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直接替换darknet/src/convolutional_laye.c文件，老版本不支持cudnn8以上的\n\n\n\n修改/src/gemm.c中的cudaThreadSynchronize为cudaDeviceSynchronize\nMakefile中添加,并删除掉低版本的信息\n\n\n最后，用make命令编译\n3、编译出错\n/bin/sh: 1: nvcc: not found \nsudo\n修改Makefile中nvcc的路径\n\n","id":"https://king-key.github.io/Blog/2023/Q1/darknetkuang-jia-gpubian-yi-an-zhuang/","title":"darknet框架GPU编译安装"},"https://king-key.github.io/Blog/2023/Q1/ffmpegjiang-mp4zhuan-wei-gif/":{"body":"\nFFmpeg是一套可以用来记录、转换数字音频、视频，并能将其转化为流的开源计算机程序。\n\n1、安装ffmpeg\n\n2、mp4等视频文件转为gif\n\n\n-i ：指定文件\n\n","id":"https://king-key.github.io/Blog/2023/Q1/ffmpegjiang-mp4zhuan-wei-gif/","title":"ffmpeg将mp4转为gif"},"https://king-key.github.io/Blog/2023/Q1/googlecolab-stablediffusion/":{"body":"1、Stable Diffusion\n{% link stable-diffusion,CompVis,https://github.com/CompVis/stable-diffusion %}\n\n\n\nStable Diffusion is a latent text-to-image diffusion model.this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.\n\ninputoutput\n \n\n但是想要运行Stable Diffusion，需要至少10GB VRAM的GPU。\n2、stable-diffusion-webui\n{% link stable-diffusion-webui,AUTOMATIC1111,https://github.com/AUTOMATIC1111/stable-diffusion-webui %}\n\n\nStable Diffusion web UI\n\n3、stable-diffusion-webui-colab\n{% link stable-diffusion-webui-colab,camenduru,https://github.com/camenduru/stable-diffusion-webui-colab/tree/drive %}\n\n\nStable Diffusion WebUI Colab With Google Drive\n\n🦒 Install the WebUI Colab to Google Drive\n.ipynb FileColab PageFunction\ninstall.ipynbOne Time Install & Update\nrun.ipynbRun\nadd.ipynbAdd Model\n\n","id":"https://king-key.github.io/Blog/2023/Q1/googlecolab-stablediffusion/","title":"4个开源Stable Diffusion项目"},"https://king-key.github.io/Blog/2023/Q1/hao-wan-de-aikai-yuan-xiang-mu/":{"body":"stable-diffusion\n\nStable Diffusion is a latent text-to-image diffusion model\n\n\n开源地址：https://github.com/CompVis/stable-diffusion\n\nStable Diffusion web UI\n\nA browser interface based on Gradio library for Stable Diffusion.\n\n\n\n模型下载：https://civitai.com/\n\n","id":"https://king-key.github.io/Blog/2023/Q1/hao-wan-de-aikai-yuan-xiang-mu/","title":"好玩的AI开源项目"},"https://king-key.github.io/Blog/2023/Q1/hexo-galleryxiang-ce-zhong-pi-liang-tian-jia-zhao-pian/":{"body":"\nhexo 可以使用gallery外挂创建相册，但手动添加太过麻烦，因此用shell脚本自动处理\n\n1、实现代码\n\n这样只需要把所有的图片放到index文件夹下，执行文件即可实现在index.md中添加所有的图片链接\n","id":"https://king-key.github.io/Blog/2023/Q1/hexo-galleryxiang-ce-zhong-pi-liang-tian-jia-zhao-pian/","title":"hexo gallery相册中批量添加照片"},"https://king-key.github.io/Blog/2023/Q1/hexoxiu-gai-yin-yong-lian-jie-de-yang-shi/":{"body":"\n修改超链接的引用样式，先看一下效果\n\n{% link Hexo的Butterfly魔改：网址卡片外挂标签,张洪Heo,https://blog.zhheo.com/p/ccaf9148.html %}\n1、新建themes/butterfly/scripts/tag/link.js文件\n\n2、在 themes/butterfly/_config.yml的inject 中引入\n\n3、使用方法\nmarkdown文件中使用\n\n最后，重新``` hexo clean / hexo g / hexo s````就能看到效果了。\n","id":"https://king-key.github.io/Blog/2023/Q1/hexoxiu-gai-yin-yong-lian-jie-de-yang-shi/","title":"hexo修改引用链接的样式"},"https://king-key.github.io/Blog/2023/Q1/itchathuo-qu-qun-liao-yong-hu-de-xin-xi/":{"body":"\n不是所有的微信账号都可以登陆网页版微信的哦\n\n1、安装itchat\npip install itchat\npython扩展包的安装太简单了，就只需要一行命令就可以解决\n2、利用itchat获取群聊好友的信息\n直接上代码，代码里面会有相关的注释的\n\n3、中间出现的问题\n\n有的微信号无法登录\n\n\n好像说是新申请的无法登录，可以试试能否登录网页版微信，能登录网页版微信就可以登录itchat，反之一样\n\n\n群聊显示不全\n\n\n这里的群聊默认是只显示已经保存在通讯录里面的群聊\n\n","id":"https://king-key.github.io/Blog/2023/Q1/itchathuo-qu-qun-liao-yong-hu-de-xin-xi/","title":"itchat获取群聊用户的信息"},"https://king-key.github.io/Blog/2023/Q1/itchathuo-qu-wei-xin-hao-you-tou-xiang-bing-jin-xing-ju-xing-pin-jie/":{"body":"\n不是所有的微信账号都可以登陆网页版微信的哦\n\n\n\n\n\n最后的结果截图，小编就不上传了，但是代码绝对是可行的！！！\n\n\n\n","id":"https://king-key.github.io/Blog/2023/Q1/itchathuo-qu-wei-xin-hao-you-tou-xiang-bing-jin-xing-ju-xing-pin-jie/","title":"itchat获取微信好友头像并进行矩形拼接"},"https://king-key.github.io/Blog/2023/Q1/itchathuo-qu-wei-xin-hao-you-zong-ren-shu-yi-ji-nan-nu-bi-li/":{"body":"\n不是所有的微信账号都可以登陆网页版微信的哦\n\n1、itchat\n\nitchat是微信的个人开放接口；本文利用Python+itchat实现简单的个人好友数量以及男女比例的统计\n\n\n安装： pip install itchat(需要权限的可以使用sudo)\n\n2、代码\n\n3、结果截图\n\n女性朋友稍多一点也就不说什么了，可是这性别不明是什么个意思？小编也是很无奈呀\n","id":"https://king-key.github.io/Blog/2023/Q1/itchathuo-qu-wei-xin-hao-you-zong-ren-shu-yi-ji-nan-nu-bi-li/","title":"itchat获取微信好友总人数以及男女比例"},"https://king-key.github.io/Blog/2023/Q1/linuxzhong-yi-xie-hao-yong-de-ming-ling/":{"body":"1、创建软连接ln\n\n2、同步文件夹rsync\n\n\na 表示以递归模式进行传输\nv 表示显示详细信息\nz 表示在传输时进行压缩\n\n3、快捷命令alias\n\n需要在.bashrc或者.zshrc中添加使用\n\n\n比如\n\n这样就能直接一次性执行三条命令进行部署\n","id":"https://king-key.github.io/Blog/2023/Q1/linuxzhong-yi-xie-hao-yong-de-ming-ling/","title":"linux中一些好用的命令"},"https://king-key.github.io/Blog/2023/Q1/qtan-zhuang-shi-yong/":{"body":"\nQt 是为嵌入式和桌面应用开发而生的跨平台软件开发平台\n\n1、安装最基本要求\n\n2、下载Qt，并安装\n\n\n安装需要注册账号，同时里面的组建可根据需求进行安装。安装完成后显示\n\n3、选择一个代码实例进行测试，比如第一个\n\n","id":"https://king-key.github.io/Blog/2023/Q1/qtan-zhuang-shi-yong/","title":"Qt安装使用"},"https://king-key.github.io/Blog/2023/Q1/rosan-zhuang/":{"body":"\n机器人学习，ROS安装\n\n\n1、ROS安装\n\n2、初始化ROS\n\n\n问题1： sudo: rosdep：找不到命令\n\n安装\n\n或者\n\n\n问题2：\n\n\n解决方案1：\n\n解决方案2：直接下载本地文件，并更改相应文件。