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  "text": "本地大模型到底能不能打?你看哈,我的這個文件夾有70多個文件。非常的凌亂,在Deepseek Harness裡面,我送出了提示詞。不一會兒,四個文件夾建好了,文件全部歸類整齊,還生成了一份整理報告。從我發出命令到結束全部的任務,我沒有碰過一次鍵盤。這個就是千萬3.827B,這個部署在我電腦本地的大模型。和Deepseek Harness配合起來的效果,都是本地哈,網線你都不用插,純純的國內生態,零費用。所以說,到底本地大模型是否有一個大模型?\n能不能幹活?我們的電腦帶不帶的動?本地模型能幹哪些事?不著急哈,今天都給你答案。又是手把手的教程,點好收藏與關注,我們這就發車。\n你如果用Codex,Code,Code,這些雲端Agent就繞不開幾個問題。價格高,本地讀寫麻煩,隱私不能被保護,還可能被封號。而千萬呢,剛剛推出了3.827B,這個本地大模型。\n但是直接跑在你的電腦上的,上面所有的問題就解決了。雲端大模型呢,咱們只能租用,本地的才是完全屬於你。\n不過本地模型啊,吃硬件,對於普通人來說呢,安裝還有門檻。速度和智力呢,肯定也比雲端的主流模型可能要弱一點。\n那今天咱們就來看一下哈,這套組合到底能不能打?\n今天內容有點長啊,而且呢,建議有時間的時候慢慢看,最好是一步一步的跟著做。\n咱第一步呢,需要下載模型。\n我們打開摩搭社區,都不用搜哈,在主頁上就能看到千萬3.8了。\n如果沒有的話呢,你也可以點這個放大鏡,直接搜的到。\n這個呢,你可以理解為是原廠原裝的模型,大概有50多G。\n所以硬件的要求啊,特別的高,咱普通的家用電腦,咱就不要想了。\n不過呢,社區的大神多哈,下面能看到一個後墜GGUF的。\n這個GGUF呢,是大模型的一種壓縮包裝格式,類似於像音樂文件一樣的。\n官方的50G的版本呢,就等於是無損母帶WAV格式。\n一首歌呀,幾十兆上百兆都很正常。\n這個GGUF壓縮之後呢,就像是MP3了。\n一首歌呢,也就是3、5兆的體積。\n常人呢,還一般都聽不出來音質有什麼區別。\n這個作者有點厲害哈,可以說是全世界做模型量化。\n最出名的。光是這一個模型的倉庫,一個月的下載量就有700多萬次。\n基本上呢,是家用電腦最適合的選擇了。\n在模型文件這個裡面,你能看到一大堆的模型清單。\n有幾個G的,還有幾十個G的都有。\n選擇也不難哈,咱們簡單粗暴的理解,你顯存多少個G,\n基本上呢,就是選多少G的模型。\n但注意哈,一定要留一定的餘量。\n因為你運行的時候呢,還會有上下文緩存這樣的一些開銷。\n我是16G的5060Ti,咱保守一點哈。\n我就選這個13G的Q3版本。\n當然呢,選16G的Q4,它也不是不能跑,只是速度呢,會慢一點。\n你可以多下幾個版本來自己比較一下。\n大模型只是腦子哈,要跑起來呢,還需要一個容器。\n趁它在下載的時候啊,咱們再給它安裝一套運行環境。\n前幾天,Deep-seek Harness不是才開源了嗎?\n今天咱們就用它。\n主頁上是它的主張,一切皆插件。\n安裝也不難哈,提示也是說一鍵使用。\n不過有一個前提,你的電腦必須有依賴的環境。\n它需要一個叫做Node.js的環境。\n但不用管它是幹嘛的哈。\n打開它的主頁,點這個綠色的按鈕,下載對應的版本。\n安裝呢,就是下一步下一步的事。\n這些具體的過程呢,我都會放到配套的文檔裡面。\n如果說你以前用過別的Aten的工具啊,大概率Node.js是你部署過的。\n這一步呢,就可以省略了。\n我們按下Wn加R這個快接鍵,輸入CMD,回車。\n彈出來一個黑乎乎的窗口,這個呢叫做終端。\n你啥都不用管哈,在Harness的主頁上,點這個複製按鈕。\n到終端裡面,Ctrl加V粘貼上,然後回車。\n按照提示,輸入字母Y,再回車。\n下面這個滾動起來,就表示已經開始安裝了。\n由於它可能會下載很多的東西,需要多等待一會兒。\n好了,這個時候呢,安裝結束了。\n它自動彈出了瀏覽器。\n如果說你的沒有彈出呢,你就把這一行地址,複製到你的瀏覽器。\n就OK,封面就有內側聲明,表示啊,這個產品還在測試階段。\n展開之後呢,還會提示你輸入Deep-seek的API-key。\n由於我們今天用的是本地大模型,所以這一步呢,就跳過就好。\n看到了這個黑鯊魚,咱們就算是正式的進入到了Deep-seek Harness的內部了。\n這會兒呢,仍然還用不了,因為Harness本身它不能直接的裝入一個本地模型。\n只能通過兼容OpenAI的API方式來調用。\n所以說我們還需要一個工具,把這個模型給它跑起來。\n這個工具呢,叫做Lama.CPP。\n現在非常火的什麼Olama呀,LM Studio啊,這些引擎啊,底層用的也都是它。\n打開它的地址,點開這裡,把頁面往下拉,找到符合你的操作系統和顯卡的版本,下載到本地。\n因為我是Windows系統哈,顯卡是50系的。\n所以我下載COODA13這個版本。\n如果你是稍微老一點的,什麼30系40系的顯卡,就要選COODA12這個版本了。\n文件不大,只有140Mbps。\n下載完呢,是一個壓縮包。\n解壓不用安裝。\n我的i盤是一個固態硬盤。\n在這裡呢,我新建了一個MODE文件夾。\n把Lama和剛才下載好的CENW3.8全部都放到了這個裡面。\n我也強烈的建議啊,你們也要放到固態硬盤裡。\n盡量啊,不要放機械硬盤,速度呢才會更快。\n就在這個文件夾裡,我們點擊鼠標的右鍵,選擇在終端中打開。\n咱們輸入這樣的一條命令。\n這個命令呢,我也會放到文檔裡哈。\n大概意思呢,就是告訴系統。\n把這個模型交給引擎。\n而且盡量把模型層放進顯卡。\n然後在本機的8080端口開一個接口,等著別的程序來調用它。\n回車之後呢,能看到它開始滾動了。\n直到你能看到一行127打頭的地址,就表示已經啟動好了。\n你可以按住鍵盤上的Ctrl鍵,點擊這個地址。\n它就會自動的打開你的默認瀏覽器。\n輸入一個你好,如果說它能回覆的話,說明大模型已經啟動成功了。\n好了,到這兒啊,冰箱門都打開了。\n最後一步,咱就是把大象放進去了。\n注意哈,剛才那個終端啊,這個時候是不能關的。\n你關了,大模型的服務就停了哈。\n以後你重新要打開Harness的話呢,不需要重新安裝。\n在終端裡頭,輸入這個命令。\n在Harness的頁面,咱們進入設置,模型,添加自定義提供方。\n你可以照著我來填哈。\n第一個框呢,必須是小寫字母開頭。\n顯示名稱,可以取一個你自己記得住的名字。\nAPI地址呢,就是剛才那個後綴是8080的那一段。\n但是後面啊,一定要有一個斜槓V1。\nAPI協議保持不變,密要,由於我們是本地模型哈。\n所以說就隨便填了,咱就敲了三個1。\n點一下獲取可用模型,就能看到咱們的千問3.8了。\n點下面的創建提供方,這就添加成功了。\n回到Harness主頁,這個時候呢,你還選不了模型,必須還有添加工作區。\n添加好了之後啊,就可以選擇模型了。\n趙莉,咱發一個你好,第一次可能想應慢一點。\n能看到它的回复,咱今天就算是全部的大功告成了。\n能回你好,不算本事啊,得看它能不能幹活。\n我們先看一個最直觀的例子。\n這個文件夾裡面有77個凌亂的文件,什麼格式都有。\n文件名也很亂。\n如果你的文件夾也這樣,按摩走一波。\n手動整理啊,當然很麻煩了。\n我在Harness裡面,按摩了這個文件夾。\n提示詞是這麼寫的。\n我送出命令之後啊,能看到它開始先列舉文件,統計文件數量。\n然後挨個的查看文件內容,匯報77個文件清理好了,準備分類。\n等到13分鐘的時候,終於看到幾個文件夾出現了。\n根下呢,還有一個report和復盤。看來根下兩個文件是臨時文件。\n它又把它清理掉了。22分鐘左右,整理報告終於出來了。\n你覺得這份報告寫的詳細嗎?\n我們的這個任務一共花了20多分鐘。這個時間還是有點久。\n當然呢,這個只是參考。\n畢竟呢,是它第一次跑任務。\n可能啊,有些環境啊工具啊還不具備。\n而且呢,我的後臺開著很多的agent,還有陸皮軟件啊這些,都會有一定的影響。\n都免費了,還要啥自行車呀?\n你的硬件不同,任務不同,當然可以自己去測。\n剛才呢,只是文件的整理。它能幹的事兒啊,遠遠不止這些。\n通常呢,有幾個共同的特點。\n處理大量的本地文件,規則相對明確,反覆發聲,還有一定的隱私性。\n我提煉了三大類,你可以來對號入座。\n第一類呢,是高頻任務,像什麼清理文件啊,給筆記打標圈啊,寫摘要啊這些。\n第二類呢,隱私,敏感型的任務。\n像是你的個人日記啊,商務文件啊,這些你當然不想上傳到雲端了。\n還有就是,常駐後台型的任務。\n比如說呢,你讓它監聽某一個文件家,一旦發生變化,就觸發任務。\n你可以不用每次都打開對話框,重新的下命令,它自己就能搞定了。\n感謝你能看到這裡啊,總的來說呢,只要你的硬件跑得動,這套模式呢,它是能幹活的。\n但是本地大模型呢,它不是雲端Agent的平替。\n雲端模型呢,負責最難的判斷,本地模型呢,負責長期的執行。\n兩邊都有用。\n我們今天不僅是部署了一套模型,更是搭建了一套本地的AI系統。\n以後啊,換掉簽文,換掉Harness,你的本地的上下文,永遠都在。\n和往常一樣的,今天用到的全部的下載地址而運行命令。\n還有這次案例的完整提示詞,所有資料我也都打包好了。\n需要的同學,評論區提我。\n我是船長,讓我們下期再見,拜拜。",
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      "text": "本地大模型到底能不能打?你看哈,我的這個文件夾有70多個文件。非常的凌亂,在Deepseek Harness裡面,我送出了提示詞。不一會兒,四個文件夾建好了,文件全部歸類整齊,還生成了一份整理報告。從我發出命令到結束全部的任務,我沒有碰過一次鍵盤。這個就是千萬3.827B,這個部署在我電腦本地的大模型。和Deepseek Harness配合起來的效果,都是本地哈,網線你都不用插,純純的國內生態,零費用。所以說,到底本地大模型是否有一個大模型?",
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      "text": "能不能幹活?我們的電腦帶不帶的動?本地模型能幹哪些事?不著急哈,今天都給你答案。又是手把手的教程,點好收藏與關注,我們這就發車。",
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      "text": "你如果用Codex,Code,Code,這些雲端Agent就繞不開幾個問題。價格高,本地讀寫麻煩,隱私不能被保護,還可能被封號。而千萬呢,剛剛推出了3.827B,這個本地大模型。",
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      "text": "但是直接跑在你的電腦上的,上面所有的問題就解決了。雲端大模型呢,咱們只能租用,本地的才是完全屬於你。",
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      "no_speech_prob": 1.478636137142164e-11,
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    {
      "id": 4,
      "seek": 5610,
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      "text": "不過本地模型啊,吃硬件,對於普通人來說呢,安裝還有門檻。速度和智力呢,肯定也比雲端的主流模型可能要弱一點。",
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    {
      "id": 5,
