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  "text": "大家看我在DeepseekHannis的圆满里翻譯了什么。\nThe Agent Codex和The Agent Cloud。\n这是要做Codex Cloud最严厉的父亲吗?\nHannis本地部署千万3.827B同时跑三个任务也行吗?\n这下是不是真的Token自由了?\n大家好,我是ZZ。\nDeepseekHannis让本地Agent框架的看研程度更上一层楼,\n而千万3.827B则是让本地部署大模型执行复杂Agent的任务成为可能。\n本期我们就来本地部署Hannis加千万3.827B,\n实现数据安全和Token自由。\n先给大家介绍一下什么是DeepseekHannis。\n网上有人说这是Deepseek版的Codex或者Cloud,\n但是我想说这种说法都不太准确,\n因为DeepseekHannis就像一块乐高的底板一样,\n所有的Agent能力皆为插件,\n这些插件就像于CoreGIM可以任意组合按需替换。\n想用什么模型,想加载什么工具,怎么调度,怎么存储,都有你决定。\n甚至可以给DeepseekHannis换了你喜欢的20元角色的UI皮肤。\n除了架构上的自由度外,\nDeepseekHannis的运行过程不仅有迹可循,还公开透明,\n这一点是在同类任何BN软件上都看不到的。\n说起BN软件,我在看DeepseekHannis的原码时,发现了一段有趣的代码。\nDeepseekHannis似乎在设计之初就考虑了可以调用其他同类本地Agent能力。\n这你敢信,大部分AI助手类软件的设计逻辑肯定是能干的活都自己干了呀,\n就算派子Agent肯定也是自己分身一个呀,\n比通行留个门还分一杯各种的事情,要是让用户跑了直接用上通行的了那怎么办呢?不得不说量身这格局够大。\n这里我也实际测试了一下,确实能调用到Codex的能力,但是似乎没能找到合适的场景,让我不得不去调用Codex的情况呀。\nCodex你得加把劲了。\n接下来我们来安装一下DeepseekHannis。\n首先第一步我们需要来到Node.js的官网下载并安装。\n安装过程我们一直点下一步即可。安装完成后,我们回到DeepseekHannis的官网,复制一下快速体验下面的正行命令。\n在开始探测中打开PowerShell,将正行命令粘贴进PowerShell后回车。等待以后我们输入Y再按回车。\n因为这里需要下载,所以它的花费时间会长一些,不是卡住了,请再按一下。\n等我们看到外部UI的地址的时候就是安装成功了。\n我们把它复制到浏览器打开。首先第一步就是让我们配置官方API Key。\n如果你想用自己的API Key或者本机部署,可以直接跳到视频的后面部分。\nDeepseekHannis的Meal,我们可以从DeepseekHannis的官网点击API开放平台。\n然后在API Key里面创建一个API Key。\n然后把API Key的Meal复制出来就行了。\n当然你得确保你还有余额,不然的话API Key也是用不了的。\n进入之后我们点击一下对话框,会让我们选择一个工作区。\n这个工作区呢,你就可以把它写成某一个具体项目的生产车间。\n比如它需要的一些文件资料,我们可以给它放在里面。\n然后它做的一些处理结果,文档也会在里面生成。\n接着我们来做一下连通性测试。\n我们随便发给它一段话,能够收到对应的回复。\n我们的DeepSeek Harness就安装成功了。\n我们可以在对话框下面选择它的权限等级。\n第一个是只读。这种情况下它只会读取你的文件,不会对你的电脑进行任何更改。\n第二个是在工作区域的写入。\n比如我们刚才选择的文件夹。正常的话我们就选择第二个就可以了。\n第三个是放开全部权限。这个适合有一定基础的小伙伴。\n接下来我们来本机部署千问3.8-27B。\n首先打开LM Studio的官网。\n我们点击下载,然后安装的时候点击下一步即可。\n在LM Studio中我们可以点击左下角的设置,然后点击第一个选项。\n这里可以把它设置为中文。\n接下来我们来下载千问3.8-27B的模型。\n我们点击左侧这个在放大镜的图标。\n点击之后我们在这里搜索千问3.8。\n这里第一个就是,如果没有的话我们可以搜索。\n这里有一个下载的设置。\n然后点开这就可以下载。\n我这里都给下载完了。\n然后大家可以根据自己的显存来进行下载。\n如果你的显存是16G的话,你就选择这个17B。\n也是可以装到下载。\n因为它有部分可能会装到内存里面。\n所以16G也是可以用的。\n然后这个如果你的内存在16G的话,你就选择这个Q6量化的。\n然后如果你的显存有29G以上的话,你就可以选择这个Q8量化的。\n下载完成后,我们来测试一下连通性。\n点击左侧的这个紫色的logo。\n接着我们选择顶部要加载的模型。\n然后接着在选择我们的模型,它就会自动加载。\n加载成功后,我们任意发送一条消息,能够收到对应的回复。\n就说明我们的LM Studio安装成功了。\n千问3.8-27B这个模型,除了它的agent能力很强外。\n它还是一个原生的视觉语言模型。\n因此它是可以看图的。\n这里我找了一堆比的图片,看它能不能数得清楚。\n好,这里它已经数完了,我们来看一下。\n它这里说15支笔,我们来数一数对不对。\n123456789141112131415。\n成功数对了,而且它也没有被那支笔杆和笔帽没有在一起的笔给干扰。\n接着我们将本地部署的千问3.8-27B进入到你们的DeepSeek Harness之中。\n首先我们在LM Studio里面点击左侧的第二个图标。\n我们把运行按钮打开,然后点击这里的加载模型。\n选择我们要加载的千问3.8-27B。\n加载完成后我们来到DeepSeek Harness之中。\n点击左下角的设置,点击模型。\n然后我们可以点击添加自定义配置方。\nProviderID这里一定要是小写字母开头。\n显示名称这里我们随便填。\nAPI地址这里我们从LMStudio这里复制一下这个地址。\n注意这个地址粘过来之后需要在后面加上斜线V1。\nAPI协议选择OpenAI Compatible。\n模型目录这里我们同样是从LMStudio这里复制过来。\n助理这里很容易复制过来空格。\n别问我为什么知道。\n我们在对话窗口将模型修改为千问3.8,\n然后进行连通性测试。\n回复任意一句话能够收到对应的回应就为成功。\n大家在本地部署的过程中遇到任何问题,\n也可以先给Harness配上个线上的API。\n让Harness直接帮你修。\n这样我们让他生成一个飞行小游戏来测试一下吧。\n与此同时我们当然他去写一个科幻小说吧。\n毕竟本地运行Token可是不要钱的啊。\n哎,怎么好像运行的还挺流畅的。\n估计还有余地。\n再给安排个生成天体的任务吧。\n生产队的驴子估计没我电脑忙了。\n我只求你别冒烟啊。\n可以看到LMStudio这里三个任务都给跑起来了。\n我这电脑的风扇也是呜呜的转。\n哎,好像开始卡了,卡了,不行了。\n我这鼠标已经非常难用了。\n不过看这个任务它还是在运行的。\n但是我这已经操作不了电脑了。\n我想把鼠标移动到银子这里啊。\n但是非常难移动啊。\n而且点机也没什么反应啊。\n这个应该是内存平行导致的。\n因为本次这个电脑的话是32G内存。\n运行一个任务的话应该是一点问题没有。\n但是如果运行多个任务的话,\n它上线文都夹在内存中是肯定撑不住的。\n所以大家一定要根据自己的情况去执行这个任务。\n包括其实LM199它其实是可以设置上远文长度的。\n如果你内存不够高的话,尽量设置的低一点。\n比如说64K以下。\n而且不要像我这样一聘同时执行好几个任务。\n我替你试过了,你就不要去试了。\n把电脑搞猫烟可不是那玩的。\n我这里想说任务管理器里面结束任务。\n但是任务管理器已经卡成一张精彩图片了。\n我这里是好不容易把录片软件给退了,\n然后才能操作鼠标。\n把后来提要两个字给关闭了,最后才生成的。\n我们来看一下生成结果吧。\n生成结果还是很出乎我的意料的。\n你别看这只是一个简单的飞行设计小游戏。\n它处理的这些碰撞啊,移动啊,\n包括敌人的生成啊,都没有任何问题。\n这对于一个27B的小模型来说是非常厉害的。\n而且这还是只是一轮提示词的情况下。\n如果多轮对话的话,它的潜力还有非常大的发布空间。\n我认为这个模型是真的可以作为生产力的一个模型了。\n然后我们再来看一下它生成的这个空外小说。\n虽然我中途停止了,\n但它生成了也很多东西了。\n而且我看它这个内容真的让我非常的震惊。\n我就一点看不出这是AI写的。\n虽然它这个东西写的是一个老套的星际移民的故事,\n但它这个东西让我看设定的时候,\n我一下子想到就是美剧那个什么羊毛战记,就是末日地宝那个。\n人们挤在地下桶汤里边,然后高层靠着隐瞒真相维持生存的秩序。\n包括它的一些场景设定,比如说,\n集水柱,然后墙上的救赎,就是人死了之后它会,\n其实它不是说是死了就直接上传数据了,\n而且它也保留了集水柱。\n我就觉得就很有画面感。\n就这个画面可能最后做出来就很震撼。\n好了,感谢大家能听到我讲到这里。\n后面我会给大家分享更多AI相关的知识。\n大家如果感兴趣的话记得关注一下。\n我是VV,在这里谢过大家了。",
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      "text": "大家看我在DeepseekHannis的圆满里翻譯了什么。",
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      "text": "The Agent Codex和The Agent Cloud。",
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      "text": "大家好,我是ZZ。",
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      "text": "而千万3.827B则是让本地部署大模型执行复杂Agent的任务成为可能。",
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      "text": "本期我们就来本地部署Hannis加千万3.827B,",
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      "text": "实现数据安全和Token自由。",
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      "text": "先给大家介绍一下什么是DeepseekHannis。",
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      "text": "网上有人说这是Deepseek版的Codex或者Cloud,",
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      "text": "但是我想说这种说法都不太准确,",
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      "text": "因为DeepseekHannis就像一块乐高的底板一样,",
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      "text": "所有的Agent能力皆为插件,",
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      "text": "这些插件就像于CoreGIM可以任意组合按需替换。",
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      "text": "想用什么模型,想加载什么工具,怎么调度,怎么存储,都有你决定。",
