用夸克跟蓄力网牌下载",
      "chunk": 0,
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      "avg_logprob": -0.27088647959183676,
      "no_speech_prob": 2.1840279584850464e-11,
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    {
      "start": 150.66,
      "end": 152.06,
      "text": "我也都放到里面去了",
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    {
      "start": 152.06,
      "end": 154.22,
      "text": "让我们开看一下",
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      "no_speech_prob": 2.1840279584850464e-11,
      "compression_ratio": 1.5095057034220531
    },
    {
      "start": 154.22,
      "end": 155.28,
      "text": "这是它的大模型",
      "chunk": 0,
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      "avg_logprob": -0.27088647959183676,
      "no_speech_prob": 2.1840279584850464e-11,
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    {
      "start": 155.28,
      "end": 156.8,
      "text": "然后这边有它的介绍",
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    {
      "start": 156.8,
      "end": 157.52,
      "text": "我们点这里",
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      "compression_ratio": 1.5095057034220531
    },
    {
      "start": 157.52,
      "end": 159.3,
      "text": "可以切换到它的官方介绍",
      "chunk": 0,
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      "avg_logprob": -0.27088647959183676,
      "no_speech_prob": 2.1840279584850464e-11,
      "compression_ratio": 1.5095057034220531
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    {
      "start": 159.3,
      "end": 161.14,
      "text": "这是它的官方介绍",
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      "compression_ratio": 1.5095057034220531
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    {
      "start": 161.14,
      "end": 163.68,
      "text": "让我们再点这个Fuse",
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    {
      "start": 163.68,
      "end": 164.92,
      "text": "就是它的文件",
      "chunk": 0,
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    {
      "start": 164.92,
      "end": 165.76,
      "text": "模型文件了",
      "chunk": 0,
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    {
      "start": 165.76,
      "end": 166.82,
      "text": "那就这些",
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    {
      "start": 166.82,
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      "text": "我们可以看一下上面是具体的模型",
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    {
      "start": 169.5,
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      "text": "这个带-MTP的",
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    {
      "start": 171.56,
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      "text": "它是有加速技术的",
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    {
      "start": 172.74,
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      "text": "它不带-MTP",
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    {
      "start": 174.12,
      "end": 175.22,
      "text": "它就是没有加速技术",
      "chunk": 0,
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    {
      "start": 175.22,
      "end": 176.08,
      "text": "它会小一点点",
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    {
      "start": 176.08,
      "end": 177.18,
      "text": "有小一点点而已",
      "chunk": 0,
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    {
      "start": 177.18,
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      "text": "我们尽量是用带加速的",
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    {
      "start": 179.02,
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      "text": "带-MTP的",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.09816761243911017,
      "no_speech_prob": 2.0690215166152548e-11,
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    },
    {
      "start": 180.52,
      "end": 183.22,
      "text": "然后最下面这个是多模态文件",
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    {
      "start": 183.22,
      "end": 184.32,
      "text": "就是理解图像的",
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    {
      "start": 184.32,
      "end": 189.2,
      "text": "这边有我写了一个具体的选择",
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      "no_speech_prob": 2.0690215166152548e-11,
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    {
      "start": 189.2,
      "end": 191.66,
      "text": "你根据自己的电脑情况进行选择就行了",
      "chunk": 0,
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    {
      "start": 191.66,
      "end": 193.38,
      "text": "然后就下载",
      "chunk": 0,
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    {
      "start": 193.38,
      "end": 195.82,
      "text": "先我们一定要把这个多模态下载",
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    {
      "start": 195.82,
      "end": 197.46,
      "text": "不然它这个多么太迟前不了的",
      "chunk": 0,
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      "compression_ratio": 1.525
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    {
      "start": 197.46,
      "end": 198.42,
      "text": "点这个下载",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.17639295516475553,
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    {
      "start": 198.42,
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      "text": "然后我就下载第一个吧",
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    {
      "start": 201.34,
      "end": 203.02,
      "text": "这边下一个因为只是做演示",
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    },
    {
      "start": 203.02,
      "end": 205.72,
      "text": "然后我们等它下载完",
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    {
      "start": 205.72,
      "end": 206.38,
      "text": "它是比较大的",
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    },
    {
      "start": 206.38,
      "end": 208.08,
      "text": "然后下载的时候",
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    },
    {
      "start": 208.08,
      "end": 209.88,
      "text": "我来推做一个小广告",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.17639295516475553,
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    },
    {
      "start": 209.88,
      "end": 211.12,
      "text": "然后我的X超和推特",
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    },
    {
      "start": 211.12,
      "end": 212.34,
      "text": "希望大家能关注一下",
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      "avg_logprob": -0.17639295516475553,
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    {
      "start": 212.34,
      "end": 213.62,
      "text": "打开看一下",
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    {
      "start": 213.62,
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      "text": "这我的推特账号",
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    },
    {
      "start": 215.9,
      "end": 217.28,
      "text": "有推特的可以关注一下",
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      "avg_logprob": -0.17639295516475553,
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    {
      "start": 217.28,
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      "text": "模型已经下载好了",
      "chunk": 0,
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      "avg_logprob": -0.17639295516475553,
      "no_speech_prob": 1.860738646497495e-11,
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    },
    {
      "start": 221.04,
      "end": 222.78,
      "text": "我们再来到我的博客",
      "chunk": 0,
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      "compression_ratio": 1.525
    },
    {
      "start": 222.78,
      "end": 225.8,
      "text": "然后第二步是我们需要下载并配置",
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    {
      "start": 225.8,
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      "text": "这是Lama.cpp",