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      "start": 71.68,
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      "text": "那今天咱們就來看一下哈,這套組合到底能不能打?",
      "tokens": [
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    {
      "id": 6,
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      "text": "今天內容有點長啊,而且呢,建議有時間的時候慢慢看,最好是一步一步的跟著做。",
      "tokens": [
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    {
      "id": 7,
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      "text": "咱第一步呢,需要下載模型。",
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    {
      "id": 8,
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      "text": "我們打開摩搭社區,都不用搜哈,在主頁上就能看到千萬3.8了。",
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    {
      "id": 9,
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      "text": "如果沒有的話呢,你也可以點這個放大鏡,直接搜的到。",
      "tokens": [
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    {
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      "text": "這個呢,你可以理解為是原廠原裝的模型,大概有50多G。",
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    {
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      "text": "所以硬件的要求啊,特別的高,咱普通的家用電腦,咱就不要想了。",
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    {
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      "text": "不過呢,社區的大神多哈,下面能看到一個後墜GGUF的。",
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    {
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      "text": "這個GGUF呢,是大模型的一種壓縮包裝格式,類似於像音樂文件一樣的。",
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    {
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      "text": "官方的50G的版本呢,就等於是無損母帶WAV格式。",
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    {
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      "text": "一首歌呀,幾十兆上百兆都很正常。",
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    {
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      "text": "這個GGUF壓縮之後呢,就像是MP3了。",
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    {
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      "text": "一首歌呢,也就是3、5兆的體積。",
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    {
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      "text": "常人呢,還一般都聽不出來音質有什麼區別。",
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    {
      "id": 19,
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      "text": "這個作者有點厲害哈,可以說是全世界做模型量化。",
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    {
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      "text": "最出名的。光是這一個模型的倉庫,一個月的下載量就有700多萬次。",
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    {
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      "text": "基本上呢,是家用電腦最適合的選擇了。",
      "tokens": [
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    {
      "id": 22,
      "seek": 13110,
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      "end": 143.1,
      "text": "在模型文件這個裡面,你能看到一大堆的模型清單。",
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    {
      "id": 23,
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      "text": "有幾個G的,還有幾十個G的都有。",
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    {
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      "text": "選擇也不難哈,咱們簡單粗暴的理解,你顯存多少個G,",
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    {
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      "text": "基本上呢,就是選多少G的模型。",
      "tokens": [
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    },
    {
      "id": 26,
      "seek": 13110,
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      "end": 153.6,
      "text": "但注意哈,一定要留一定的餘量。",
      "tokens": [
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    {
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      "text": "因為你運行的時候呢,還會有上下文緩存這樣的一些開銷。",
      "tokens": [
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    {
      "id": 28,
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      "text": "我是16G的5060Ti,咱保守一點哈。",
      "tokens": [
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    {
      "id": 29,
      "seek": 16060,
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      "text": "我就選這個13G的Q3版本。",
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      "temperature": 0,
      "avg_logprob": -0.15276094872181809,
      "compression_ratio": 1.1515151515151516,
      "no_speech_prob": 2.0068738337819525e-11,
      "source": "whisper"
    },
    {
      "id": 30,
      "seek": 16060,
      "start": 163.6,
      "end": 167.6,
      "text": "當然呢,選16G的Q4,它也不是不能跑,只是速度呢,會慢一點。",
      "tokens": [
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      "no_speech_prob": 2.0068738337819525e-11,
      "source": "whisper"
    },
    {
      "id": 31,
      "seek": 16060,
      "start": 167.6,
      "end": 170.1,
      "text": "你可以多下幾個版本來自己比較一下。",
      "tokens": [
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      "no_speech_prob": 2.0068738337819525e-11,
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    {
      "id": 32,
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      "start": 170.1,