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      "text": "甚至可以给DeepseekHannis换了你喜欢的20元角色的UI皮肤。",
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      "text": "除了架构上的自由度外,",
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      "text": "DeepseekHannis的运行过程不仅有迹可循,还公开透明,",
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      "text": "这一点是在同类任何BN软件上都看不到的。",
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      "text": "说起BN软件,我在看DeepseekHannis的原码时,发现了一段有趣的代码。",
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      "text": "DeepseekHannis似乎在设计之初就考虑了可以调用其他同类本地Agent能力。",
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      "text": "这你敢信,大部分AI助手类软件的设计逻辑肯定是能干的活都自己干了呀,",
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      "text": "就算派子Agent肯定也是自己分身一个呀,",
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      "text": "接下来我们来安装一下DeepseekHannis。",
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      "text": "首先第一步我们需要来到Node.js的官网下载并安装。",
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      "text": "在开始探测中打开PowerShell,将正行命令粘贴进PowerShell后回车。等待以后我们输入Y再按回车。",
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      "text": "因为这里需要下载,所以它的花费时间会长一些,不是卡住了,请再按一下。",
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      "text": "等我们看到外部UI的地址的时候就是安装成功了。",
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      "text": "我们把它复制到浏览器打开。首先第一步就是让我们配置官方API Key。",
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      "text": "如果你想用自己的API Key或者本机部署,可以直接跳到视频的后面部分。",
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      "text": "DeepseekHannis的Meal,我们可以从DeepseekHannis的官网点击API开放平台。",
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      "text": "然后在API Key里面创建一个API Key。",
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        249,
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      "avg_logprob": -0.10188660492768159,
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      "no_speech_prob": 1.834917807974623e-11,
      "source": "whisper"
    },
    {
      "id": 38,
      "seek": 12710,
      "start": 145.1,
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      "text": "然后把API Key的Meal复制出来就行了。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.10188660492768159,
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    {
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      "text": "当然你得确保你还有余额,不然的话API Key也是用不了的。",
      "tokens": [
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      "avg_logprob": -0.10188660492768159,
      "compression_ratio": 1.372972972972973,
      "no_speech_prob": 1.834917807974623e-11,
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    {
      "id": 40,
      "seek": 12710,
      "start": 152.1,
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      "text": "进入之后我们点击一下对话框,会让我们选择一个工作区。",
      "tokens": [
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      "compression_ratio": 1.372972972972973,
      "no_speech_prob": 1.834917807974623e-11,
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    {
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      "seek": 15610,
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      "text": "这个工作区呢,你就可以把它写成某一个具体项目的生产车间。",
      "tokens": [
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      "no_speech_prob": 1.6113770040515618e-11,
      "source": "whisper"
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    {
      "id": 42,
      "seek": 15610,
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      "text": "比如它需要的一些文件资料,我们可以给它放在里面。",
      "tokens": [
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      "avg_logprob": -0.08736167444246952,
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      "no_speech_prob": 1.6113770040515618e-11,
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    {
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      "start": 163.1,
      "end": 167.1,
      "text": "然后它做的一些处理结果,文档也会在里面生成。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.08736167444246952,
      "compression_ratio": 1.401985111662531,
      "no_speech_prob": 1.6113770040515618e-11,
      "source": "whisper",
      "text_original": "然后它做的一些铲除结果,文档也会在里面生成。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 44,
      "seek": 15610,
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      "end": 169.1,
      "text": "接着我们来做一下连通性测试。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.08736167444246952,
      "compression_ratio": 1.401985111662531,
      "no_speech_prob": 1.6113770040515618e-11,
      "source": "whisper",
      "text_original": "接着我们来做一下联通性测试。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 45,
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      "text": "我们随便发给它一段话,能够收到对应的回复。",
      "tokens": [
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      "avg_logprob": -0.08736167444246952,
      "compression_ratio": 1.401985111662531,
      "no_speech_prob": 1.6113770040515618e-11,
      "source": "whisper"
    },
    {
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      "start": 172.1,