\n\n文件链接：链接: https://pan.baidu.com/s/1buhh86hURn-0K6llS7Ypfw  提取码: 2o8b\n\n下载解压并移动到 /etc/ros/ 目录\n新建文件/修改文件 sudo gedit /etc/ros/rosdep/sources.list.d/20-default.list\n\n替换为\n\n至此，初始化成功\n3、添加环境变量/问题 zsh: command not found: roscore\nbash:\n\nzsh:\n\n4、测试小乌龟\n\n鼠标放在shell 3里面，按下上下左右键，可控制小乌龟，shell 2显示坐标。\n","id":"https://king-key.github.io/Blog/2023/Q1/rosan-zhuang/","title":"ROS安装"},"https://king-key.github.io/Blog/2023/Q1/streamlit-appchuang-jian/":{"body":"\n用写Python的代码完成一个Web App\n\n1、Streamlit\n项目地址：https://github.com/streamlit/streamlit\n\n2、安装与使用\n\n安装\n\n\n\n构建可执行代码\n\n在原有Python文件的基础上，import Streamlit，并设置数据显示模块即可。如有文件 hello.py ：\n\n\n执行命令streamlit run 文件.py。如：\n\n\n\n执行命令后，点击跳转网页的链接，就可以看到效果。\n\n3、Streamlit官网\nStreamlit可显示的数据也有很多，图表数据，视频等等都可以进行操作，同时也可以和其他Python模块结合使用。具体可参考官网介绍。\n","id":"https://king-key.github.io/Blog/2023/Q1/streamlit-appchuang-jian/","title":"Streamlit App创建"},"https://king-key.github.io/Blog/2023/Q1/vscodezhong-chuang-jian-markdownmo-ban/":{"body":"\n利用代码片段快速生成特定信息\n\n1、vscode中新建“用户代码片段”\n\n打开“code“ - “首选项” - “配置用户代码片段”\n\n\n\n“新建全局代码片段文件”，文件名可随意\n\n\n2、创建自定义信息\n\n文件默认信息是这样的\n\n\n\n参数说明\n\n\n\n修改后的文件是这样的\n\n\n\n可以插入的变量\n\n使用规则：$name或${name:default}\n\n3、结果展示\n\n","id":"https://king-key.github.io/Blog/2023/Q1/vscodezhong-chuang-jian-markdownmo-ban/","title":"VScode中创建markdown模板"},"https://king-key.github.io/Blog/2023/Q2/":{"body":"","id":"https://king-key.github.io/Blog/2023/Q2/","title":"index"},"https://king-key.github.io/Blog/2023/Q2/butterlfyzhong-ji-ge-hao-yong-de-wai-gua-biao-qian/":{"body":"\n记录保存，方便自己用\n\n1、tab\n{% tabs 代码 %}\nsss\n\n\n\n\n\nthis is tab1\n\n\nthis is tab2\n\n\n{% endtabs %}\n2、timeline\n{% tabs 代码 %}\n\n\n\n\n{% timeline title,color %}\n\nxxxxx\n\n\nxxxxx\n\n{% endtimeline %}\n\n{% endtabs %}\n3、details\n\nmarkdown中都支持，不局限于butterfly主题\n\n{% tabs 代码 %}\n\n\n\n\n\n    展开!\n内容展开\n\n\n{% endtabs %}\n","id":"https://king-key.github.io/Blog/2023/Q2/butterlfyzhong-ji-ge-hao-yong-de-wai-gua-biao-qian/","title":"hexo butterlfy主题中几个好用的外挂标签"},"https://king-key.github.io/Blog/2023/Q2/chatglm2-6b-int4ben-di-bu-shu/":{"body":"\nChatGLM2-6B 是开源中英双语对话模型 ChatGLM-6B 的第二代版本\nGitHub地址：https://github.com/THUDM/ChatGLM2-6B\n\n1、先看效果\n\n2、ChatGLM2-6B新特性\n\n\n更强大的性能：基于 ChatGLM 初代模型的开发经验，我们全面升级了 ChatGLM2-6B 的基座模型。ChatGLM2-6B 使用了 GLM 的混合目标函数，经过了 1.4T 中英标识符的预训练与人类偏好对齐训练，评测结果显示，相比于初代模型，ChatGLM2-6B 在 MMLU（+23%）、CEval（+33%）、GSM8K（+571%） 、BBH（+60%）等数据集上的性能取得了大幅度的提升，在同尺寸开源模型中具有较强的竞争力。\n\n\n更长的上下文：基于 FlashAttention 技术，我们将基座模型的上下文长度（Context Length）由 ChatGLM-6B 的 2K 扩展到了 32K，并在对话阶段使用 8K 的上下文长度训练，允许更多轮次的对话。但当前版本的 ChatGLM2-6B 对单轮超长文档的理解能力有限，我们会在后续迭代升级中着重进行优化。\n\n\n更高效的推理：基于 Multi-Query Attention 技术，ChatGLM2-6B 有更高效的推理速度和更低的显存占用：在官方的模型实现下，推理速度相比初代提升了 42%，INT4 量化下，6G 显存支持的对话长度由 1K 提升到了 8K。\n\n\n更开放的协议：ChatGLM2-6B 权重对学术研究完全开放，在获得官方的书面许可后，亦允许商业使用。如果您发现我们的开源模型对您的业务有用，我们欢迎您对下一代模型 ChatGLM3 研发的捐赠。\n\n\n3、本地部署(ChatGLM2-6B-int4)\n部署环境\n\n下载\n\n创建虚拟环境，安装库\n\n本地模型下载\n\n然后在清华大学云盘下载相应的模型参数文件,并将文件拷贝到chatglm2-6b-int4文件夹下\n\nint-4推理\n需要先修改web_demo.py,修改内容\n\nweb_demo.py\n执行\n\n\n\n遇到的问题\n\n问题1\n\n\n-- 解决，输入\n\n\n并点击链接生成new token,拷贝到shell中输入即可\n\n\n\n问题2\n\n\n-- 解决，输入\n\n","id":"https://king-key.github.io/Blog/2023/Q2/chatglm2-6b-int4ben-di-bu-shu/","title":"🖋 ChatGLM2-6B-Int4本地部署"},"https://king-key.github.io/Blog/2023/Q2/googlecolab-aidemo/":{"body":"\n随着大模型时代的到来，算力的需求越来越大！\n\nAI Demo in Google Colab\nFileDemogoogle colabcode\nStable_Diffusion_Webui_colab.ipynbStable Diffusion Webui\nstable_diffusion_colab.ipynbStable Diffusion\nchatglm-colab.ipynbChatGLM-6B-int4推理\nChatGLM2-6B.ipynbChatGLM2-6B-int4推理\nwechat_demo.ipynbitchat + ChatGLM2-6B-int4推理，实现微信对话\n\n","id":"https://king-key.github.io/Blog/2023/Q2/googlecolab-aidemo/","title":"白嫖Tesla T4 GPU玩转 AI Demo"},"https://king-key.github.io/Blog/2023/Q2/hexo-butterflyzhong-tian-jia-github-calendargong-xian-tu/":{"body":"\n个性化博客\n\n1、先来看一下效果\nWang Guo's Blog\n\n2、安装\n\n建议直接安装插件，省事也方便\n\n\n\n在博客项目根目录的_config.yml文件中添加\n\n\n\nhexo clean && hexo g && hexo s  三连即可\n\n3、最重要的\n\nuser:填写个人github账号名称即可\n关键是api的问题\n\n首先，公用api不可用，只能自建；\n其次，自建api的具体教程可以参考https://github.com/Zfour/hexo-github-calendar。但是个人在实际部署中发现，无法正常得到数据，经过排查后是发现这个项目里面的原代码有点小问题\n原项目https://github.com/Zfour/hexo-github-calendar本人修改后的https://github.com/King-Key/github_calendar_api\n\n\n最主要的区别就是13行的代码，在github首页的源代码中，我们就可以找到这个值是data-level，data-level是要统计的提交次数。\n\n原项目的得到的值是错误的，在实际部署后没法显示。所以大家可以在自建api的时候记得修改这个内容，或者也可以直接fork我的项目自建api进行部署。\nhttps://github.com/King-Key/github_calendar_api\n","id":"https://king-key.github.io/Blog/2023/Q2/hexo-butterflyzhong-tian-jia-github-calendargong-xian-tu/","title":"hexo butterfly主题中添加github calendar贡献图"},"https://king-key.github.io/Blog/2023/Q2/promptzhi-ling/":{"body":"\n怎么可以用好ChatGPT，prompt是关键！\n\n1、prompt的基本原则\n\n开头提供明确说明，有助于为模型设置上下文\n指定预期答案的格式类型\n可以加入系统消息或角色扮演技术增强交互\n\n2、基础prompt工程策略\n\n正确的prompt关键词\n简洁的prompt\n角色分配和目标设置，也就是需要明确一个具体场景\n正负提示，即告诉模型应该这样做、不这样做\n\n3、高级策略\n\n输入/输出prompt：给模型说明输入什么，输入什么\nZero-shot prompt：零样本，模糊的提问\nOne-shot prompt：给模型一个样例\nFew-shot prompt：给模型多个样例\n思维链 Prompt：给模型一个事件的推理的过程，引导模型按此进行推理\nSelf-Criticism prompt策略：引导模型对其输出进行评估并进行改进\n迭代prompt策略：逐步引导\n与模型合作\n\nprompt的prompt：让模型生成prompt的建议\n模型引导prompt：给定模型结果，让模型反推输入过程\n\n\n\n4、prompt案例\n\n图片生成\n\n\n\n对话思考\n\n\n","id":"https://king-key.github.io/Blog/2023/Q2/promptzhi-ling/","title":"chatgpt prompt学习"},"https://king-key.github.io/Blog/2023/Q2/pythonzhong-yi-xie-hao-yong-de-kuo-zhan-bao/":{"body":"\n正所谓“工欲善其事，必先利其器”。