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      "avg_logprob": -0.14876563208443777,
      "no_speech_prob": 3.767742715043987e-11,
      "compression_ratio": 1.501930501930502
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    {
      "start": 227.8,
      "end": 229.8,
      "text": "然后这是Lama.cpp的下载地址",
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      "no_speech_prob": 3.767742715043987e-11,
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    },
    {
      "start": 229.8,
      "end": 230.8,
      "text": "也是三个",
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    {
      "start": 230.8,
      "end": 232.8,
      "text": "第一个是官方的Headahub下载地址",
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      "avg_logprob": -0.14876563208443777,
      "no_speech_prob": 3.767742715043987e-11,
      "compression_ratio": 1.501930501930502
    },
    {
      "start": 232.8,
      "end": 235.8,
      "text": "第二个是Quack给迅雷的",
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      "avg_logprob": -0.14876563208443777,
      "no_speech_prob": 3.767742715043987e-11,
      "compression_ratio": 1.501930501930502
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    {
      "start": 235.8,
      "end": 237.8,
      "text": "如果是Headahub打不开的话",
      "chunk": 0,
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      "avg_logprob": -0.14876563208443777,
      "no_speech_prob": 3.767742715043987e-11,
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    },
    {
      "start": 237.8,
      "end": 239.8,
      "text": "可以用Quack网盘跟迅雷网盘进行下载",
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      "language": "zh",
      "avg_logprob": -0.14876563208443777,
      "no_speech_prob": 3.767742715043987e-11,
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    {
      "start": 239.8,
      "end": 241.8,
      "text": "我就用这个官方的",
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      "compression_ratio": 1.501930501930502
    },
    {
      "start": 241.8,
      "end": 242.8,
      "text": "点击打开",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.14876563208443777,
      "no_speech_prob": 3.767742715043987e-11,
      "compression_ratio": 1.501930501930502
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    {
      "start": 244.8,
      "end": 245.8,
      "text": "打开之后",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.14876563208443777,
      "no_speech_prob": 3.767742715043987e-11,
      "compression_ratio": 1.501930501930502
    },
    {
      "start": 245.8,
      "end": 247.8,
      "text": "我们点击这个进行的版本号",
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      "avg_logprob": -0.14876563208443777,
      "no_speech_prob": 3.767742715043987e-11,
      "compression_ratio": 1.501930501930502
    },
    {
      "start": 248.8,
      "end": 250.8,
      "text": "点击这个版本号之后",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.14876563208443777,
      "no_speech_prob": 3.767742715043987e-11,
      "compression_ratio": 1.501930501930502
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    {
      "start": 250.8,
      "end": 252.8,
      "text": "这就是它的下载链接",
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      "no_speech_prob": 3.767742715043987e-11,
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    {
      "start": 252.8,
      "end": 254.8,
      "text": "我们可以看到这边是有MacOS版本",
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      "no_speech_prob": 3.767742715043987e-11,
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    {
      "start": 254.8,
      "end": 256.72,
      "text": "然后有Winux版本",
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    {
      "start": 256.72,
      "end": 258.44,
      "text": "因为我是Windows我就下载Windows的",
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    {
      "start": 258.44,
      "end": 261.48,
      "text": "然后它是有很多电接的Windows的",
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    {
      "start": 261.48,
      "end": 263.24,
      "text": "我们可以对比一下",
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    {
      "start": 263.24,
      "end": 266.52,
      "text": "这边是Windows的各个不同的版本的对应",
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    },
    {
      "start": 266.52,
      "end": 269.04,
      "text": "然后给你自己的显卡跟CPU进行选择",
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    {
      "start": 269.04,
      "end": 270.6,
      "text": "因为我是英伟达显卡",
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    {
      "start": 270.6,
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      "text": "我下这两个就行了",
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    {
      "start": 272.04,
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      "text": "我选择一个就行了",
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    },
    {
      "start": 273.28,
      "end": 275.24,
      "text": "然后这个13的",
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    {
      "start": 275.24,
      "end": 276.0,
      "text": "coda13",
      "chunk": 0,
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    {
      "start": 276.0,
      "end": 277.76,
      "text": "然后这个要下载",
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    {
      "start": 277.76,
      "end": 280.92,
      "text": "然后后面这个DLS也要下载",
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    {
      "start": 280.92,
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      "text": "点击下载",
      "chunk": 0,
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    {
      "start": 282.12,
      "end": 285.62,
      "text": "我们可以看到已经下载好了",
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    {
      "start": 285.62,
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      "text": "这两个压缩包",
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    },
    {
      "start": 287.22,
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      "text": "一个是前面的正式的程序",
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    {
      "start": 289.62,
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      "text": "后面是dlls",
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    {
      "start": 291.22,
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      "text": "这两个都要下载的前后两个",
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    {
      "start": 293.62,
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      "text": "我们可以打开看一下",
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    {
      "start": 296.62,
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      "text": "然后这是两个模型",
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    {
      "start": 298.22,
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      "text": "一个多模态",
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    {
      "start": 298.82,
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      "text": "一个正常的模型文件",
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    {
      "start": 300.32,
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      "text": "这两个就是lama.cpp的程序",
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    {
      "start": 303.82,
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      "text": "然后先把这个两个压缩包都给它解压出来",
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    {
      "start": 308.42,
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      "text": "然后打开看一下",
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    {
      "start": 309.26,
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      "text": "这个是dll的",
      "chunk": 0,
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    {
      "start": 310.72,