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      "text": "大模型只是腦子哈,要跑起來呢,還需要一個容器。",
      "tokens": [
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      "no_speech_prob": 2.0068738337819525e-11,
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    },
    {
      "id": 33,
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      "text": "趁它在下載的時候啊,咱們再給它安裝一套運行環境。",
      "tokens": [
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    },
    {
      "id": 34,
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      "text": "前幾天,Deep-seek Harness不是才開源了嗎?",
      "tokens": [
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    {
      "id": 35,
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      "text": "今天咱們就用它。",
      "tokens": [
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      "no_speech_prob": 2.2243722488934914e-11,
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    },
    {
      "id": 36,
      "seek": 18360,
      "start": 184.6,
      "end": 187.6,
      "text": "主頁上是它的主張,一切皆插件。",
      "tokens": [
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    },
    {
      "id": 37,
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      "text": "安裝也不難哈,提示也是說一鍵使用。",
      "tokens": [
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      "no_speech_prob": 2.2243722488934914e-11,
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    {
      "id": 38,
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      "start": 190.6,
      "end": 193.6,
      "text": "不過有一個前提,你的電腦必須有依賴的環境。",
      "tokens": [
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      "no_speech_prob": 2.2243722488934914e-11,
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    },
    {
      "id": 39,
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      "text": "它需要一個叫做Node.js的環境。",
      "tokens": [
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    },
    {
      "id": 40,
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      "text": "但不用管它是幹嘛的哈。",
      "tokens": [
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      "no_speech_prob": 2.2243722488934914e-11,
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    },
    {
      "id": 41,
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      "text": "打開它的主頁,點這個綠色的按鈕,下載對應的版本。",
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    },
    {
      "id": 42,
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      "text": "安裝呢,就是下一步下一步的事。",
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    },
    {
      "id": 43,
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      "text": "這些具體的過程呢,我都會放到配套的文檔裡面。",
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    {
      "id": 44,
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      "text": "如果說你以前用過別的Aten的工具啊,大概率Node.js是你部署過的。",
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      "no_speech_prob": 1.9463831588129032e-11,
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    {
      "id": 45,
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      "text": "這一步呢,就可以省略了。",
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    {
      "id": 46,
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      "text": "我們按下Wn加R這個快接鍵,輸入CMD,回車。",
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    },
    {
      "id": 47,
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      "start": 217.6,
      "end": 220.6,
      "text": "彈出來一個黑乎乎的窗口,這個呢叫做終端。",
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    {
      "id": 48,
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      "text": "你啥都不用管哈,在Harness的主頁上,點這個複製按鈕。",
      "tokens": [
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      "no_speech_prob": 1.9463831588129032e-11,
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    {
      "id": 49,
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      "text": "到終端裡面,Ctrl加V粘貼上,然後回車。",
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      "no_speech_prob": 1.9463831588129032e-11,
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    {
      "id": 50,
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      "text": "按照提示,輸入字母Y,再回車。",
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    {
      "id": 51,
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      "text": "下面這個滾動起來,就表示已經開始安裝了。",
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    {
      "id": 52,
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      "text": "由於它可能會下載很多的東西,需要多等待一會兒。",
      "tokens": [
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    {
      "id": 53,
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      "text": "好了,這個時候呢,安裝結束了。",
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    },
    {
      "id": 54,
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      "end": 243.6,
      "text": "它自動彈出了瀏覽器。",
      "tokens": [
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    {
      "id": 55,
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      "text": "如果說你的沒有彈出呢,你就把這一行地址,複製到你的瀏覽器。",