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      "text": "我们的DeepSeek Harness就安装成功了。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.6113770040515618e-11,
      "source": "whisper",
      "text_original": "我们的DeepseekHannis就安装成功了。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 47,
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      "text": "我们可以在对话框下面选择它的权限等级。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.6113770040515618e-11,
      "source": "whisper",
      "text_original": "我们可以在对话框下面选择它的群演等级。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 48,
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      "end": 183.1,
      "text": "第一个是只读。这种情况下它只会读取你的文件,不会对你的电脑进行任何更改。",
      "tokens": [
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      "no_speech_prob": 1.6113770040515618e-11,
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    {
      "id": 49,
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      "text": "第二个是在工作区域的写入。",
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      "temperature": 0,
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    },
    {
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      "seek": 18510,
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      "text": "比如我们刚才选择的文件夹。正常的话我们就选择第二个就可以了。",
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      "no_speech_prob": 1.4772662260131852e-11,
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    {
      "id": 51,
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      "text": "第三个是放开全部权限。这个适合有一定基础的小伙伴。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.11477580277816109,
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      "no_speech_prob": 1.4772662260131852e-11,
      "source": "whisper"
    },
    {
      "id": 52,
      "seek": 18510,
      "start": 193.1,
      "end": 196.1,
      "text": "接下来我们来本机部署千问3.8-27B。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.11477580277816109,
      "compression_ratio": 1.4627329192546583,
      "no_speech_prob": 1.4772662260131852e-11,
      "source": "whisper",
      "text_original": "接下来我们来本机部署签问3.827B。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 53,
      "seek": 18510,
      "start": 196.1,
      "end": 198.1,
      "text": "首先打开LM Studio的官网。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.11477580277816109,
      "compression_ratio": 1.4627329192546583,
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      "source": "whisper",
      "text_original": "首先打开LM499的官网。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 54,
      "seek": 18510,
      "start": 198.1,
      "end": 202.1,
      "text": "我们点击下载,然后安装的时候点击下一步即可。",
      "tokens": [
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      "temperature": 0,
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      "compression_ratio": 1.4627329192546583,
      "no_speech_prob": 1.4772662260131852e-11,
      "source": "whisper",
      "text_original": "我们点一下载,然后安装的时候点击一支下一步即可。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
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    {
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      "text": "在LM Studio中我们可以点击左下角的设置,然后点击第一个选项。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.11477580277816109,
      "compression_ratio": 1.4627329192546583,
      "no_speech_prob": 1.4772662260131852e-11,
      "source": "whisper",
      "text_original": "在LM499中我们可以点击左下角的设置,然后点击第一个选项。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 56,
      "seek": 18510,
      "start": 206.1,
      "end": 208.1,
      "text": "这里可以把它设置为中文。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.11477580277816109,
      "compression_ratio": 1.4627329192546583,
      "no_speech_prob": 1.4772662260131852e-11,
      "source": "whisper"
    },
    {
      "id": 57,
      "seek": 18510,
      "start": 208.1,
      "end": 211.1,
      "text": "接下来我们来下载千问3.8-27B的模型。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.11477580277816109,
      "compression_ratio": 1.4627329192546583,
      "no_speech_prob": 1.4772662260131852e-11,
      "source": "whisper",
      "text_original": "接下来我们来下载签问3.827B的模型。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 58,
      "seek": 21110,
      "start": 211.1,
      "end": 215.1,
      "text": "我们点击左侧这个在放大镜的图标。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.12558947024138079,
      "compression_ratio": 1.4090909090909092,
      "no_speech_prob": 1.6115419762541272e-11,
      "source": "whisper"
    },
    {
      "id": 59,
      "seek": 21110,
      "start": 215.1,
      "end": 217.1,
      "text": "点击之后我们在这里搜索千问3.8。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.12558947024138079,
      "compression_ratio": 1.4090909090909092,
      "no_speech_prob": 1.6115419762541272e-11,
      "source": "whisper",
      "text_original": "点击之后我们在这里搜索千元3.8。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 60,
      "seek": 21110,
      "start": 217.1,
      "end": 219.1,
      "text": "这里第一个就是,如果没有的话我们可以搜索。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.12558947024138079,
      "compression_ratio": 1.4090909090909092,
      "no_speech_prob": 1.6115419762541272e-11,
      "source": "whisper"
    },
    {
      "id": 61,