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      "text": "然后给它剪切",
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    {
      "start": 312.38,
      "end": 315.34,
      "text": "复制剪切到我们的LAMA主程序里面来",
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      "no_speech_prob": 1.7978903088522458e-11,
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      "start": 315.34,
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      "text": "这个只要它的主程序给它剪切过来",
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    {
      "start": 319.86,
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      "text": "这个是已经空了",
      "chunk": 0,
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    {
      "start": 321.4,
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      "text": "然后这给它删了吧",
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    {
      "start": 322.94,
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      "text": "这两个压缩包给它删了",
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      "no_speech_prob": 1.7978903088522458e-11,
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    {
      "start": 324.72,
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      "text": "这个就是LAMA的主程序",
      "chunk": 0,
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      "avg_logprob": -0.26581070998619344,
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    {
      "start": 326.48,
      "end": 328.06,
      "text": "然后我给它命名一下吧",
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    {
      "start": 328.06,
      "end": 328.78,
      "text": "这个太长了",
      "chunk": 0,
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    {
      "start": 330.64,
      "end": 331.44,
      "text": "然后现在就好了",
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    {
      "start": 331.44,
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      "text": "我们就先不要管它",
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      "compression_ratio": 1.6033755274261603
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    {
      "start": 332.64,
      "end": 334.04,
      "text": "然后把这两个给它关掉",
      "chunk": 0,
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      "avg_logprob": -0.26581070998619344,
      "no_speech_prob": 1.7978903088522458e-11,
      "compression_ratio": 1.6033755274261603
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    {
      "start": 335.04,
      "end": 336.28,
      "text": "然后我们再看下面",
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      "avg_logprob": -0.26581070998619344,
      "no_speech_prob": 1.7978903088522458e-11,
      "compression_ratio": 1.6033755274261603
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    {
      "start": 336.28,
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      "text": "下面是需要把模型",
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      "compression_ratio": 1.6033755274261603
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    {
      "start": 339.56,
      "end": 341.24,
      "text": "接入到lama.cpp里面",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.19614511654700761,
      "no_speech_prob": 2.194030720992224e-11,
      "compression_ratio": 1.5513307984790874
    },
    {
      "start": 341.24,
      "end": 342.38,
      "text": "我们才能运行它",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.19614511654700761,
      "no_speech_prob": 2.194030720992224e-11,
      "compression_ratio": 1.5513307984790874
    },
    {
      "start": 342.38,
      "end": 347.24,
      "text": "是把下载的大模型放到lama.cpp程序的game目录下的modus目录",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.19614511654700761,
      "no_speech_prob": 2.194030720992224e-11,
      "compression_ratio": 1.5513307984790874
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    {
      "start": 347.24,
      "end": 349.8,
      "text": "然后这个目录我们是需要自行创建的",
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      "avg_logprob": -0.19614511654700761,
      "no_speech_prob": 2.194030720992224e-11,
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    {
      "start": 349.8,
      "end": 352.7,
      "text": "我们先来到lama.cpp的程序的game目录下",
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      "no_speech_prob": 2.194030720992224e-11,
      "compression_ratio": 1.5513307984790874
    },
    {
      "start": 354.38,
      "end": 355.82,
      "text": "这个就是刚刚我命名的这个",
      "chunk": 0,
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      "avg_logprob": -0.19614511654700761,
      "no_speech_prob": 2.194030720992224e-11,
      "compression_ratio": 1.5513307984790874
    },
    {
      "start": 355.82,
      "end": 357.98,
      "text": "然后打开它就是game目录",
      "chunk": 0,
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      "avg_logprob": -0.19614511654700761,
      "no_speech_prob": 2.194030720992224e-11,
      "compression_ratio": 1.5513307984790874
    },
    {
      "start": 357.98,
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      "text": "然后我们需要创建一个文件夹",
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      "no_speech_prob": 2.194030720992224e-11,
      "compression_ratio": 1.5513307984790874
    },
    {
      "start": 360.44,
      "end": 363.62,
      "text": "文件夹就是modus文件夹",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.19614511654700761,
      "no_speech_prob": 2.194030720992224e-11,
      "compression_ratio": 1.5513307984790874
    },
    {
      "start": 363.62,
      "end": 365.12,
      "text": "然后我们复制一下名字",
      "chunk": 0,
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      "avg_logprob": -0.19614511654700761,
      "no_speech_prob": 2.194030720992224e-11,
      "compression_ratio": 1.5513307984790874
    },
    {
      "start": 365.12,
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      "text": "然后给它点击空白处UG",
      "chunk": 0,
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      "avg_logprob": -0.19614511654700761,
      "no_speech_prob": 2.194030720992224e-11,
      "compression_ratio": 1.5513307984790874
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    {
      "start": 367.98,
      "end": 370.22,
      "text": "给它清点一个文件夹",
      "chunk": 0,
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      "avg_logprob": -0.2333114930312999,
      "no_speech_prob": 1.8919515260007458e-11,
      "compression_ratio": 1.44
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    {
      "start": 370.22,
      "end": 372.78,
      "text": "明天给大家粘贴进去",
      "chunk": 0,
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      "avg_logprob": -0.2333114930312999,
      "no_speech_prob": 1.8919515260007458e-11,
      "compression_ratio": 1.44
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    {
      "start": 372.78,
      "end": 374.74,
      "text": "就这个目录我们已经创建好了",
      "chunk": 0,
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      "avg_logprob": -0.2333114930312999,
      "no_speech_prob": 1.8919515260007458e-11,
      "compression_ratio": 1.44
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    {
      "start": 374.74,
      "end": 378.22,
      "text": "然后我们需要把大模型放到这个目录里面来",
      "chunk": 0,
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      "avg_logprob": -0.2333114930312999,
      "no_speech_prob": 1.8919515260007458e-11,
      "compression_ratio": 1.44
    },
    {
      "start": 380.34,
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      "text": "然后给大家来到这边",
      "chunk": 0,
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      "avg_logprob": -0.2333114930312999,
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    {
      "start": 382.74,
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      "text": "剪切这两个模型",