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      "no_speech_prob": 1.6678432940286925e-11,
      "source": "whisper"
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    {
      "id": 56,
      "seek": 23560,
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      "end": 251.6,
      "text": "就OK,封面就有內側聲明,表示啊,這個產品還在測試階段。",
      "tokens": [
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    {
      "id": 57,
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      "text": "展開之後呢,還會提示你輸入Deep-seek的API-key。",
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      "text": "由於我們今天用的是本地大模型,所以這一步呢,就跳過就好。",
      "tokens": [
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    {
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      "text": "看到了這個黑鯊魚,咱們就算是正式的進入到了Deep-seek Harness的內部了。",
      "tokens": [
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      "text": "這會兒呢,仍然還用不了,因為Harness本身它不能直接的裝入一個本地模型。",
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    {
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      "text": "只能通過兼容OpenAI的API方式來調用。",
      "tokens": [
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    {
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      "text": "所以說我們還需要一個工具,把這個模型給它跑起來。",
      "tokens": [
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    {
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      "text": "這個工具呢,叫做Lama.CPP。",
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      "text": "現在非常火的什麼Olama呀,LM Studio啊,這些引擎啊,底層用的也都是它。",
      "tokens": [
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    {
      "id": 65,
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      "text": "打開它的地址,點開這裡,把頁面往下拉,找到符合你的操作系統和顯卡的版本,下載到本地。",
      "tokens": [
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      "text": "因為我是Windows系統哈,顯卡是50系的。",
      "tokens": [
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    {
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      "text": "所以我下載COODA13這個版本。",
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    {
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      "text": "如果你是稍微老一點的,什麼30系40系的顯卡,就要選COODA12這個版本了。",
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    {
      "id": 69,
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      "text": "文件不大,只有140Mbps。",
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    {
      "id": 70,
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      "text": "下載完呢,是一個壓縮包。",
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    {
      "id": 71,
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      "text": "解壓不用安裝。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.811804872797751e-11,
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    {
      "id": 72,
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      "text": "我的i盤是一個固態硬盤。",
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      "temperature": 0,
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    {
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      "text": "在這裡呢,我新建了一個MODE文件夾。",
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    {
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      "text": "把Lama和剛才下載好的CENW3.8全部都放到了這個裡面。",
      "tokens": [
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    {
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      "text": "我也強烈的建議啊,你們也要放到固態硬盤裡。",
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    {
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      "text": "盡量啊,不要放機械硬盤,速度呢才會更快。",
      "tokens": [
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    {
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      "text": "就在這個文件夾裡,我們點擊鼠標的右鍵,選擇在終端中打開。",
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    {
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      "text": "咱們輸入這樣的一條命令。",
      "tokens": [
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    {
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      "text": "這個命令呢,我也會放到文檔裡哈。",
      "tokens": [
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    {
      "id": 80,
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      "text": "大概意思呢,就是告訴系統。",
      "tokens": [
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      "temperature": 0,
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    {
      "id": 81,
      "seek": 34060,
      "start": 340.6,
      "end": 342.6,
      "text": "把這個模型交給引擎。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.06886414275772271,
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      "id": 82,
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      "text": "而且盡量把模型層放進顯卡。",
      "tokens": [
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    {
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      "text": "然後在本機的8080端口開一個接口,等著別的程序來調用它。",
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    {
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      "text": "回車之後呢,能看到它開始滾動了。",