      "seek": 21110,
      "start": 219.1,
      "end": 221.1,
      "text": "这里有一个下载的设置。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.12558947024138079,
      "compression_ratio": 1.4090909090909092,
      "no_speech_prob": 1.6115419762541272e-11,
      "source": "whisper",
      "text_original": "这里一个下载的一个设置。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 62,
      "seek": 21110,
      "start": 221.1,
      "end": 223.1,
      "text": "然后点开这就可以下载。",
      "tokens": [
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      "temperature": 0,
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      "compression_ratio": 1.4090909090909092,
      "no_speech_prob": 1.6115419762541272e-11,
      "source": "whisper"
    },
    {
      "id": 63,
      "seek": 21110,
      "start": 223.1,
      "end": 225.1,
      "text": "我这里都给下载完了。",
      "tokens": [
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      "no_speech_prob": 1.6115419762541272e-11,
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    {
      "id": 64,
      "seek": 21110,
      "start": 225.1,
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      "text": "然后大家可以根据自己的显存来进行下载。",
      "tokens": [
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      "no_speech_prob": 1.6115419762541272e-11,
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    {
      "id": 65,
      "seek": 22810,
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      "text": "如果你的显存是16G的话,你就选择这个17B。",
      "tokens": [
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      "compression_ratio": 1.5508474576271187,
      "no_speech_prob": 1.64867668128732e-11,
      "source": "whisper",
      "text_original": "如果你的显存是16G的话,你就选择这个17G。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 66,
      "seek": 22810,
      "start": 233.1,
      "end": 234.1,
      "text": "也是可以装到下载。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.1979998118841826,
      "compression_ratio": 1.5508474576271187,
      "no_speech_prob": 1.64867668128732e-11,
      "source": "whisper"
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    {
      "id": 67,
      "seek": 22810,
      "start": 234.1,
      "end": 237.1,
      "text": "因为它有部分可能会装到内存里面。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.64867668128732e-11,
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    {
      "id": 68,
      "seek": 22810,
      "start": 237.1,
      "end": 239.1,
      "text": "所以16G也是可以用的。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.1979998118841826,
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      "no_speech_prob": 1.64867668128732e-11,
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    {
      "id": 69,
      "seek": 22810,
      "start": 239.1,
      "end": 244.1,
      "text": "然后这个如果你的内存在16G的话,你就选择这个Q6量化的。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.1979998118841826,
      "compression_ratio": 1.5508474576271187,
      "no_speech_prob": 1.64867668128732e-11,
      "source": "whisper",
      "text_original": "然后这个如果你的内存在LM4G的话,你就选择这个Q6样化的。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 70,
      "seek": 22810,
      "start": 244.1,
      "end": 249.1,
      "text": "然后如果你的显存有29G以上的话,你就可以选择这个Q8量化的。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.1979998118841826,
      "compression_ratio": 1.5508474576271187,
      "no_speech_prob": 1.64867668128732e-11,
      "source": "whisper",
      "text_original": "然后如果你的显存有29G以上的话,你就可以选择这个Q8样化的。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 71,
      "seek": 22810,
      "start": 249.1,
      "end": 252.1,
      "text": "下载完成后,我们来测试一下连通性。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.1979998118841826,
      "compression_ratio": 1.5508474576271187,
      "no_speech_prob": 1.64867668128732e-11,
      "source": "whisper",
      "text_original": "下载完成后,我们来测试一下连通信。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 72,
      "seek": 25210,
      "start": 252.1,
      "end": 255.1,
      "text": "点击左侧的这个紫色的logo。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.11775314166981687,
      "compression_ratio": 1.3255813953488371,
      "no_speech_prob": 1.4517329179064564e-11,
      "source": "whisper",
      "text_original": "紧左侧的这个紫色的logo。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 73,
      "seek": 25210,
      "start": 255.1,
      "end": 258.1,
      "text": "接着我们选择顶部要加载的模型。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.11775314166981687,
      "compression_ratio": 1.3255813953488371,
      "no_speech_prob": 1.4517329179064564e-11,
      "source": "whisper"
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    {
      "id": 74,
      "seek": 25210,
      "start": 258.1,
      "end": 261.1,
      "text": "然后接着在选择我们的模型,它就会自动加载。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.4517329179064564e-11,
      "source": "whisper"
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    {
      "id": 75,
      "seek": 25210,
      "start": 261.1,
      "end": 266.1,
      "text": "加载成功后,我们任意发送一条消息,能够收到对应的回复。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.11775314166981687,
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      "no_speech_prob": 1.4517329179064564e-11,