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      "compression_ratio": 1.44
    },
    {
      "start": 384.54,
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      "text": "一个多模的一个正式的",
      "chunk": 0,
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      "avg_logprob": -0.2333114930312999,
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      "compression_ratio": 1.44
    },
    {
      "start": 386.22,
      "end": 389.62,
      "text": "然后给它剪切到这个modus目录来就行了",
      "chunk": 0,
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      "avg_logprob": -0.2333114930312999,
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    {
      "start": 389.62,
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      "text": "然后这个LAMA主程序我们放哪都行啊",
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    {
      "start": 391.66,
      "end": 392.26,
      "text": "随便放",
      "chunk": 0,
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      "no_speech_prob": 1.8919515260007458e-11,
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    {
      "start": 392.26,
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      "text": "只要你的盘够用就行",
      "chunk": 0,
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      "compression_ratio": 1.44
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    {
      "start": 394.42,
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      "text": "下面我们需要配置启动脚本",
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    {
      "start": 398.42,
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      "text": "lama.cpp gamelo创建启动脚本",
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    {
      "start": 401.42,
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      "text": "命名成启动.bat",
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      "no_speech_prob": 3.103764467660142e-11,
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    {
      "start": 403.42,
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      "text": "也可以命名成1.bat都行",
      "chunk": 0,
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      "no_speech_prob": 3.103764467660142e-11,
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    {
      "start": 405.42,
      "end": 407.42,
      "text": "然后我们需要创建这个启动脚本",
      "chunk": 0,
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      "no_speech_prob": 3.103764467660142e-11,
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    {
      "start": 407.42,
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      "text": "然后我先复制这个名字吧",
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      "start": 408.42,
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      "text": "启动.bat 复制",
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    {
      "start": 410.42,
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      "text": "然后来到lama主程序来",
      "chunk": 0,
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      "no_speech_prob": 3.103764467660142e-11,
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    {
      "start": 413.42,
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      "text": "然后右击空白处",
      "chunk": 0,
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      "no_speech_prob": 3.103764467660142e-11,
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    },
    {
      "start": 415.42,
      "end": 417.42,
      "text": "然后给它新建一个文本文档先",
      "chunk": 0,
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      "no_speech_prob": 3.103764467660142e-11,
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    {
      "start": 417.42,
      "end": 420.42,
      "text": "然后我们把这个文本文档给它重义命名一下",
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    {
      "start": 420.42,
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      "text": "后面的.tlt也要给它删了",
      "chunk": 0,
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      "no_speech_prob": 3.103764467660142e-11,
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    {
      "start": 423.42,
      "end": 425.92,
      "text": "上讲之后我们把启动点BAT给它粘贴进来",
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    {
      "start": 425.92,
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      "text": "这就是一个BAT文件了",
      "chunk": 0,
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      "avg_logprob": -0.17859450000071386,
      "no_speech_prob": 1.769936801288008e-11,
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    {
      "start": 427.42,
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      "text": "让给它确定一下",
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    {
      "start": 430.72,
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      "text": "现在已经改好了",
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    {
      "start": 431.52,
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      "text": "这边前面变成一个小尺",
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    {
      "start": 433.12,
      "end": 435.22,
      "text": "说明它就是命名成功了",
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    {
      "start": 435.22,
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      "text": "让我们右击",
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    {
      "start": 437.22,
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      "text": "右击它用文明打开",
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    {
      "start": 438.52,
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      "text": "我们还要均匀修改",
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    {
      "start": 440.72,
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      "text": "打开入里面是啥都没有的",
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    {
      "start": 442.02,
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      "text": "是空白的",
      "chunk": 0,
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    {
      "start": 442.72,
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      "text": "我们需要把我们的这个",
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    {
      "start": 445.72,
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      "text": "启动脚本给它复制粘贴进去",
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    {
      "start": 448.22,
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      "text": "让我们点击这边的copy",
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    {
      "start": 451.42,
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      "text": "让给它粘贴过来",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.17859450000071386,
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    {
      "start": 453.42,
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      "text": "监贴到启动.bat里面来",
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    {
      "start": 455.22,
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      "text": "这就是它的启动脚本",
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    {
      "start": 457.96,
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      "text": "我们可以看一下这边",
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    {
      "start": 459.48,
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      "text": "这里",
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    {
      "start": 460.36,
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      "text": "其实我这边也有写的",
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    {
      "start": 463.52,
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      "text": "这边是代码说明",
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      "avg_logprob": -0.1710121912985855,