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      "id": 85,
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      "start": 352.6,
      "end": 357.6,
      "text": "直到你能看到一行127打頭的地址,就表示已經啟動好了。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.06886414275772271,
      "compression_ratio": 1.322674418604651,
      "no_speech_prob": 1.824921810889002e-11,
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    },
    {
      "id": 86,
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      "start": 357.6,
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      "text": "你可以按住鍵盤上的Ctrl鍵,點擊這個地址。",
      "tokens": [
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      "avg_logprob": -0.06886414275772271,
      "compression_ratio": 1.322674418604651,
      "no_speech_prob": 1.824921810889002e-11,
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    },
    {
      "id": 87,
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      "start": 361.6,
      "end": 364.6,
      "text": "它就會自動的打開你的默認瀏覽器。",
      "tokens": [
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      "avg_logprob": -0.06886414275772271,
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      "no_speech_prob": 1.824921810889002e-11,
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    },
    {
      "id": 88,
      "seek": 34060,
      "start": 364.6,
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      "text": "輸入一個你好,如果說它能回覆的話,說明大模型已經啟動成功了。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.06886414275772271,
      "compression_ratio": 1.322674418604651,
      "no_speech_prob": 1.824921810889002e-11,
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    },
    {
      "id": 89,
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      "start": 369.6,
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      "text": "好了,到這兒啊,冰箱門都打開了。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.08278969795473161,
      "compression_ratio": 1.3201320132013201,
      "no_speech_prob": 2.2476309008423456e-11,
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    },
    {
      "id": 90,
      "seek": 36960,
      "start": 371.6,
      "end": 374.6,
      "text": "最後一步,咱就是把大象放進去了。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.08278969795473161,
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      "no_speech_prob": 2.2476309008423456e-11,
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    },
    {
      "id": 91,
      "seek": 36960,
      "start": 374.6,
      "end": 377.6,
      "text": "注意哈,剛才那個終端啊,這個時候是不能關的。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.08278969795473161,
      "compression_ratio": 1.3201320132013201,
      "no_speech_prob": 2.2476309008423456e-11,
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    },
    {
      "id": 92,
      "seek": 36960,
      "start": 377.6,
      "end": 379.6,
      "text": "你關了,大模型的服務就停了哈。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.08278969795473161,
      "compression_ratio": 1.3201320132013201,
      "no_speech_prob": 2.2476309008423456e-11,
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    },
    {
      "id": 93,
      "seek": 36960,
      "start": 379.6,
      "end": 382.6,
      "text": "以後你重新要打開Harness的話呢,不需要重新安裝。",
      "tokens": [
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      "avg_logprob": -0.08278969795473161,
      "compression_ratio": 1.3201320132013201,
      "no_speech_prob": 2.2476309008423456e-11,
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    },
    {
      "id": 94,
      "seek": 36960,
      "start": 382.6,
      "end": 384.6,
      "text": "在終端裡頭,輸入這個命令。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.08278969795473161,
      "compression_ratio": 1.3201320132013201,
      "no_speech_prob": 2.2476309008423456e-11,
      "source": "whisper"
    },
    {
      "id": 95,
      "seek": 36960,
      "start": 384.6,
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      "text": "在Harness的頁面,咱們進入設置,模型,添加自定義提供方。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.08278969795473161,
      "compression_ratio": 1.3201320132013201,
      "no_speech_prob": 2.2476309008423456e-11,
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    },
    {
      "id": 96,
      "seek": 36960,
      "start": 389.6,
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      "text": "你可以照著我來填哈。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.08278969795473161,
      "compression_ratio": 1.3201320132013201,
      "no_speech_prob": 2.2476309008423456e-11,
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    },
    {
      "id": 97,
      "seek": 39160,
      "start": 391.6,
      "end": 393.6,
      "text": "第一個框呢,必須是小寫字母開頭。",
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      "temperature": 0,
      "avg_logprob": -0.12540497176948634,
      "compression_ratio": 1.2925373134328357,
      "no_speech_prob": 1.9909584397792557e-11,
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    },
    {
      "id": 98,
      "seek": 39160,
      "start": 393.6,
      "end": 396.6,