推荐几个常用的、且非常方便的Python扩展库\n\n1、pqi\n\npqi解决pip安装速度较慢的问题\n\npip安装默认的软件源是国外的，下载起来速度很慢，而将软件源换成国内的镜像源之后，可以提高下载速度。手动编辑源文件比较麻烦，pqi则可以帮助我们直接切换软件源。\n\n\n安装pip install pqi\n\n\n使用方法\n\n\n\n查看当前的pip软件源pqi show\n\npqi中内置了5个软件源pqi ls\n\n切换软件源pqi use [url name]，比如 pqi use douban，这样再查看软件源粉时候就发现已经切换了。\n2、pipreqs\n\npipreqs可以生成Python依赖包清单，也就是导出requirements.txt文件\n\n与pip freeze > requirements.txt的功能类似，但pipreqs只导出指定项目所依赖的组件及其版本👌，相比于前者导出当前python环境下所有安装的组件，pipsegs的导出会更轻量化。\n\n安装pip install pipreqs\n使用方法\n\npipreqs 项目路径，比如pipreqs ./。\n\n对比一下freeze和pipreqs\n\npip listpip freeze > requirements.txtpipreqs ./\naiofiles              23.1.0altair                4.2.2anyio                 3.6.2attrs                 22.2.0blinker               1.5cachetools            5.3.0certifi               2022.12.7charset-normalizer    3.1.0ci-info               0.3.0click                 8.1.3configobj             5.0.8configparser          5.3.0decorator             5.1.1entrypoints           0.4etelemetry            0.3.0filelock              3.10.7fitz                  0.0.1.dev2frontend              0.0.3future                0.18.3gitdb                 4.0.10GitPython             3.1.31h11                   0.14.0httplib2              0.22.0idna                  3.4importlib-metadata    6.1.0isodate               0.6.1itsdangerous          2.1.2Jinja2                3.1.2jsonschema            4.17.3looseversion          1.1.2lxml                  4.9.2markdown-it-py        2.2.0MarkupSafe            2.1.2mdurl                 0.1.2networkx              3.0nibabel               5.0.1nipype                1.8.5numpy                 1.24.2packaging             23.0pandas                1.5.3pathlib               1.0.1Pillow                9.4.0pip                   22.0.2protobuf              3.20.3prov                  2.0.0pyarrow               11.0.0pydeck                0.8.0pydot                 1.4.2Pygments              2.14.0Pympler               1.0.1PyMuPDF               1.21.1pyparsing             3.0.9pyrsistent            0.19.3python-dateutil       2.8.2pytils                0.4.1pytz                  2022.7.1pytz-deprecation-shim 0.1.0.post0pyxnat                1.5rdflib                6.3.1requests              2.28.2rich                  13.3.2scipy                 1.10.1semver                2.13.0setuptools            59.6.0simplejson            3.18.4six                   1.16.0smmap                 5.0.0sniffio               1.3.0starlette             0.26.1stqdm                 0.0.5streamlit             1.20.0toml                  0.10.2tools                 0.1.9toolz                 0.12.0tornado               6.2tqdm                  4.65.0traits                6.3.2typing_extensions     4.5.0tzdata                2022.7tzlocal               4.3urllib3               1.26.15uvicorn               0.21.1validators            0.20.0watchdog              3.0.0wheel                 0.37.1zipp                  3.15.0aiofiles==23.1.0altair==4.2.2anyio==3.6.2attrs==22.2.0blinker==1.5cachetools==5.3.0certifi==2022.12.7charset-normalizer==3.1.0ci-info==0.3.0click==8.1.3configobj==5.0.8configparser==5.3.0decorator==5.1.1entrypoints==0.4etelemetry==0.3.0filelock==3.10.7fitz==0.0.1.dev2frontend==0.0.3future==0.18.3gitdb==4.0.10GitPython==3.1.31h11==0.14.0httplib2==0.22.0idna==3.4importlib-metadata==6.1.0isodate==0.6.1itsdangerous==2.1.2Jinja2==3.1.2jsonschema==4.17.3looseversion==1.1.2lxml==4.9.2markdown-it-py==2.2.0MarkupSafe==2.1.2mdurl==0.1.2networkx==3.0nibabel==5.0.1nipype==1.8.5numpy==1.24.2packaging==23.0pandas==1.5.3pathlib==1.0.1Pillow==9.4.0protobuf==3.20.3prov==2.0.0pyarrow==11.0.0pydeck==0.8.0pydot==1.4.2Pygments==2.14.0Pympler==1.0.1PyMuPDF==1.21.1pyparsing==3.0.9pyrsistent==0.19.3python-dateutil==2.8.2pytils==0.4.1pytz==2022.7.1pytz-deprecation-shim==0.1.0.post0pyxnat==1.5rdflib==6.3.1requests==2.28.2rich==13.3.2scipy==1.10.1semver==2.13.0simplejson==3.18.4six==1.16.0smmap==5.0.0sniffio==1.3.0starlette==0.26.1stqdm==0.0.5streamlit==1.20.0toml==0.10.2tools==0.1.9toolz==0.12.0tornado==6.2tqdm==4.65.0traits==6.3.2typing_extensions==4.5.0tzdata==2022.7tzlocal==4.3urllib3==1.26.15uvicorn==0.21.1validators==0.20.0watchdog==3.0.0zipp==3.15.0PyMuPDF==1.21.1streamlit==1.20.0\n\n3、Streamlit\n\nStreamlit主要用来创建App，并且可以方便的和Python结合以及部署。相关的安装使用可以参考之前的文章\n\n{% link Streamlit App创建,Guo Wang,https://King-Key.github.io/posts/34479.html%}\n{% link Streamlit App多页面运行,Guo Wang,https://King-Key.github.io/posts/22558.html%}\n{% link streamlit cloud部署一个streamlit App,Guo Wang,https://King-Key.github.io/posts/cda857c1.html%}\n4、tqdm\n\ntqdm是一个进度条模块\n\n\n安装pip install tqdm\n使用方法，在Python文件中直接引入使用即可\n\n\n官方demo演示\n","id":"https://king-key.github.io/Blog/2023/Q2/pythonzhong-yi-xie-hao-yong-de-kuo-zhan-bao/","title":"Python中一些好用的扩展包"},"https://king-key.github.io/Blog/2023/Q2/ru-he-she-ji-prompt/":{"body":"\nprompt资源\n\n1、Lexica\n\n2、promptoMANIA: AI art community with prompt generator\n\n3、Arthub.ai: Discover, Upload and Share AI Generated Art\n\n4、KREA — create better prompts.