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    {
      "start": 464.96,
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      "text": "有一个模型名是我们要改的",
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    {
      "start": 467.06,
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      "text": "这个端口号",
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    {
      "start": 468.22,
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      "text": "如果你本身端口号",
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    {
      "start": 469.42,
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      "text": "跟他自跟我这个启动脚本的端口号冲突的话",
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      "no_speech_prob": 2.4181242078147314e-11,
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      "start": 472.58,
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      "text": "你要改一下",
      "chunk": 0,
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    },
    {
      "start": 473.58,
      "end": 476.08,
      "text": "然后-c就是上下文长度",
      "chunk": 0,
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      "no_speech_prob": 2.4181242078147314e-11,
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    {
      "start": 476.08,
      "end": 478.22,
      "text": "我们来到启动脚本这边",
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    {
      "start": 478.22,
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      "text": "这个就是模型名称",
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      "start": 479.32,
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      "text": "我们要改成我们自己的模型名称",
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    {
      "start": 480.92,
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      "text": "你要使用的模型名称",
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    {
      "start": 483.42,
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      "text": "比如说我刚刚下载的是",
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    {
      "start": 494.83,
      "end": 497.81,
      "text": "然后把这个模型名给它删了",
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    {
      "start": 497.81,
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      "text": "把真正的模型名称给它复制过来就行了",
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      "no_speech_prob": 1.5270078604356918e-11,
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    {
      "start": 500.55,
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      "text": "然后后面这个是多模态文件",
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    {
      "start": 504.81,
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      "text": "这个我们就不要动它了",
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      "start": 506.47,
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      "text": "多模态",
      "chunk": 1,
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      "start": 506.89,
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      "text": "但是你一定要下载这个多模态才行",
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      "start": 509.35,
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      "text": "这个文件",
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    {
      "start": 510.53,
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      "text": "然后后面这个",
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    {
      "start": 511.97,
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      "text": "这个是我们的端口号8085",
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      "start": 514.31,
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      "text": "如果你冲突的话",
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    {
      "start": 515.31,
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      "text": "就是你改一下别的",
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      "start": 516.61,
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      "text": "如果不冲突",
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      "start": 517.21,
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      "text": "你就默然用8085就行了",
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    {
      "start": 518.73,
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      "text": "然后后面这个是上下文长度",
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    {
      "start": 521.35,
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      "text": "现在我默认给它是6万",
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    {
      "start": 523.49,
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      "text": "如果你觉得小你可以自己改再改大一些就行了",
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    {
      "start": 526.03,
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      "text": "让我们现在给它改好了",
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    {
      "start": 527.69,
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      "text": "给它保存就行了",
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    {
      "start": 528.75,
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      "text": "保存之后",
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    {
      "start": 531.35,
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      "text": "我们就可以启动它了",
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    {
      "start": 532.97,
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      "text": "我们来到拉玛主程序",
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    {
      "start": 534.71,
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      "text": "然后启动点BAT",
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      "start": 537.19,
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      "text": "这个我们双击打开进行启动",
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      "start": 539.55,
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      "text": "我们可以看一下已经启动成功了",
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    {
      "start": 548.49,
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      "text": "当看到这个段号后面有127的0.0.1的时候就启动成功了",
      "chunk": 1,
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      "avg_logprob": -0.19608461953760162,
      "no_speech_prob": 2.0896815527970958e-11,
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    },
    {
      "start": 552.71,
      "end": 556.35,
      "text": "然后我们按住键盘左下角的Kintry键",
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    {
      "start": 556.35,
      "end": 558.87,
      "text": "然后再点击这个链接就能打开了",
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    {
      "start": 560.57,
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      "text": "你也可以在我这边点击这个链接是一样的",
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    {
      "start": 563.61,
      "end": 564.47,
      "text": "这个端号要对",
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    {
      "start": 564.47,
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      "text": "要是你自己的才行",
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    {
      "start": 566.17,
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      "text": "点击一样可以打开的",
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    {
      "start": 569.27,
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      "text": "但是我测试一下",
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      "start": 570.75,
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      "text": "比如说八个你好",
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    {
      "start": 572.95,
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      "text": "你是什么大模型",
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      "start": 578.81,