      "text": "顯示名稱,可以取一個你自己記得住的名字。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.9909584397792557e-11,
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    },
    {
      "id": 99,
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      "text": "API地址呢,就是剛才那個後綴是8080的那一段。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.12540497176948634,
      "compression_ratio": 1.2925373134328357,
      "no_speech_prob": 1.9909584397792557e-11,
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    },
    {
      "id": 100,
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      "start": 400.6,
      "end": 403.6,
      "text": "但是後面啊,一定要有一個斜槓V1。",
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      "temperature": 0,
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      "no_speech_prob": 1.9909584397792557e-11,
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    },
    {
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      "text": "API協議保持不變,密要,由於我們是本地模型哈。",
      "tokens": [
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      "temperature": 0,
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      "compression_ratio": 1.2925373134328357,
      "no_speech_prob": 1.9909584397792557e-11,
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    },
    {
      "id": 102,
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      "text": "所以說就隨便填了,咱就敲了三個1。",
      "tokens": [
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      "temperature": 0,
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      "compression_ratio": 1.2925373134328357,
      "no_speech_prob": 1.9909584397792557e-11,
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    },
    {
      "id": 103,
      "seek": 39160,
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      "text": "點一下獲取可用模型,就能看到咱們的千問3.8了。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.12540497176948634,
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      "no_speech_prob": 1.9909584397792557e-11,
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    },
    {
      "id": 104,
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      "text": "點下面的創建提供方,這就添加成功了。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.12540497176948634,
      "compression_ratio": 1.2925373134328357,
      "no_speech_prob": 1.9909584397792557e-11,
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    },
    {
      "id": 105,
      "seek": 41860,
      "start": 418.6,
      "end": 423.6,
      "text": "回到Harness主頁,這個時候呢,你還選不了模型,必須還有添加工作區。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.1389091444311675,
      "compression_ratio": 1.3444108761329305,
      "no_speech_prob": 1.9002806272983008e-11,
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    },
    {
      "id": 106,
      "seek": 41860,
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      "end": 425.6,
      "text": "添加好了之後啊,就可以選擇模型了。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.1389091444311675,
      "compression_ratio": 1.3444108761329305,
      "no_speech_prob": 1.9002806272983008e-11,
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    },
    {
      "id": 107,
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      "start": 425.6,
      "end": 428.6,
      "text": "趙莉,咱發一個你好,第一次可能想應慢一點。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.1389091444311675,
      "compression_ratio": 1.3444108761329305,
      "no_speech_prob": 1.9002806272983008e-11,
      "source": "whisper"
    },
    {
      "id": 108,
      "seek": 41860,
      "start": 428.6,
      "end": 433.6,
      "text": "能看到它的回复,咱今天就算是全部的大功告成了。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.1389091444311675,
      "compression_ratio": 1.3444108761329305,
      "no_speech_prob": 1.9002806272983008e-11,
      "source": "whisper"
    },
    {
      "id": 109,
      "seek": 41860,
      "start": 436.6,
      "end": 439.6,
      "text": "能回你好,不算本事啊,得看它能不能幹活。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.1389091444311675,
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      "no_speech_prob": 1.9002806272983008e-11,
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    },
    {
      "id": 110,
      "seek": 41860,
      "start": 439.6,
      "end": 441.6,
      "text": "我們先看一個最直觀的例子。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.1389091444311675,
      "compression_ratio": 1.3444108761329305,
      "no_speech_prob": 1.9002806272983008e-11,
      "source": "whisper"
    },
    {
      "id": 111,
      "seek": 41860,
      "start": 441.6,
      "end": 445.6,
      "text": "這個文件夾裡面有77個凌亂的文件,什麼格式都有。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.1389091444311675,
      "compression_ratio": 1.3444108761329305,
      "no_speech_prob": 1.9002806272983008e-11,
      "source": "whisper"
    },
    {
      "id": 112,
      "seek": 41860,
      "start": 445.6,
      "end": 446.6,
      "text": "文件名也很亂。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.1389091444311675,
      "compression_ratio": 1.3444108761329305,
      "no_speech_prob": 1.9002806272983008e-11,
      "source": "whisper"
    },