\n\n5、Discover and Generate AI Art | OpenArt\n\n","id":"https://king-key.github.io/Blog/2023/Q2/ru-he-she-ji-prompt/","title":"如何设计prompt"},"https://king-key.github.io/Blog/2023/Q2/stable-diffusion-webui-googlecolab/":{"body":"\n想要玩stable diffusion，算力不可少，白嫖google colab Tesla T4 GPU 玩转Stable Diffusion Webui\n\n1、google colab上安装stable diffusion webui\n\n\n\n在google colab中新建Stable Diffusion Webui googlecolab.ipynb文件\n\n\nclone stable diffusion webui项目\n\n\n\n\n创建虚拟环境，并进入虚拟环境\n\n\n\n安装需要的环境\n\n\n\n运行，不要忘记在代码执行程程序 中 更改运行时类型为gpu\n\n\n\n\n最后会生成一个public link，点击链接打开即可\n\n2、关于模型及下载\n\nCivitai提供了很多模型，可以看看\n\n\n- 可以在 Hugging Face  搜索模型的下载地址（主要是这个提供下载地址，可以更方便的在google colab中执行）\n\n-也可以挂载Google 云盘，通过Google云盘上传模型\n\n3、关于google colab中命令的使用\n%: 目录操作\n!: 执行操作\n","id":"https://king-key.github.io/Blog/2023/Q2/stable-diffusion-webui-googlecolab/","title":"白嫖Tesla T4 GPU玩转Stable Diffusion Webui"},"https://king-key.github.io/Blog/2023/Q2/streamlit-cloudbu-shu-yi-ge-streamlit-app/":{"body":"\n白嫖一个Cloud部署自己的算法程序\n\n1、github上创建部署的项目\n项目包括streamlit执行文件和requirements.txt文件。requirements.txt文件的创建可以参考Python中一些好用的扩展包中pipreqs的用法。\ngithub上的项目\n{% link pdf2png,King-Key,https://github.com/King-Key/pdf2png %} \n2、Streamlit Cloud上部署项目\nStreamlit Cloud 地址：https://share.streamlit.io/\n\n点击new app-from existing repo\n\n\n\n从github中选择要部署的项目\n\n\n\n最后进行部署即可\n\n部署成功后，会提供一个地址，点击进入就可以使用了\n\n3、部署的pdf2png项目\n\n点击图片可以跳转使用！\n","id":"https://king-key.github.io/Blog/2023/Q2/streamlit-cloudbu-shu-yi-ge-streamlit-app/","title":"Streamlit Cloud部署一个Streamlit App"},"https://king-key.github.io/Blog/2023/Q2/zheng-li-yi-xie-kai-yuan-de-llms/":{"body":"\n自ChatGPT爆火🔥之后，类ChatGPT不断涌现。整理一些关于LLMs/MLLMs的开源项目\n\n\n\n\n通用大模型\nmodelModel linkstars作者\nMOSShttps://github.com/OpenLMLab/MOSS复旦NLP实验室在读博士 孙天祥\nbaizehttps://github.com/project-baize/baize-chatbot加州大学圣地亚哥分校在读博士 Canwen Xu\nGLM-130Bhttps://github.com/THUDM/GLM-130B清华大学在读博士 Aohan Zeng\nChatGLMhttps://github.com/THUDM/ChatGLM-6B https://github.com/THUDM/ChatGLM2-6Bhttps://github.com/THUDM/ChatGLM3智谱华章\nLaMini-LMhttps://github.com/mbzuai-nlp/LaMini-LM莫纳什大学在读博士 吴明昊\nWizardLMhttps://github.com/nlpxucan/WizardLM微软STCA（亚洲软件技术中心）NLP集团高级研究科学家 Can Xu\nPandaLLMhttps://github.com/dandelionsllm/pandallm南洋理工大学在读博士 焦方锴\nPandaLMhttps://github.com/WeOpenML/PandaLM北大在读博士 王一栋\nMLC-LLMhttps://github.com/mlc-ai/mlc-llmCMU助理教授 陈天奇团队\nColossalChathttps://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat潞晨科技\nDromedaryhttps://github.com/IBM/DromedaryCMU在读博士 Zhiqing Sun\nChatPLUGhttps://github.com/X-PLUG/ChatPLUG达摩院\nmPLUG-Owlhttps://github.com/X-PLUG/mPLUG-Owl达摩院\nalpacahttps://github.com/tatsu-lab/stanford_alpaca斯坦福大学在读博士 Xuechen Li\nFastChathttps://github.com/lm-sys/FastChat加州大学伯克利分校在读博士 Lianmin Zheng\nLMFlowhttps://github.com/OptimalScale/LMFlow香港科技大学在读博士 刁诗哲\nPromptCLUEhttps://github.com/clue-ai/PromptCLUE元语智能\nBaichuanhttps://github.com/baichuan-inc/baichuan-7B百川智能\nInternLMhttps://github.com/InternLM/InternLM上海人工智能实验室\nbubogpthttps://github.com/magic-research/bubogpt抖音\nQwenhttps://github.com/QwenLM/Qwen阿里云\n\n\n专用大模型\nmodel领域Model linkStars作者\n聚宝盆\nChatLaw系列法律https://github.com/PKU-YuanGroup/ChatLaw北京大学信息工程学院\n\n\n\n多模态大模型\nmodelModel link作者\nInstructBLIPhttps://github.com/salesforce/LAVIS/tree/main/projects/instructblip香港科技大学在读博士 戴文亮\nLLaVAhttps://github.com/haotian-liu/LLaVA威斯康星大学麦迪逊分校 Haotian Liu\nMiniGPT-4https://github.com/Vision-CAIR/MiniGPT-4阿卜杜拉国王科技大学在读博士 朱德尧\nGPT4Toolshttps://github.com/StevenGrove/GPT4Tools腾讯 AILab 研究员 宋林\nVPGTranshttps://github.com/VPGTrans/VPGTrans新加坡国立大学在读博士 张傲\n\n","id":"https://king-key.github.io/Blog/2023/Q2/zheng-li-yi-xie-kai-yuan-de-llms/","title":"🔥 开源 LLMs/MLLMs"},"https://king-key.github.io/Blog/2023/Q2/zi-ding-yi-shou-ye-qia-pian-gao-du/":{"body":"修改代码\n打开themes/butterfly/source/css/_page/homepage.styl文件\n修改\n\n看看效果\n18em效果15em效果\n\n\n","id":"https://king-key.github.io/Blog/2023/Q2/zi-ding-yi-shou-ye-qia-pian-gao-du/","title":"🦋 hexo butterfly自定义首页卡片高度"},"https://king-key.github.io/Blog/2023/Q3/":{"body":"","id":"https://king-key.github.io/Blog/2023/Q3/","title":"index"},"https://king-key.github.io/Blog/2023/Q3/0821-langchainxue-xi-2/":{"body":"\nlangchain+chatglm2-6b\n\n1、langchain中的主要模块\n\n模型（models）\n提示（prompts)\n内存（memory）\n索引（indexes）\n链（chains）\n代理（agents)\n\n2、langchain+chatglm2-6b\n\nChatglm2-6b api（官方代码），先 python api.py进行运行\n\n\n\nlangchain中使用chatglm\n\n\n\n最后运行即可\n\n","id":"https://king-key.github.io/Blog/2023/Q3/0821-langchainxue-xi-2/","title":"🖋 langchain学习2"},"https://king-key.github.io/Blog/2023/Q3/0824-langchain-agents/":{"body":"\nAgents\n\n1、Agents(代理)\n\nAgents使用LLM来确认下一步用什么工具，执行什么操作。\n\n\n\n工具：执行具体功能的函数：比如搜索，数据库查找等；\n\n\nLLM：为Agents提供动力的语言模型；\n\n\nAgents：\n\n\n2、使用\n\n加载LLM\n\n\n\n加载工具\n\n\n\n使用工具\n\n\n\n执行\n\n\n3、Agents+ChatGLM2-6B\n\n4、AgentType（代理类型）\n\nzero-shot-react-description：基于工具的描述来确认使用的工具\nreact-docstore：存储文档的交互\nreact-docstore：查找问题的事实性答案\nconversational-react-description：对话中的Propmpt设计\n\n5、Tools（可调用的工具）\n\n\n官网工具\n\nhttps://www.langchain.asia/modules/agents/tools/getting_started\n\n\n\n自定义\n\n\n","id":"https://king-key.github.io/Blog/2023/Q3/0824-langchain-agents/","title":"🖋 langchain Agents"},"https://king-key.github.io/Blog/2023/Q3/fastapichu-shi-yong/":{"body":"\nfastapi 是一个基于 Python 的 API 构建框架，简单且易用！