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    {
      "id": 76,
      "seek": 25210,
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      "end": 268.1,
      "text": "就说明我们的LM Studio安装成功了。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.4517329179064564e-11,
      "source": "whisper",
      "text_original": "就说明我们的LM19安装成功了。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 77,
      "seek": 25210,
      "start": 268.1,
      "end": 272.1,
      "text": "千问3.8-27B这个模型,除了它的agent能力很强外。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.11775314166981687,
      "compression_ratio": 1.3255813953488371,
      "no_speech_prob": 1.4517329179064564e-11,
      "source": "whisper",
      "text_original": "千元3.8RTB这个模型,除了它的agent能力很强外。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 78,
      "seek": 25210,
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      "end": 275.1,
      "text": "它还是一个原生的视觉语言模型。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.4517329179064564e-11,
      "source": "whisper"
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    {
      "id": 79,
      "seek": 25210,
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      "end": 276.1,
      "text": "因此它是可以看图的。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.4517329179064564e-11,
      "source": "whisper"
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    {
      "id": 80,
      "seek": 25210,
      "start": 276.1,
      "end": 280.1,
      "text": "这里我找了一堆比的图片,看它能不能数得清楚。",
      "tokens": [
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      "no_speech_prob": 1.4517329179064564e-11,
      "source": "whisper"
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    {
      "id": 81,
      "seek": 28010,
      "start": 280.1,
      "end": 282.1,
      "text": "好,这里它已经数完了,我们来看一下。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.4198672619591957e-11,
      "source": "whisper"
    },
    {
      "id": 82,
      "seek": 28010,
      "start": 282.1,
      "end": 285.1,
      "text": "它这里说15支笔,我们来数一数对不对。",
      "tokens": [
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      "temperature": 0,
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      "compression_ratio": 1.2463343108504399,
      "no_speech_prob": 1.4198672619591957e-11,
      "source": "whisper",
      "text_original": "它这里说15之笔,我们来数一数对不对。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 83,
      "seek": 28010,
      "start": 285.1,
      "end": 293.1,
      "text": "123456789141112131415。",
      "tokens": [
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      "no_speech_prob": 1.4198672619591957e-11,
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    {
      "id": 84,
      "seek": 28010,
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      "text": "成功数对了,而且它也没有被那支笔杆和笔帽没有在一起的笔给干扰。",
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      "temperature": 0,
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      "compression_ratio": 1.2463343108504399,
      "no_speech_prob": 1.4198672619591957e-11,
      "source": "whisper",
      "text_original": "成功数对了,而且它也没有被这一个笔杆和笔帽没有在一起的笔给干扰。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 85,
      "seek": 28010,
      "start": 298.1,
      "end": 303.1,
      "text": "接着我们将本地部署的千问3.8-27B进入到你们的DeepSeek Harness之中。",
      "tokens": [
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      "temperature": 0,
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      "compression_ratio": 1.2463343108504399,
      "no_speech_prob": 1.4198672619591957e-11,
      "source": "whisper",
      "text_original": "接着我们将本地语题的千元3.8RTB进入到你们的DeepSyncHoney之中。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 86,
      "seek": 28010,
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      "end": 306.1,
      "text": "首先我们在LM Studio里面点击左侧的第二个图标。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.4198672619591957e-11,
      "source": "whisper",
      "text_original": "首先我们在LM1999里面点击左侧的第二个图标。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 87,
      "seek": 28010,
      "start": 306.1,
      "end": 309.1,
      "text": "我们把运行按钮打开,然后点击这里的加载模型。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.4198672619591957e-11,
      "source": "whisper"
    },
    {
      "id": 88,
      "seek": 30910,
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      "end": 312.1,
      "text": "选择我们要加载的千问3.8-27B。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.09772035501299113,
      "compression_ratio": 1.2014652014652014,
      "no_speech_prob": 1.6380669390358982e-11,
      "source": "whisper",
      "text_original": "选择我们要加载的千元3.8RTB。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 89,
      "seek": 30910,
      "start": 312.1,
      "end": 315.1,
      "text": "加载完成后我们来到DeepSeek Harness之中。",
      "tokens": [
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      "temperature": 0,
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      "compression_ratio": 1.2014652014652014,
      "no_speech_prob": 1.6380669390358982e-11,
      "source": "whisper",
      "text_original": "加载完成后我们来到DeepSyncHoney之中。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