    {
      "id": 113,
      "seek": 44660,
      "start": 446.6,
      "end": 449.6,
      "text": "如果你的文件夾也這樣,按摩走一波。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.14837904855714623,
      "compression_ratio": 1.3217993079584776,
      "no_speech_prob": 1.85865645790928e-11,
      "source": "whisper"
    },
    {
      "id": 114,
      "seek": 44660,
      "start": 449.6,
      "end": 451.6,
      "text": "手動整理啊,當然很麻煩了。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.14837904855714623,
      "compression_ratio": 1.3217993079584776,
      "no_speech_prob": 1.85865645790928e-11,
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    },
    {
      "id": 115,
      "seek": 44660,
      "start": 451.6,
      "end": 453.6,
      "text": "我在Harness裡面,按摩了這個文件夾。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.14837904855714623,
      "compression_ratio": 1.3217993079584776,
      "no_speech_prob": 1.85865645790928e-11,
      "source": "whisper"
    },
    {
      "id": 116,
      "seek": 44660,
      "start": 453.6,
      "end": 455.6,
      "text": "提示詞是這麼寫的。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.14837904855714623,
      "compression_ratio": 1.3217993079584776,
      "no_speech_prob": 1.85865645790928e-11,
      "source": "whisper"
    },
    {
      "id": 117,
      "seek": 44660,
      "start": 455.6,
      "end": 460.6,
      "text": "我送出命令之後啊,能看到它開始先列舉文件,統計文件數量。",
      "tokens": [
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        7781,
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        11,
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      "temperature": 0,
      "avg_logprob": -0.14837904855714623,
      "compression_ratio": 1.3217993079584776,
      "no_speech_prob": 1.85865645790928e-11,
      "source": "whisper"
    },
    {
      "id": 118,
      "seek": 44660,
      "start": 460.6,
      "end": 465.6,
      "text": "然後挨個的查看文件內容,匯報77個文件清理好了,準備分類。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.14837904855714623,
      "compression_ratio": 1.3217993079584776,
      "no_speech_prob": 1.85865645790928e-11,
      "source": "whisper"
    },
    {
      "id": 119,
      "seek": 44660,
      "start": 465.6,
      "end": 469.6,
      "text": "等到13分鐘的時候,終於看到幾個文件夾出現了。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.14837904855714623,
      "compression_ratio": 1.3217993079584776,
      "no_speech_prob": 1.85865645790928e-11,
      "source": "whisper"
    },
    {
      "id": 120,
      "seek": 46960,
      "start": 469.6,
      "end": 474.6,
      "text": "根下呢,還有一個report和復盤。看來根下兩個文件是臨時文件。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.14735259357680622,
      "compression_ratio": 1.2775665399239544,
      "no_speech_prob": 1.746314551298589e-11,
      "source": "whisper"
    },
    {
      "id": 121,
      "seek": 46960,
      "start": 474.6,
      "end": 478.6,
      "text": "它又把它清理掉了。22分鐘左右,整理報告終於出來了。",
      "tokens": [
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      "temperature": 0,
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      "compression_ratio": 1.2775665399239544,
      "no_speech_prob": 1.746314551298589e-11,
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    },
    {
      "id": 122,
      "seek": 46960,
      "start": 478.6,
      "end": 480.6,
      "text": "你覺得這份報告寫的詳細嗎?",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.14735259357680622,
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      "no_speech_prob": 1.746314551298589e-11,
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    },
    {
      "id": 123,
      "seek": 46960,
      "start": 480.6,
      "end": 484.6,
      "text": "我們的這個任務一共花了20多分鐘。這個時間還是有點久。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.14735259357680622,
      "compression_ratio": 1.2775665399239544,
      "no_speech_prob": 1.746314551298589e-11,
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    },
    {
      "id": 124,
      "seek": 46960,
      "start": 484.6,
      "end": 486.6,
      "text": "當然呢,這個只是參考。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.14735259357680622,
      "compression_ratio": 1.2775665399239544,
      "no_speech_prob": 1.746314551298589e-11,
      "source": "whisper"
    },
    {
      "id": 125,
      "seek": 46960,
      "start": 486.6,
      "end": 488.6,
      "text": "畢竟呢,是它第一次跑任務。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.14735259357680622,
      "compression_ratio": 1.2775665399239544,
      "no_speech_prob": 1.746314551298589e-11,
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    },
    {
      "id": 126,
      "seek": 48860,
      "start": 488.6,
      "end": 490.6,
      "text": "可能啊,有些環境啊工具啊還不具備。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.10449863372639538,
      "compression_ratio": 1.348901098901099,
      "no_speech_prob": 1.839139604498108e-11,
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    },
    {
      "id": 127,
      "seek": 48860,
      "start": 490.6,
      "end": 495.6,
      "text": "而且呢,我的後臺開著很多的agent,還有陸皮軟件啊這些,都會有一定的影響。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.839139604498108e-11,