\n\n1、安装\n安装，主要分为两部分：\n1）fastapi： pip install fastapi\n2）uvicorn: pip install uvicorn。服务器端，可以用来运行fastapi的代码。\n也可以一起安装pip install fastapi[all]\n2、api实现\n\napp.get(\"/1)\napi路径引入,也就是用 1 调用 read_root函数，最终返回一个 hello。\n\napp.get(\"/{a})\n\n也可以输入值,当输入一个int型参数，调用 read_root函数，返回一个 hello。\n\n@app.get(\"/{a}+{b}\")\n\n实现一个a+b的API。\n\n函数和api分离\n不可能把所有的api函数都放在一个.py文件中，在python文件中可以通过import调用函数。\n\n\nsum.py,定义了一个add函数，实现a+b\n\n\n\nmain.py,调用add函数\n\n\n\n\n3、运行\n\nok，结束！\n","id":"https://king-key.github.io/Blog/2023/Q3/fastapichu-shi-yong/","title":"🖋 fastapi初使用"},"https://king-key.github.io/Blog/2023/Q3/ffmpegjiang-gifzhuan-wei-mp4/":{"body":"\nFFmpeg是一套可以用来记录、转换数字音频、视频，并能将其转化为流的开源计算机程序。\n\n1、安装ffmpeg\n\n2、gif转为mp4等视频文件\n\n","id":"https://king-key.github.io/Blog/2023/Q3/ffmpegjiang-gifzhuan-wei-mp4/","title":"🖋 ffmpeg将gif转为mp4"},"https://king-key.github.io/Blog/2023/Q3/hexo-butterflytian-jia-hua-ban-piao-luo-bei-jing/":{"body":"\n博客美化继续\n\n1、先来看看效果\n\n2、实现方法\n在themes/butterfly/_config.yml文件中找到buttom添加以下内容:\n\n","id":"https://king-key.github.io/Blog/2023/Q3/hexo-butterflytian-jia-hua-ban-piao-luo-bei-jing/","title":"🦋 hexo butterfly添加花瓣飘落背景"},"https://king-key.github.io/Blog/2023/Q3/hexo-butterflyzhong-tian-jia-gong-gao-lan/":{"body":"\n博客美化\n\n参考\n{% link Hexo中Buttefly主题公告栏设置与适配（十二）,偷掉月亮的阿硕,https://moonshuo.cn/posts/46856.html %}\n{% link 首页置顶公告栏轮播,花猪,http://cnhuazhu.top/butterfly/2022/10/12/Hexo魔改/首页置顶公告栏轮播/ %}\n修改\n主要是themes/butterfly/layout/includes/motice.pug文件的修改\n\n","id":"https://king-key.github.io/Blog/2023/Q3/hexo-butterflyzhong-tian-jia-gong-gao-lan/","title":"🦋 hexo butterfly中添加公告栏"},"https://king-key.github.io/Blog/2023/Q3/langchainchu-xue-xi/":{"body":"\nLangChain is a framework for developing applications powered by language models(LangChain是一个由语言模型驱动的应用程序开发框架，旨在帮助开发人员使用语言模型构建端到端的应用程序)\n\n1、一些概念\nComponents and Chains\n\nComponent 是模块化的构建块，可以组合起来创建强大的应用程序。\nChain 是组合在一起以完成特定任务的一系列 Components。一个 Chain 可能包括一个 Prompt 模板、一个语言模型和一个输出解析器，它们一起工作以处理用户输入、生成响应并处理输出。\n\nPrompt Templates and Values\n\nPrompt Templates 负责创建 PromptValue，这是最终传递给语言模型的内容。有助于将用户输入和其他动态信息转换为适合语言模型的格式。\nPromptValues 是具有方法的类，这些方法可以转换为每个模型类型期望的确切输入类型（如文本或聊天消息）。\n\nExample Selectors\n\n可以在 Prompt 中动态包含示例，接受用户输入并返回一个示例列表以在提示中使用，使其更强大和特定于上下文。\n\nOutput Parsers\n\n负责将语言模型响应构建为更有用的格式，使得在应用程序中处理输出数据变得更加容易。实现了两种主要方法：\n\n一种用于提供格式化指令。\n一种用于将语言模型的响应解析为结构化格式。\n\n\n\nIndexes and Retrievers\n\nIndex 是一种组织文档的方式，使语言模型更容易与它们交互。\nRetrievers 是用于获取相关文档并将它们与语言模型组合的接口。\n\n\nLangChain 提供了用于处理不同类型的索引和检索器的工具和功能，例如矢量数据库和文本拆分器。\n\nChat Message History\n\n主要通过聊天界面与语言模型进行交互。\n\n\nChatMessageHistory 类负责记住所有以前的聊天交互数据，然后可以将这些交互数据传递回模型、汇总或以其他方式组合。这有助于维护上下文并提高模型对对话的理解。\n\nAgents and Toolkits\n\nAgent 是在 LangChain 中推动决策制定的实体。他们可以访问一套工具，并可以根据用户输入决定调用哪个工具。\nTookits 是一组工具，当它们一起使用时，可以完成特定的任务。代理执行器负责使用适当的工具运行代理。\n\nLangChain model\nLangChain 中的模型主要分为三类\n\nLLM（大型语言模型）：可以将文本字符串作为输入并返回文本字符串作为输出。\n聊天模型( Chat Model) ：将聊天消息列表作为输入并返回聊天消息。用于管理对话历史记录和维护上下文。\n文本嵌入模型(Text Embedding Models) ：将文本作为输入并返回表示文本嵌入的浮点列表。用于文档检索、聚类和相似性比较等任务。\n\n2、安装\n pip install langchain or pip install langchain[all] or pip install langchain[llm]\nzsh 安装需要使用 pip install 'langchain[all]' \n","id":"https://king-key.github.io/Blog/2023/Q3/langchainchu-xue-xi/","title":"🖋 langchain初学习"},"https://king-key.github.io/Blog/2023/Q3/macwei-xin-shuang-kai/":{"body":"\n通过Mac上的“自动操作”和一行代码实现图像化的Mac双开\n\n1、先看看效果\n\n2、Mac上，一行命令解决微信双开的问题\n\n但是每次通过命令行去操作也太过麻烦，最好的方式还是加一个图标，和正常的应用程序一样，点击图标执行最好。\n3、利用“自动操作”，创建双开微信App\n\n打开“自动操作”\n\n\n\n“应用程序”-新建文稿\n\n\n\n“实用工具”-“运行shell脚本”-创建文件\n\n\n\n复制双开命令，保存为“应用程序”\n\n\n\n右键“显示简介”，点击图标进行替换即可\n\n\n4、图标\n也可以找其他图标进行替换\n\n","id":"https://king-key.github.io/Blog/2023/Q3/macwei-xin-shuang-kai/","title":"Mac微信双开"},"https://king-key.github.io/Blog/2023/Q4/":{"body":"","id":"https://king-key.github.io/Blog/2023/Q4/","title":"index"},"https://king-key.github.io/Blog/2023/Q4/1228-aigc/":{"body":"\n玩一玩sd webui\n\n先看效果\n\n\n\n提示词\n\n\n\n反向提示词\n\n\n","id":"https://king-key.github.io/Blog/2023/Q4/1228-aigc/","title":"sd webui美女生成01"},"https://king-key.github.io/Blog/2023/Q4/ben-di-hua-bu-shu-quivr/":{"body":"\n矢量数据库的安装\n\nsupabase安装 1\n\n\n问题1\n\n\n解决\n\n\n问题2\n\n\n解决\n\n参考链接：\n1\nhttps://supabase.com/docs/guides/self-hosting/docker#running-supabase\n\n","id":"https://king-key.github.io/Blog/2023/Q4/ben-di-hua-bu-shu-quivr/","title":"本地化部署Supabase"},"https://king-key.github.io/Blog/2023/Q4/gitde-xie-zuo-shi-yong/":{"body":"\n小型团队协作场景下，需要使用的git命令\n\n\n2、git的使用\n\n","id":"https://king-key.github.io/Blog/2023/Q4/gitde-xie-zuo-shi-yong/","title":"git的协作使用"},"https://king-key.github.io/Blog/2023/Q4/zen-me-xie-ti-shi-ci/":{"body":"\nAIGC绘画提示词应该怎么写\n\n1、基本\n\n提示词由多个单词、词组和短句组成，用逗号分开\n分为正向提示词positive prompt和反向提示词negative prompt\n权重：\n\n范围0.1-100，默认为1，低于1是减弱，大于1是增强\n顺序影响权重，按照顺序依次减弱\n权重比例相对，3:1 = 6:2\n\n\n顺序\n\n明确主题：画面质量->主要元素->细节\n明确风格：画面质量->风格->元素->细节\n景色在前，人物会变小，反之亦然\n参考顺序\n\n画面质量，风格，主题，外表（发型、发色、衣服、眼睛、手臂、胸部、腿部....），情绪，姿势，背景\n（画面质量+风格+镜头效果+光照效果+主题+构图）+ 主体（人物&对象+姿势+服装+道具）+ 