    },
    {
      "id": 90,
      "seek": 30910,
      "start": 315.1,
      "end": 318.1,
      "text": "点击左下角的设置,点击模型。",
      "tokens": [
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      "compression_ratio": 1.2014652014652014,
      "no_speech_prob": 1.6380669390358982e-11,
      "source": "whisper"
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    {
      "id": 91,
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      "start": 318.1,
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      "text": "然后我们可以点击添加自定义配置方。",
      "tokens": [
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      "compression_ratio": 1.2014652014652014,
      "no_speech_prob": 1.6380669390358982e-11,
      "source": "whisper"
    },
    {
      "id": 92,
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      "end": 324.1,
      "text": "ProviderID这里一定要是小写字母开头。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.6380669390358982e-11,
      "source": "whisper",
      "text_original": "ProvideID这里一定要是小写字幕开头。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
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    {
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      "start": 324.1,
      "end": 326.1,
      "text": "显示名称这里我们随便填。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.6380669390358982e-11,
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    {
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      "text": "API地址这里我们从LMStudio这里复制一下这个地址。",
      "tokens": [
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      "temperature": 0,
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      "compression_ratio": 1.2014652014652014,
      "no_speech_prob": 1.6380669390358982e-11,
      "source": "whisper"
    },
    {
      "id": 95,
      "seek": 33010,
      "start": 330.1,
      "end": 333.1,
      "text": "注意这个地址粘过来之后需要在后面加上斜线V1。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.3760628053693935e-11,
      "source": "whisper"
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      "text": "API协议选择OpenAI Compatible。",
      "tokens": [
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      "source": "whisper",
      "text_original": "API协议选择OpenAI CovLacens。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
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    {
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      "text": "模型目录这里我们同样是从LMStudio这里复制过来。",
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      "no_speech_prob": 1.3760628053693935e-11,
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    {
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      "text": "助理这里很容易复制过来空格。",
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      "temperature": 0,
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      "no_speech_prob": 1.3760628053693935e-11,
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    {
      "id": 99,
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      "text": "别问我为什么知道。",
      "tokens": [
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      "no_speech_prob": 1.3760628053693935e-11,
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    {
      "id": 100,
      "seek": 33010,
      "start": 343.1,
      "end": 346.1,
      "text": "我们在对话窗口将模型修改为千问3.8,",
      "tokens": [
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      "temperature": 0,
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      "compression_ratio": 1.1682242990654206,
      "no_speech_prob": 1.3760628053693935e-11,
      "source": "whisper",
      "text_original": "我们在对话空中将模型修改为千问3.8,",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
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    {
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      "text": "然后进行连通性测试。",
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      "temperature": 0,
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      "source": "whisper",
      "text_original": "然后进行联通性测试。",
      "correction_applied": true,
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    {
      "id": 102,
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      "text": "回复任意一句话能够收到对应的回应就为成功。",
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    {
      "id": 103,
      "seek": 35110,
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      "text": "大家在本地部署的过程中遇到任何问题,",
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      "no_speech_prob": 1.5695160449080703e-11,
      "source": "whisper"
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    {
      "id": 104,
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      "text": "也可以先给Harness配上个线上的API。",
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      "source": "whisper",
      "text_original": "也可以先给HaniZ配上个线上的API。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
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    {
      "id": 105,
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      "text": "让Harness直接帮你修。",
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      "temperature": 0,