      "source": "whisper"
    },
    {
      "id": 128,
      "seek": 48860,
      "start": 495.6,
      "end": 497.6,
      "text": "都免費了,還要啥自行車呀?",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.10449863372639538,
      "compression_ratio": 1.348901098901099,
      "no_speech_prob": 1.839139604498108e-11,
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    },
    {
      "id": 129,
      "seek": 48860,
      "start": 497.6,
      "end": 500.6,
      "text": "你的硬件不同,任務不同,當然可以自己去測。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.10449863372639538,
      "compression_ratio": 1.348901098901099,
      "no_speech_prob": 1.839139604498108e-11,
      "source": "whisper"
    },
    {
      "id": 130,
      "seek": 48860,
      "start": 500.6,
      "end": 504.6,
      "text": "剛才呢,只是文件的整理。它能幹的事兒啊,遠遠不止這些。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.10449863372639538,
      "compression_ratio": 1.348901098901099,
      "no_speech_prob": 1.839139604498108e-11,
      "source": "whisper"
    },
    {
      "id": 131,
      "seek": 48860,
      "start": 504.6,
      "end": 507.6,
      "text": "通常呢,有幾個共同的特點。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.10449863372639538,
      "compression_ratio": 1.348901098901099,
      "no_speech_prob": 1.839139604498108e-11,
      "source": "whisper"
    },
    {
      "id": 132,
      "seek": 48860,
      "start": 507.6,
      "end": 512.6,
      "text": "處理大量的本地文件,規則相對明確,反覆發聲,還有一定的隱私性。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.10449863372639538,
      "compression_ratio": 1.348901098901099,
      "no_speech_prob": 1.839139604498108e-11,
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    },
    {
      "id": 133,
      "seek": 48860,
      "start": 512.6,
      "end": 515.6,
      "text": "我提煉了三大類,你可以來對號入座。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.839139604498108e-11,
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    },
    {
      "id": 134,
      "seek": 51560,
      "start": 515.6,
      "end": 520.6,
      "text": "第一類呢,是高頻任務,像什麼清理文件啊,給筆記打標圈啊,寫摘要啊這些。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.10626641441794003,
      "compression_ratio": 1.4289473684210525,
      "no_speech_prob": 1.9795585309845265e-11,
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    },
    {
      "id": 135,
      "seek": 51560,
      "start": 520.6,
      "end": 522.6,
      "text": "第二類呢,隱私,敏感型的任務。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.10626641441794003,
      "compression_ratio": 1.4289473684210525,
      "no_speech_prob": 1.9795585309845265e-11,
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    },
    {
      "id": 136,
      "seek": 51560,
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      "end": 527.6,
      "text": "像是你的個人日記啊,商務文件啊,這些你當然不想上傳到雲端了。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.10626641441794003,
      "compression_ratio": 1.4289473684210525,
      "no_speech_prob": 1.9795585309845265e-11,
      "source": "whisper"
    },
    {
      "id": 137,
      "seek": 51560,
      "start": 527.6,
      "end": 529.6,
      "text": "還有就是,常駐後台型的任務。",
      "tokens": [
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      "temperature": 0,
      "avg_logprob": -0.10626641441794003,
      "compression_ratio": 1.4289473684210525,
      "no_speech_prob": 1.9795585309845265e-11,
      "source": "whisper"
    },
    {
      "id": 138,
      "seek": 51560,
      "start": 529.6,
      "end": 534.6,
      "text": "比如說呢,你讓它監聽某一個文件家,一旦發生變化,就觸發任務。",
      "tokens": [
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      "temperature": 0,
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      "compression_ratio": 1.4289473684210525,
      "no_speech_prob": 1.9795585309845265e-11,
      "source": "whisper"
    },
    {
      "id": 139,
      "seek": 51560,
      "start": 534.6,
      "end": 538.6,
      "text": "你可以不用每次都打開對話框,重新的下命令,它自己就能搞定了。",
      "tokens": [
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      "temperature": 0,
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      "no_speech_prob": 1.9795585309845265e-11,
      "source": "whisper"
    },
    {
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      "text": "感謝你能看到這裡啊,總的來說呢,只要你的硬件跑得動,這套模式呢,它是能幹活的。",
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      "text": "但是本地大模型呢,它不是雲端Agent的平替。",
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      "text": "雲端模型呢,負責最難的判斷,本地模型呢,負責長期的執行。",
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      "text": "兩邊都有用。",
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      "text": "我們今天不僅是部署了一套模型,更是搭建了一套本地的AI系統。",
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      "text": "以後啊,換掉簽文,換掉Harness,你的本地的上下文,永遠都在。",
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      "text": "和往常一樣的,今天用到的全部的下載地址而運行命令。",
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      "text": "還有這次案例的完整提示詞,所有資料我也都打包好了。",
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      "text": "我是船長,讓我們下期再見,拜拜。",
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