细节（场景+环境+饰品+特征）\n\n\n\n\n符号（可以用来改变提示词的权重）\n\n符号介绍格式eg\n:自定义权重比例（prompt:数字）（girl:0.8），权重0.8\nand权重一致\n|交替渲染\n<>调用Lora模型lora:filename:数字\n[]交替渲染[prompt1:prompt2:0.5]prompt1渲染到50%的时候，渲染开始prompt2\n融合[prompt1｜prompt2]融合1和2\n\n\n命令参数\n\n命令解释\n--seed number种子\n--aspect number: number宽高比\n--version number版本\n--chaos number多样性\n--quality number质量\n--sameseed相似值\n--stop停止\n--stylize number风格化\n--iw图像权重\n--uplight轻量放大器\n--upbeta测试版放大器\n--niji动漫模型\n--test测试模型\n--testp摄影测试模型\n--hd高清模型\n--repeat number重复\n\n参考：https://id8ex69uxy.feishu.cn/wiki/wikcn6VV58f94Wwt4hNuy7xex3e\n2、提示词\n分类提示词\n动作姿势Arms Wide Open (双臂张开)Leaning Forward (身体前倾，像鞠躬那样)Sitting on a Chair(坐在椅子上)Hand on Hip (手叉腰)Hand in Pocket (手插兜)Crossed Arms (双臂交叉)Crossed Legs (翘二郎腿)Kneeling (跪地)Hands Up (双手举起来)Arms Up (双臂高举)Arms Behind Back (双臂放在身后)Salute (敬礼)Jumping (跳跃)All Fours (四肢着地，爬行姿势)Yoga Pose (瑜伽姿势)High Kicks (高踢腿)Leaning Against a Wall (靠在墙上)Looking Back Over Shoulder (回头看)Running (跑步)Squatting（蹲着）Catwalk Pose (T型台走秀姿势)Hand Wave (挥手)Fighting Stance(战斗姿势)Standing on One Leg (单腿站立)\n\n\n3、提示词插件\n\nstable-diffusion-webui-wd14-tagger\n\n可以根据图片反推提示词\n\n\nsd-prompt-translator\n\n汉化后的提示词选择器\n\n\nsdweb-easy-prompt-selector\n\n提示词翻译插件，可以直接用中文写提示词\n\n4、关于分辨率\n\n正方形（1024x1024）： 这是默认分辨率，适合大多数普通图像。\n宽屏（1792x1024）： 适合风景、全景或任何需要水平方向的图像。\n高（1024x1792）： 最适合全身肖像、高大结构或任何需要垂直方向的图像。\n\n","id":"https://king-key.github.io/Blog/2023/Q4/zen-me-xie-ti-shi-ci/","title":"Stable Diffusion Webui中怎么写提示词"},"https://king-key.github.io/Blog/2024/":{"body":"","id":"https://king-key.github.io/Blog/2024/","title":"index"},"https://king-key.github.io/Blog/2024/Q1/":{"body":"","id":"https://king-key.github.io/Blog/2024/Q1/","title":"index"},"https://king-key.github.io/Blog/2024/Q1/01-hf-torrent/":{"body":"\n用种子协议下载Huggingface模型\n\n\n多年后提起 2023 时，一个不可避免的关键词肯定是「大模型」。在各种大模型科研如火如荼展开的同时，大模型下载也成为了很多人的烦恼。Huggingface 每月仅模型的下载流量就超过了 10PB。\n为此，MIT 的朱力耕博士开发了一个叫做 HF-Torrent 的工具，可以使用种子协议（Torrent）以P2P形式进行下载。希望大家在2024年的出版物和模型大小一样都能节节高🎆！\nDemo展示\n\n如何使用\n\n在启动下载之前，请确保您已安装**aria2c**并在单独的终端中运行以下命令\n\n然后，可以使用以下命令开始使用HF-Torrent：\n\n或者，如果您有喜欢的种子客户端，只需要提取种子文件\n\n项目地址\nhttps://github.com/lyken17/hf-torrent\n","id":"https://king-key.github.io/Blog/2024/Q1/01-hf-torrent/","title":"HF大模型下载利器"},"https://king-key.github.io/Blog/2024/Q1/2024-02-29/":{"body":"1、问题\nLinux下的虚拟环境执行命令\n\n而在Windows的Power Shell中却会报错\n\n2、解决\n以管理员权限打开Power Shell，执行命令\n\n然后执行命令\n\n⚠️注意：这里是执行的Scripts目录下的activate.psl，不是bin目录下的activate.psl\n","id":"https://king-key.github.io/Blog/2024/Q1/2024-02-29/","title":"Windows下如何用命令打开虚拟环境（venv）"},"https://king-key.github.io/Blog/2024/Q1/2024-03-12/":{"body":"1、git push报错\n\n2、解决\n修改.git -> config文件 ->url参数\n\n这个是正常的url\n\n\n\n这个是修改后的，在github.com前面加上了”{github用户名}@“\n\n\n","id":"https://king-key.github.io/Blog/2024/Q1/2024-03-12/","title":"git push报错解决"},"https://king-key.github.io/Blog/2024/Q1/2024-03-17/":{"body":"安装（Ubuntu）\n\n使用\n\n","id":"https://king-key.github.io/Blog/2024/Q1/2024-03-17/","title":"Linux中docker使用"},"https://king-key.github.io/Blog/2024/Q1/fastgpt-chatglm2-wechat/":{"body":"\n没有微信限制\n\n先看效果\n\n左：单人回复；右：社群回复\n\n\n\nChatGLM2-6B-int4本地部署\n本地部署教程之前写过，按照这个来即可：https://wangguo.site/posts/9d8c1768.html\n这个是运行web_demo.py的效果\n\n这次运行openai_api.py，启动接口服务\n\n\nOne-API接入ChatGLM2-6B-int4的OpenAI服务接口\n参考：https://doc.fastgpt.in/docs/development/one-api/\n基于docker进行部署即可，只需要一条命令\n\n\n-p，这里用了本地的13000端口，也可以调整\n-v，是指数据和日志的保存目录，也可以调整\n\n安装完成后，打开http://localhost:13000\n\n默认账号：root\n密码：123456\n\n\n点击渠道 -> 添加新的渠道，按照如下进行配置并进行提交\n\n\n类型：OpenAI，这个是固定的\n名称：chatglm2，这个随意\n模型：chatglm2，这个通过“输入自定义模型名称”来输入，这个名称是后面FastGPT里面会显示的名称\n代理：本地ChatGLM2-6B-int4的接口服务地址\n\n点击令牌-> 添加新的令牌\n\n\n名称等信息随便写即可，最后点击这里的复制，可以拷贝令牌（后面要用到）\n\n\nFastGPT通过One-API接入本地模型ChatGLM2-6B-int4\nDocker Compose快速部署\n参考：https://doc.fastgpt.in/docs/development/docker/\n\n在docker-compose.yml修改 FastGPT 的环境变量(第52行)\n\n在config.json中加入ChatGLM2\n\n\n启动容器\n\n通过http://localhost:3000进入FastGPT\n\n默认账号：root\n密码：1234\n\n\n创建ChatGLM2应用\n\n应用 -> 新建\n\n\n\nAI模型选择ChatGLM2\n\n\n测试\n\n在FastGPT创建的应用里面，选择外部使用 -> API访问\n\n新建API密钥管理，名称随意，拷贝这个密钥\n\n智能微秘书接入FastGPT中的ChatGLM2-6B-int4\n参考：https://doc.fastgpt.in/docs/use-cases/wechat/\n配置微秘书\n\n打开微秘书 注册登陆后找到菜单栏「基础配置」->「智能配置」，选择智能机器人回复，如下配置。这里的apikey就是刚才FastGPT中拷贝的\n\n\nDocker部署智能微秘书客户端\n\n\n微秘书apikey和微秘书apiSecret可以在个人中心中获得\n\n\n进入首页扫码登陆微信，结束！\n\n问题总结\n\n问题：chatglm的openAPI报错\n\n\n解决：参考https://github.com/THUDM/ChatGLM2-6B/issues/483\n将openai_api.py文件中3处\n\n替换为\n\n","id":"https://king-key.github.io/Blog/2024/Q1/fastgpt-chatglm2-wechat/","title":"FastGPT,ChatGLM2,WeChat本地化部署实战"},"https://king-key.github.io/Blog/2024/Q1/llmzhong-de-yi-xie-ji-ben-gai-nian/":{"body":"\n逐步系统化地了解一下\n\n☝ 基本概念\n1、预训练（Pre-training）\n预训练是指在特定任务前，让模型在大规模数据上进行训练的过程。预训练可以使模型学习到数据中的通用特征，从而在后续任务中取得更好的性能\n2、微调（Fine-Tuning）\n微调是在预训练模型的基础上，使用标注数据对模型进行进一步训练，以适应特征任务的方法。微调可以使预训练模型在目标任务上具有更好的效果。\n3、强化学习（Reinforcement Learning,RL）\n强化学习事一种人工智能技术，通过让智能体在环境中尝试行动，根据行动的结果来调整策略，以实现某种目标的方法。强化学习算法主要关注如何通过试错学习来优化决策过程。\n4、人类反馈强化学习（Reinforcement Learning from Human Feedback，RLHF）\nRLHF是一种基于人类反馈的强化学习方法，通过学习人类提供的奖励信号和反馈来训练智能体，其宗旨是使智能体能更好地是适应人类的需求和偏好\n5、思维链（Chain of Thoughts，CoT）\n思维链是一种表达人类推理过程的方法。通过一系列的逻辑推理步骤将从初始条件和结论相连。（就像数学公式的推理）。