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      "text": "他说我签位",
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    {
      "start": 579.85,
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      "text": "然后速度是50tok每秒",
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    {
      "start": 583.39,
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      "text": "然后这个是",
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    {
      "start": 584.51,
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      "text": "这边是有个句子的模型信息的",
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    {
      "start": 587.55,
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      "text": "这句子的模型信息",
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    {
      "start": 588.71,
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      "text": "这个就是我们现在正在用的",
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    {
      "start": 591.01,
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      "text": "它是支持多模态的",
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    {
      "start": 592.79,
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      "text": "因为我们有多模态文件在里面",
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    {
      "start": 595.31,
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      "text": "比如我上传个图片给他",
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      "start": 598.21,
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      "text": "添加个图片",
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    {
      "start": 601.95,
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      "text": "然后识别图片内容",
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    {
      "start": 607.55,
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      "text": "我们可以看到它运行完了",
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      "start": 609.21,
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      "text": "它是每秒也是50Token",
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    {
      "start": 612.21,
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      "text": "然后这是它识别到的图片内容",
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    {
      "start": 615.51,
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      "text": "是比较准确的",
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    {
      "start": 616.71,
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      "text": "它是能够识别图片的",
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    {
      "start": 618.55,
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      "text": "这是多模态",
      "chunk": 1,
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    {
      "start": 619.79,
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      "text": "然后我们这个可以给它关掉",
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    {
      "start": 621.85,
      "end": 625.01,
      "text": "因为我们要对接到Hermes的A进里面去",
      "chunk": 1,
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      "avg_logprob": -0.23745783574163576,
      "no_speech_prob": 2.91341950564572e-11,
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    {
      "start": 625.01,
      "end": 627.91,
      "text": "然后我说一下这个中端是不能关掉的",
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    {
      "start": 627.91,
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      "text": "你关掉它就没办法用了",
      "chunk": 1,
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      "no_speech_prob": 2.91341950564572e-11,
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    {
      "start": 630.11,
      "end": 631.75,
      "text": "因为它靠这个中端的",
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      "no_speech_prob": 2.91341950564572e-11,
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    {
      "start": 631.75,
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      "text": "如果你重启电脑之后",
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      "no_speech_prob": 2.91341950564572e-11,
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    {
      "start": 633.09,
      "end": 634.85,
      "text": "你也要再开启这个中端才能用",
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      "no_speech_prob": 2.91341950564572e-11,
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    },
    {
      "start": 634.85,
      "end": 637.51,
      "text": "就是点击微软起动点BAT就行了",
      "chunk": 1,
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      "avg_logprob": -0.23745783574163576,
      "no_speech_prob": 2.91341950564572e-11,
      "compression_ratio": 1.398753894080997
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    {
      "start": 637.51,
      "end": 640.91,
      "text": "让我们现在看一下紫模对接到荷免声音镜里面去",
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      "avg_logprob": -0.22962970382596817,
      "no_speech_prob": 1.7881607652925346e-11,
      "compression_ratio": 1.5810276679841897
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    {
      "start": 640.91,
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      "text": "荷免声音镜下载地址是这个",
      "chunk": 1,
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      "avg_logprob": -0.22962970382596817,
      "no_speech_prob": 1.7881607652925346e-11,
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    {
      "start": 642.67,
      "end": 644.85,
      "text": "我们可以点开",
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      "compression_ratio": 1.5810276679841897
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    {
      "start": 644.85,
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      "text": "点开之后",
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      "avg_logprob": -0.22962970382596817,
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      "compression_ratio": 1.5810276679841897
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    {
      "start": 645.91,
      "end": 647.71,
      "text": "点击这个下载就行了",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.22962970382596817,
      "no_speech_prob": 1.7881607652925346e-11,
      "compression_ratio": 1.5810276679841897
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    {
      "start": 647.71,
      "end": 649.65,
      "text": "这个是他默认显示的就是windows",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.22962970382596817,
      "no_speech_prob": 1.7881607652925346e-11,
      "compression_ratio": 1.5810276679841897
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    {
      "start": 649.65,
      "end": 650.55,
      "text": "点击",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.22962970382596817,
      "no_speech_prob": 1.7881607652925346e-11,
      "compression_ratio": 1.5810276679841897
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    {
      "start": 650.55,
      "end": 651.95,
      "text": "进行下载",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.22962970382596817,
      "no_speech_prob": 1.7881607652925346e-11,
      "compression_ratio": 1.5810276679841897
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    {
      "start": 651.95,
      "end": 653.51,
      "text": "这边我允许一下",
      "chunk": 1,
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      "avg_logprob": -0.22962970382596817,
      "no_speech_prob": 1.7881607652925346e-11,
      "compression_ratio": 1.5810276679841897
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    {
      "start": 653.51,
      "end": 655.05,
      "text": "他比较小的已经下载好了",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.22962970382596817,
      "no_speech_prob": 1.7881607652925346e-11,
      "compression_ratio": 1.5810276679841897
    },
    {
      "start": 655.05,
      "end": 656.51,