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      "text_original": "让HaniZ直接帮你修。",
      "correction_applied": true,
      "correction_source": "llm_correct_a_presegmentation"
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    {
      "id": 106,
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      "text": "这样我们让他生成一个飞行小游戏来测试一下吧。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.5695160449080703e-11,
      "source": "whisper",
      "text_original": "这样我们让他生存一个飞行小游戏来测试一下吧。",
      "correction_applied": true,
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    {
      "id": 107,
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      "text": "与此同时我们当然他去写一个科幻小说吧。",
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    {
      "id": 108,
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      "text": "毕竟本地运行Token可是不要钱的啊。",
      "tokens": [
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      "source": "whisper",
      "text_original": "毕竟本地运行TOK可是不要钱的啊。",
      "correction_applied": true,
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    {
      "id": 109,
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      "text": "哎,怎么好像运行的还挺流畅的。",
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    {
      "id": 110,
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      "text": "估计还有余地。",
      "tokens": [
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      "no_speech_prob": 1.5695160449080703e-11,
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    {
      "id": 111,
      "seek": 35110,
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      "end": 371.1,
      "text": "再给安排个生成天体的任务吧。",
      "tokens": [
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      "no_speech_prob": 1.5695160449080703e-11,
      "source": "whisper"
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    {
      "id": 112,
      "seek": 35110,
      "start": 371.1,
      "end": 373.1,
      "text": "生产队的驴子估计没我电脑忙了。",
      "tokens": [
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    {
      "id": 113,
      "seek": 35110,
      "start": 373.1,
      "end": 375.1,
      "text": "我只求你别冒烟啊。",
      "tokens": [
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    },
    {
      "id": 114,
      "seek": 35110,
      "start": 375.1,
      "end": 378.1,
      "text": "可以看到LMStudio这里三个任务都给跑起来了。",
      "tokens": [
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    {
      "id": 115,
      "seek": 37810,
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      "end": 380.1,
      "text": "我这电脑的风扇也是呜呜的转。",
      "tokens": [
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      "source": "whisper"
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    {
      "id": 116,
      "seek": 37810,
      "start": 380.1,
      "end": 382.1,
      "text": "哎,好像开始卡了,卡了,不行了。",
      "tokens": [
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      "source": "whisper"
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    {
      "id": 117,
      "seek": 37810,
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      "end": 385.1,
      "text": "我这鼠标已经非常难用了。",
      "tokens": [
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    {
      "id": 118,
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      "text": "不过看这个任务它还是在运行的。",
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    {
      "id": 119,
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      "text": "但是我这已经操作不了电脑了。",
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    {
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      "text": "我想把鼠标移动到银子这里啊。",
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    {
      "id": 121,
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      "text": "但是非常难移动啊。",
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    {
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      "text": "而且点机也没什么反应啊。",
      "tokens": [
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    {
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      "text": "这个应该是内存平行导致的。",
      "tokens": [
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    {
      "id": 124,
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      "text": "因为本次这个电脑的话是32G内存。",
      "tokens": [
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    {
      "id": 125,
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      "text": "运行一个任务的话应该是一点问题没有。",
      "tokens": [
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    {
      "id": 126,
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      "text": "但是如果运行多个任务的话,",
      "tokens": [
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      "temperature": 0,
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    {
      "id": 127,
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      "text": "它上线文都夹在内存中是肯定撑不住的。",
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    {
      "id": 128,
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      "text": "所以大家一定要根据自己的情况去执行这个任务。",
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    {
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      "text": "包括其实LM199它其实是可以设置上远文长度的。",