在人工智能领域，思维链可以用于表达模型在解决问题时的推理过程。\n6、涌现（Emergence）\n涌现是指在一个复杂系统中，通过相互作用和反馈机制产生的宏观现象。大模型能力的涌现是指：当模型达到一定规模（如计算量、模型参数或者数据集大小）时，模型会表现出意想不到的能力，这些能力在小规模模型中是不存在的。\n7、泛化（Generalization）\n泛化是指模型在训练集数据集上学到的知识能否在测试数据集上取得较好性能的能力。一个好的模型应该具备较好的泛化能力，从而在新数据上取得更好的表现。\n8、幻觉（hallucination）\n大模型的幻觉是指模型在生成的内容产生了一些和现实世界/用户输入不一致的现象。大模型的幻觉可以分为事实性幻觉（Factuality Hullucination）和忠实性幻觉（Faithfulness Huallunination）。\n9、Transformer\nTransformer是一种神经网络结构，之前一直用于自然语言处理任务。它采用的是自注意力机制（Self-Attention Mechanism）来捕捉输入序列中的长距离依赖关系。\n10、注意力机制（Attention Mechanism）\n注意力机制是一种计算方法，用户在输入序列中动态的分配权重。在Transformer结构中，注意力机制可以帮助模型在处理场序列时只关注与当前目标相关的部分，从而提好计算效率和性能。\n11、自回归（Autoregressive）\n自回归是一种生成模型，通过逐个预测序列中的元素，从而从前往后地生成序列。\n12、Token\n在自然语言处理中，Token是指文本的最小单元，可以是单词、字符或者其他语言单位。在深度学习中，Token用于表示输入数据的基本元素，以便进行编码和处理。\n13、指令微调（instruction Tuning，IT）\n指令微调是针对指令生成任务的语言模型预训练方法，通过让模型学习指令和形影的输入之间的关系，提高指令生成任务的性能。\n14、温度（Temperature）\n在生成模型中，温度参数用于控制生成过程的随机性。较低的温度会导致生成的结果更加具有确定性，相反会使得生成结果更加随机。\n15、Top-p\nTop-p是一种用于生成模型中的概率分布策略。它通过保留输入序列中概率前p的元素，来控制生成过程中的词汇多样性。\n16、自监督\n自监督学习是一种无监督学习方法，即模型可以在没有标签的数据上学习到有用的特征表示。在大模型中，自监督可以作为一种在无标签数据上进行预训练的方法。\n17、检索增强生成（RAG，Retrieval Augmented Generation）\nRAG是是通过检索获取知识库里面的相关知识，并将其融入到Prompt中，让大模型能够参考相应的知识从而给出合理的答案。其核心是“检索+生成”。\n18、混合专家模型（MOE，Mixture of Experts）\nMOE是一种稀疏门控制的深度学习模型，它主要由一组专家模型（Experts）和一个门控模型（GateNe）组成。MoE的基本理念是将输入数据根据任务类型分割成多个区域，并将每个区域的数据分配一个或多个专家模型。每个专家模型可以专注于处理输入这部分数据，从而提高模型的整体性能。简单理解就是：术业有专攻+三个臭皮匠，顶个诸葛亮。代表作品Mistral 8x7B。\n19、多模态大模型（Multimodal Large Language Models, MLLM）\n文本、语音、图像等等，这些都可以称为是一种模态。多模态就是对Language、Vision、Audio进行联合建模。包括到3d, radar, point cloud, structure (e.g. layout, markup language)。多模态大模型就是一个端到端的具备多种模态输入，多种模态输出的大模型。\n","id":"https://king-key.github.io/Blog/2024/Q1/llmzhong-de-yi-xie-ji-ben-gai-nian/","title":"LLM中的一些基本概念"},"https://king-key.github.io/Blog/2024/Q2/":{"body":"","id":"https://king-key.github.io/Blog/2024/Q2/","title":"index"},"https://king-key.github.io/Blog/2024/Q2/2024-05-09/":{"body":"1、ollama安装使用\n1.1、ollama\n\nGet up and running with large language models locally.\n\n\n1.2、支持的模型列表\n\n1.3、下载安装\n\n1.4、使用（基于终端操作）\n1.4.1、下载模型(以llama3为例)\n\n下载需要等待一段时间，同时也需要科学上网\n1.4.2、使用模型\n\n\n2、obsidian中使用ollama\n2.1、obsidian插件copilot\n插件市场搜索copilot进行安装并启用\n\n2.2、copilot中配置ollama\n打开copilot的配置文件进行设置\n2.2.1、default model修改为ollama\n\n2.2.2、QA Setting设置为ollama-nomic-embed-text\n需要先用ollama pull命令下载模型\n\n\n2.2.3、ollama设置model为llama3\n\n3、obsidian中使用llama3\n3.1、执行ollama serve\n此时需要关闭之前打开的ollama，重新用命令打开ollama\n\n3.2、obsidian使用\n\n打开copilot对话窗口\n\n设置为ollama\n\n使用\n\n\n3.3、copilot中除了对话功能还有Long Note QA、Vault QA功能，可以继续探索使用一下\n\n","id":"https://king-key.github.io/Blog/2024/Q2/2024-05-09/","title":"obsidian中使用ollama部署llama3,实现本地对话"},"https://king-key.github.io/Blog/2024/Q2/2024-06-04/":{"body":"\n之前使用的微秘书对微信接口收费了，没法免费接入大模型。只能换成chatgpt-on-wechat接入了\n\n往期参考文章\n1、 FastGPT,ChatGLM2,WeChat本地化部署实战\n2、obsidian中使用ollama部署llama3,实现本地对话\n1、ollama安装\nollama安装参考：obsidian中使用ollama部署llama3,实现本地对话中的第一小节\n1.1、下载对应版本ollama进行安装\n\n1.2、在终端中使用ollama\n常用命令\n\n\n要pull的模型可以在ollama的模型仓库中查看\n\n\n\n要run的模型可以通过ollama ls查看\n\n\n\nollama serve之后，就可以通过api的方式调用ollama服务了\n\n\n2、FastFPT中调用ollama\n2.1、安装FastGPT\n安装\n参考：Docker Compose快速部署\n\n运行\n\n\n此时，我们可以在docker-desktop 中看到已经运行的fastgpt，其中已经自带运行了one-api\n2.2、One API+ollama\n参考：One-API接入ChatGLM2-6B-int4的OpenAI服务接口\n登陆one api\n浏览器端打开one api的网页界面localhost:3001\n\n默认账号：root\n密码：123456\n\n\n新建渠道\n\n\n类型：OpenAI 即可\n名称：随意\n分组：default 即可\n模型：选择ollama ls查看到的模型\n密钥：随便填写一下就好，例如sk-fastgpt\n代理：ollama serve的地址，一般情况下用本地ip+端口11434即可\n\n新建令牌\n\n\n名称：随意\n模型范围：选择渠道里面的模型即可\n\n2.3、FastGPT+ollama\n修改fastgpt的docker-compose.yml配置文件(第52行)\n\n\n重启fastgpt\n\nfastgpt中新建ollama应用\n\n\n\n在AI模型中可以看到ollama在本地下载的模型，也就是在one api中选择的模型\n测试\n\n3、chat-on-wechat+fasgpt\n3.1、chat-on-wechat安装\n3.2、配置fastgpt\n4、chat-on-wechat+ollama实现微信机器人对话\n","id":"https://king-key.github.io/Blog/2024/Q2/2024-06-04/","title":"ollama+one api+fastgpt+微信，实现微信机器人"},"https://king-key.github.io/Blog/2024/Q2/2024-06-14/":{"body":"\n说明：安装使用环境是在Windows下\n\n1、给ollama一个好看的交互界面（open webui）\n1.1、ollama安装\n\n\n\n安装：在ollama官网下载windows版本进行安装\n\n\n\n模型列表：支持的模型列表可以在ollama模型仓库查看\n\n\n\n模型下载：使用ollama pull 模型名称下载模型\n\n如：ollama pull llama3\n\n\n模型使用\n\n\n终端使用：使用ollama run 模型名称即可使用\n如：ollama run llama3\n\n\napi使用：使用ollama serve、或者直接点击ollama图标即可\n\n\n\n\n\napi服务验证：浏览器端输入127.0.0.1:11434，显示ollama id running即为正常\n\n\n\n1.2、open webui安装\n\nOpen WebUI是一个可扩展、功能强大且用户友好的自主WebUI，旨在完全离线操作。它支持多种LLM运行器，包括Ollama和OpenAI，兼容API。\n\n\n基于docker安装open webui\n\n\n\n本地打开：localhost:3000\n\n首次打开需要注册，第一位注册的用户默认为管理员（注册的信息随便填写，只要邮箱符合@规则即可）\n\n此时我们点击“选择一个模型”的时候是没有模型列表的，这个时候就需要配置ollama了\n1.3、open webui引入ollama\n\n首先，我们需要让ollama可以通过本地ip访问，windows下需要新建环境变量。\n\n\n步骤：windows下直接搜索编辑系统环境变量-环境变量-在系统变量中新建-变量名：OLLAMA_HOST；变量值：0.0.0.0\n\n\n\n验证ollama：此时在浏览器输入本地ip+端口号就显示可以正常使用了。