      "text": "让我点击进行安装",
      "chunk": 1,
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      "avg_logprob": -0.22962970382596817,
      "no_speech_prob": 1.7881607652925346e-11,
      "compression_ratio": 1.5810276679841897
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    {
      "start": 659.01,
      "end": 660.61,
      "text": "打开之后我们点这边的安装就行了",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.22962970382596817,
      "no_speech_prob": 1.7881607652925346e-11,
      "compression_ratio": 1.5810276679841897
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    {
      "start": 664.27,
      "end": 665.85,
      "text": "我们可以看到他已经装好了",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.22962970382596817,
      "no_speech_prob": 1.7881607652925346e-11,
      "compression_ratio": 1.5810276679841897
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    {
      "start": 665.85,
      "end": 667.23,
      "text": "让我们点这边的开始",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.22962970382596817,
      "no_speech_prob": 1.7881607652925346e-11,
      "compression_ratio": 1.5810276679841897
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    {
      "start": 667.51,
      "end": 670.75,
      "text": "现在已经好了",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.24026784029873935,
      "no_speech_prob": 1.974652386049769e-11,
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    {
      "start": 670.75,
      "end": 673.51,
      "text": "我们点这边的稍后再选择提供方",
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      "avg_logprob": -0.24026784029873935,
      "no_speech_prob": 1.974652386049769e-11,
      "compression_ratio": 1.5991902834008098
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    {
      "start": 673.51,
      "end": 674.51,
      "text": "先给它打开",
      "chunk": 1,
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      "avg_logprob": -0.24026784029873935,
      "no_speech_prob": 1.974652386049769e-11,
      "compression_ratio": 1.5991902834008098
    },
    {
      "start": 674.51,
      "end": 677.01,
      "text": "然后打开之后我们把这个给它关掉",
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      "avg_logprob": -0.24026784029873935,
      "no_speech_prob": 1.974652386049769e-11,
      "compression_ratio": 1.5991902834008098
    },
    {
      "start": 677.01,
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      "text": "关掉之后我们下面要开始配置它了",
      "chunk": 1,
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      "avg_logprob": -0.24026784029873935,
      "no_speech_prob": 1.974652386049769e-11,
      "compression_ratio": 1.5991902834008098
    },
    {
      "start": 679.51,
      "end": 681.51,
      "text": "然后再打开HermicEinge",
      "chunk": 1,
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      "avg_logprob": -0.24026784029873935,
      "no_speech_prob": 1.974652386049769e-11,
      "compression_ratio": 1.5991902834008098
    },
    {
      "start": 681.51,
      "end": 684.51,
      "text": "然后点这边的齿轮打开",
      "chunk": 1,
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      "avg_logprob": -0.24026784029873935,
      "no_speech_prob": 1.974652386049769e-11,
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    {
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      "text": "打开之后我们选择",
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      "no_speech_prob": 1.974652386049769e-11,
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    },
    {
      "start": 686.51,
      "end": 688.51,
      "text": "左边这边有个提供方点击",
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      "compression_ratio": 1.5991902834008098
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    {
      "start": 688.51,
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      "text": "点击之后下面有一个APM秘钥点击",
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      "no_speech_prob": 1.974652386049769e-11,
      "compression_ratio": 1.5991902834008098
    },
    {
      "start": 691.51,
      "end": 695.51,
      "text": "点击之后我们找到下面下面有一个OpenAI",
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    {
      "start": 695.51,
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      "text": "我们往阿拉找一下",
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    {
      "start": 696.51,
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      "text": "这个有个openaiapi点击",
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      "text": "点击之后这有两个要填",
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    {
      "start": 702.03,
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      "text": "一个是密钥",
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    {
      "start": 702.99,
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      "text": "另外一个就是url",
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    {
      "start": 704.61,
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      "text": "这个密钥我们就随便填就行了",
      "chunk": 1,
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    {
      "start": 706.49,
      "end": 707.59,
      "text": "因为它是本地模型",
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    {
      "start": 707.59,
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      "text": "12345",
      "chunk": 1,
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      "compression_ratio": 1.3895582329317269
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    {
      "start": 708.85,
      "end": 709.99,
      "text": "然后保存",
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      "text": "url我们用这个",
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    {
      "start": 713.41,
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      "text": "这个url",
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    {
      "start": 714.91,
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      "text": "我博客这边的",
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    {
      "start": 716.37,
      "end": 717.45,
      "text": "然后这个8085",
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    {
      "start": 717.45,
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      "text": "你就",
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    {
      "start": 717.83,
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      "text": "你没改的话就是8085",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.19111167060004342,
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    {
      "start": 719.59,
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      "text": "你改了端口号的话",
      "chunk": 1,
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      "avg_logprob": -0.19111167060004342,
      "no_speech_prob": 3.0307544668373154e-11,
      "compression_ratio": 1.3895582329317269
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    {
      "start": 721.31,
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      "text": "你就是你自己改的那个端口号",
      "chunk": 1,
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      "avg_logprob": -0.19111167060004342,
      "no_speech_prob": 3.0307544668373154e-11,
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    {
      "start": 723.23,
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      "text": "我们复制一下",
      "chunk": 1,
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    {
      "start": 724.05,
      "end": 726.85,
      "text": "然后给它粘贴过来",
      "chunk": 1,
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      "avg_logprob": -0.2181730923587329,
      "no_speech_prob": 2.6363080121716287e-11,
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    {
      "start": 726.85,
      "end": 728.21,