      "tokens": [
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    {
      "id": 130,
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      "text": "如果你内存不够高的话,尽量设置的低一点。",
      "tokens": [
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      "no_speech_prob": 1.3315882251008215e-11,
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    },
    {
      "id": 131,
      "seek": 40710,
      "start": 417.1,
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      "text": "比如说64K以下。",
      "tokens": [
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      "no_speech_prob": 1.3315882251008215e-11,
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    },
    {
      "id": 132,
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      "text": "而且不要像我这样一聘同时执行好几个任务。",
      "tokens": [
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    {
      "id": 133,
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      "text": "我替你试过了,你就不要去试了。",
      "tokens": [
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    {
      "id": 134,
      "seek": 40710,
      "start": 424.1,
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      "text": "把电脑搞猫烟可不是那玩的。",
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    {
      "id": 135,
      "seek": 40710,
      "start": 426.1,
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      "text": "我这里想说任务管理器里面结束任务。",
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    {
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      "text": "但是任务管理器已经卡成一张精彩图片了。",
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    {
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      "text": "我这里是好不容易把录片软件给退了,",
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    {
      "id": 138,
      "seek": 43310,
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      "end": 435.1,
      "text": "然后才能操作鼠标。",
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    {
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      "text": "把后来提要两个字给关闭了,最后才生成的。",
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    {
      "id": 140,
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      "text": "我们来看一下生成结果吧。",
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    {
      "id": 141,
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      "text": "生成结果还是很出乎我的意料的。",
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    {
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      "text": "你别看这只是一个简单的飞行设计小游戏。",
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    {
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      "text": "它处理的这些碰撞啊,移动啊,",
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    {
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      "text": "包括敌人的生成啊,都没有任何问题。",
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    {
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      "text": "这对于一个27B的小模型来说是非常厉害的。",
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    {
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      "text": "而且这还是只是一轮提示词的情况下。",
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    {
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      "text": "如果多轮对话的话,它的潜力还有非常大的发布空间。",
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      "text": "我认为这个模型是真的可以作为生产力的一个模型了。",
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      "text": "然后我们再来看一下它生成的这个空外小说。",
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      "text": "虽然我中途停止了,",
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    {
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      "text": "但它生成了也很多东西了。",
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      "text": "而且我看它这个内容真的让我非常的震惊。",
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      "text": "我就一点看不出这是AI写的。",
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      "text": "虽然它这个东西写的是一个老套的星际移民的故事,",
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      "text": "但它这个东西让我看设定的时候,",
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      "text": "我一下子想到就是美剧那个什么羊毛战记,就是末日地宝那个。",
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      "text": "人们挤在地下桶汤里边,然后高层靠着隐瞒真相维持生存的秩序。",
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      "text": "包括它的一些场景设定,比如说,",
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      "text": "集水柱,然后墙上的救赎,就是人死了之后它会,",
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      "text": "其实它不是说是死了就直接上传数据了,",
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      "text": "而且它也保留了集水柱。",
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      "text": "我就觉得就很有画面感。",
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      "text": "就这个画面可能最后做出来就很震撼。",
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    {
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      "text": "好了,感谢大家能听到我讲到这里。",
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      "text": "后面我会给大家分享更多AI相关的知识。",
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      "text": "大家如果感兴趣的话记得关注一下。",
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      "text": "我是VV,在这里谢过大家了。",
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