      "text": "然后点保存",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.2181730923587329,
      "no_speech_prob": 2.6363080121716287e-11,
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    {
      "start": 728.21,
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      "text": "现在就已经保存成功了",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.2181730923587329,
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    {
      "start": 731.17,
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      "text": "让我们点这边的模型",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.2181730923587329,
      "no_speech_prob": 2.6363080121716287e-11,
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    {
      "start": 732.99,
      "end": 735.75,
      "text": "然后这边有一个选择",
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    {
      "start": 735.75,
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      "text": "现在是自动",
      "chunk": 1,
      "language": "zh",
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    {
      "start": 736.95,
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      "text": "然后我们点一下",
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      "no_speech_prob": 2.6363080121716287e-11,
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    {
      "start": 737.91,
      "end": 739.41,
      "text": "这边有一个open AI",
      "chunk": 1,
      "language": "zh",
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    {
      "start": 739.41,
      "end": 740.37,
      "text": "选中它",
      "chunk": 1,
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      "no_speech_prob": 2.6363080121716287e-11,
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    {
      "start": 740.37,
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      "text": "然后这我们点应用",
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      "no_speech_prob": 2.6363080121716287e-11,
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    {
      "start": 741.95,
      "end": 743.39,
      "text": "现在保错",
      "chunk": 1,
      "language": "zh",
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    {
      "start": 743.39,
      "end": 745.15,
      "text": "然后我们选一下这个模型",
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    {
      "start": 745.15,
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      "text": "下面有我们刚刚的这个选择的模型",
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    {
      "start": 748.63,
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      "text": "然后选中它",
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    {
      "start": 749.83,
      "end": 751.11,
      "text": "7213.827B",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.2181730923587329,
      "no_speech_prob": 2.6363080121716287e-11,
      "compression_ratio": 1.6228070175438596
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    {
      "start": 751.11,
      "end": 751.91,
      "text": "然后再点应用",
      "chunk": 1,
      "language": "zh",
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    {
      "start": 751.91,
      "end": 753.79,
      "text": "然后现在就成功了",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.2181730923587329,
      "no_speech_prob": 2.6363080121716287e-11,
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    {
      "start": 753.79,
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      "text": "我们给它关掉",
      "chunk": 1,
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      "no_speech_prob": 2.1233112490470774e-11,
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    {
      "start": 755.29,
      "end": 759.09,
      "text": "关掉之后我们可以看到这边对话框这边",
      "chunk": 1,
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      "avg_logprob": -0.2082943463847585,
      "no_speech_prob": 2.1233112490470774e-11,
      "compression_ratio": 1.3397683397683398
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    {
      "start": 759.09,
      "end": 763.47,
      "text": "默认就选中了723.827BGSQRCO这个模型",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.2082943463847585,
      "no_speech_prob": 2.1233112490470774e-11,
      "compression_ratio": 1.3397683397683398
    },
    {
      "start": 763.47,
      "end": 765.71,
      "text": "我们问一下看它能不能用",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.2082943463847585,
      "no_speech_prob": 2.1233112490470774e-11,
      "compression_ratio": 1.3397683397683398
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    {
      "start": 765.71,
      "end": 767.57,
      "text": "比如说发个你好",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.2082943463847585,
      "no_speech_prob": 2.1233112490470774e-11,
      "compression_ratio": 1.3397683397683398
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    {
      "start": 767.57,
      "end": 769.79,
      "text": "你是什么模型",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.2082943463847585,
      "no_speech_prob": 2.1233112490470774e-11,
      "compression_ratio": 1.3397683397683398
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    {
      "start": 769.79,
      "end": 773.51,
      "text": "我们可以看到它回复了",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.2082943463847585,
      "no_speech_prob": 2.1233112490470774e-11,
      "compression_ratio": 1.3397683397683398
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    {
      "start": 773.51,
      "end": 776.21,
      "text": "然后它说明它是能够正常连上的",
      "chunk": 1,
      "language": "zh",
      "avg_logprob": -0.2082943463847585,
      "no_speech_prob": 2.1233112490470774e-11,
      "compression_ratio": 1.3397683397683398
    },
    {
      "start": 776.21,
      "end": 778.73,
      "text": "然后我们再选择一张图片",
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      "text": "他之前是太大了",
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      "text": "这Hermicene Geek还是挺智能的",
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      "text": "然后我提醒一下大家",
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      "text": "还是他这个",
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      "start": 805.27,
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      "text": "还是用这个网址进行使用这个模型",
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      "start": 809.03,
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      "text": "我们都不要把这个终端给关掉",
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      "text": "你关掉就没办法用了",
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      "start": 812.01,
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      "text": "好吧",
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      "start": 813.05,
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      "text": "今天这段就到这吧",
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      "text": "如果大家有什么问题的话",
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    {
      "start": 815.31,
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      "text": "可以在视频下方的频道去留言交流",
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    {
      "start": 817.31,
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      "text": "谢谢大家的观看",
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    "status": "SEGMENTED_TRANSCRIPT_OK",
    "created_at": "2026-09-18T16:30:23",
    "manifest": "/Users/sagawa/AI_WORK/video_notes/url/20260918_162952__小体积不降智_Qwen3.8-27B_多模态_AI_模型本地部署_低配置电脑也能跑_接入_Hermes_Agent/segmented_work/chunk_manifest.json",
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    "global_repeat_text": "我們現在看到的這個就是CM3.827B精華版。它的特點就是模型體積被壓縮的很小,但是它的性能或者智商並沒有下降。",
    "tail_repeat_run": 1,
    "tail_repeat_text": "然后再点应用",
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