比如我的地址为：192.168.1.9:11434\n\n\n\n在open webui中配置ollama服务，并进行验证\n\n\n步骤：点击右上角的图标-设置-管理员设置-外部连接-在ollama api中填写本地ip+端口号，如192.168.1.9:11434\n验证：点击后面的刷新按钮，弹出已验证服务器连接\n\n\n\nopen webui中使用ol lama服务\n\n此时就可以选择ollama下载的模型进行对话使用了\n\n2、在open webui中连接sd，引入图像生成的功能\n2.1、sd webui安装\n\n前提：需要安装git/python\n\n\n\n打开本地IP+端口号7860\n\n\n\n在web.bat中添加启动参数（13行左右），以便sd提供api服务\n\n\n2.2、open webui引入sd\n在管理员面板- 设置-图像里面按照下图进行配置，所有的参数都可以根据自己的进行修改\n\n3、使用\n\n正常对话，进行描述\n\n\n\n点击图像生成按钮，便可以生成图像\n\n\n","id":"https://king-key.github.io/Blog/2024/Q2/2024-06-14/","title":"windows下open webui+ollama+sd webui"},"https://king-key.github.io/Blog/2024/Q3/":{"body":"","id":"https://king-key.github.io/Blog/2024/Q3/","title":"index"},"https://king-key.github.io/Blog/2024/Q3/2024-07-06/":{"body":"\n学习资料：提示工程指南\n\n1、模型设置\n\nTemperature：值越小，结果越准确；反之，更具有想象力。\nTop_p：和Temperature一样的效果。\nMax Length：大模型生成的Token数。（Token太长，容易冗余且成本高）\nStop Sequences：阻止模型生成 token。类似于Max Length。\nFrequency Penalty：对下一个生成的 token 进行惩罚，和重复次数成正比。\nPresence Penalty：对所有重复的 token 进行相同的惩罚。设置越高，重复词越少，内容更丰富；反之，越专注。\n\n\ntemperature 和 top_p,frequency penalty 和 presence penalty，只需要调整其中一个参数就行，不要同时调整两个。\n\n2、提示词\n要素\n指令、上下文、输入数据、输出指示：输出的格式或类型\n技巧\n具体、准确、提供正向执行\n技术\n\n零样本提示（zero-shot prompting）\n少样本提示\n链式思考（CoT）提示\n\n\n\n零样本 COT 提示：让我们逐步思考\n\n\n\n自动思维链（Auto-CoT）\n\n\n\n\n自我一致性：通过少样本 CoT 采样多个不同的推理路径，并使用生成结果选择最一致的答案。\n\n\n生成知识提示：问题，知识,解释和答案\n\n\n\n链式提示（prompt chaining）：将任务分解为许多子任务。 确定子任务后，将子任务的提示词提供给语言模型，得到的结果作为新的提示词的一部分。\n\n\n","id":"https://king-key.github.io/Blog/2024/Q3/2024-07-06/","title":"Prompt学习"},"https://king-key.github.io/Blog/2024/Q3/2024-08-08/":{"body":"\n虽然日常用不了，但是可以简单了解下\n\n预训练成本\n$预训练成本 = ( 模型参数量 ✖️ 6 /A100 卡每秒的计算次数) ✖️ 显卡的租用成本 ✖️ 训练数据的 token 量$\n\n\n常量\n\n\nA100 卡每秒的计算次数 = 312 TFLOPS/Watt (官方公布，每秒可以进行 312T 次浮点数计算)\n\n\n显卡租用成本：暂时以微软 Azure 云上公布的 Nvdia A100 的三年期租用价格$2.2659/小时计算，本文按秒来计算，就是每秒租用价格为$(2.2659/3600) (参考 https://azure.microsoft.com/en-us/pricing/details/virtual-machines/linux/#pricing)\n\n\n\n\neg：\n\nGPT3 的参数量是 175B，训练数据的 token 量 500B，约 105 万美元*5 折，约 372.75 万元。\nGPT4 的参数量是 1800B，训练数据的 token 量 13T， 费用是 GPT3 的 280 倍，约 1.45 亿美元，人民币 10 亿元。\nGPT4 Turbo 的参数量是 8*222B ，训练数据的 token 量 13T，费用与 GPT4 差不多。\n\n推理成本\n$$\n\\text{推理成本（每千个token）} = \\frac{\\text{模型参数量} \\times 2 \\times \\text{每次推理所需token数}}{\\text{GPU每秒计算次数} \\times \\text{显卡租用成本}}\n$$\n$$\n\\text{单位token推理成本} = \\frac{\\text{推理成本（每千个token）}}{\\text{batch size}}\n$$\n$$\n\\text{并行化推理成本} = \\frac{\\text{单卡推理成本}}{\\text{并行卡数}}\n$$\n$$\n\\text{优化后推理成本} = \\text{并行化推理成本} \\times (1 - \\text{优化效率})\n$$\n其中：\n\n模型参数量是指模型中的可训练参数总数。\n每次推理所需token数是指每次推理过程中模型需要处理的token数量。\nGPU每秒计算次数是指GPU在一秒内能够完成的浮点运算次数。\n显卡租用成本是指租用GPU进行计算的成本。\nbatch size是指每次推理可以同时处理的样本数量。\n并行卡数是指用于并行化推理的GPU数量。\n优化效率是指通过模型压缩、硬件加速等方法提高效率的比例。\n\n请注意，这个公式是一个简化的示例，实际的推理成本计算可能需要考虑更多的因素，如模型的具体架构、硬件的具体性能、电力成本、运维成本等。\neg：GPT4 Turbo\n根据之前提供的搜索结果，我们可以构建一个简化的计算公式来估算GPT4 Turbo模型的推理成本。请注意，实际成本可能会因各种因素（如电力消耗、冷却系统、人工维护等）而有所不同，但以下公式提供了一个基本的估算方法。\n首先，我们需要以下参数：\n\n模型参数量（( P )）：GPT4 Turbo的参数量为8,222亿（即 $8.222 * 10^{11}$ ）。\n每次推理所需token数（T ）：假设为1,000（即1千个token）。\nGPU每秒计算次数（ C ）：假设使用NVIDIA A100 GPU，其计算次数可以是一个估算值，例如每秒可以处理的浮点运算次数。\n显卡租用成本（ R ）：假设租用成本为每小时一定金额，例如每小时3.93美元。\n\n根据搜索结果中的一个示例，我们可以构建如下公式：\n$ \\text{每千个token推理成本} = \\frac{P \\times T}{C \\times R} $\n将示例中的值代入公式：\n\n$  P = 8.222 \\times 10^{11} （模型参数量）$\n$ T = 1,000 （每次推理所需token数）$\n$C$ 假设为 $10^{14} $（A100 GPU每秒的计算次数，这是一个示例值，实际值可能不同）\n( R = 3.93 )（每小时的租用成本，以美元计）\n\n计算公式变为：\n$ \\text{每千个token推理成本} = \\frac{8.222 \\times 10^{11} \\times 1,000}{10^{14} \\times 3.93} $\n进行计算：\n$\\text{每千个token推理成本} = \\frac{8.222 \\times 10^{14}}{3.93 \\times 10^{14}}$\n$\\text{每千个token推理成本} \\approx 2.09 \\text{美元} $\n这个计算结果是一个简化的估算，实际成本可能会更高，因为还需要考虑其他因素，如电力成本、冷却系统成本、运维成本以及可能的优化方法等。此外，实际的GPU计算次数和租用成本可能会根据具体使用情况和供应商而有所不同。\n","id":"https://king-key.github.io/Blog/2024/Q3/2024-08-08/","title":"关于大模型的使用成本计算"},"https://king-key.github.io/Blog/2024/Q3/2024-08-18/":{"body":"\n用程序调用api搭建自己的ollama\n\nollama api\n\n\n\n\n\n\n\n参数\n\nmodel：（必填）模型名称\nprompt：生成响应的提示\nsuffix：模型响应后的文本\nimages：（可选）base64编码图像列表（用于多模态模型，例如llava）\n\n高级参数（可选）：\n\nformat：返回响应的格式。目前唯一接受的值是json\noptions： Modelfile文档中列出的其他模型参数，例如temperature\nsystem：系统消息（覆盖 中定义的内容Modelfile）\ntemplate：要使用的提示模板（覆盖 中定义的内容Modelfile）\ncontext：从上一次请求返回的上下文参数/generate，可用于保存简短的对话记忆\nstream：如果false响应将作为单个响应对象返回，而不是对象流\nraw：如果true提示不应用任何格式。raw如果您在向 API 发出的请求中指定了完整的模板提示，则可以选择使用该参数\nkeep_alive：控制模型在请求后保持加载到内存中的时间（默认值5m：）\n\n\n参数\n\nmodel：（必填）模型名称\nmessages：聊天消息，可用于保存聊天记忆\ntools：模型使用的工具（如果支持）。需要stream设置为false\n\n该message对象具有以下字段：\n\nrole：消息的角色，可以是system、user、assistant或tool\ncontent：消息内容\nimages（可选）：要包含在消息中的图像列表（用于多模式模型，例如llava）\ntool_calls（可选）：模型想要使用的工具列表\n\n高级参数（可选）：\n\nformat：返回响应的格式。目前唯一接受的值是json\noptions： Modelfile文档中列出的其他模型参数，例如temperature\nstream：如果false响应将作为单个响应对象返回，而不是对象流\nkeep_alive：控制模型在请求后保持加载到内存中的时间（默认值5m：）\n\n简单使用\n\n\n与LangChain一起使用\n\n参考：https://github.com/ollama/ollama/blob/main/docs/tutorials/langchainpy.md\n\n","id":"https://king-key.github.io/Blog/2024/Q3/2024-08-18/","title":"调用本地ollama api服务"},"https://king-key.github.io/Blog/2024/Q3/2024-08-19/":{"body":"\n部署环境：Windows\n\n安装 supabase向量数据库\n安装Windows下的包管理器scoop\n\n使用scoop安装supabase\n\n参考：https://supabase.com/docs/guides/cli/getting-started?queryGroups=platform&platform=windows\n\n\n在运行supabase之前，需要先配置docker desktop\n\n设置好之后开始运行supabase\n\n\n此时浏览器端输入http://192.168.1.8:54323/就能看到supabase的界面\n\n安装Quivr\n\n修改.env的参数，取消注释\n\n\nPS：这一步需要的时间是真长啊～\n登陆localhost:3000,账号邮箱admin@quivr.app,密码admin\nPS：不弄了～这玩意是真连不上。上一个版本就是，这个版本的Quivr也是，闹心～～～\n","id":"https://king-key.github.io/Blog/2024/Q3/2024-08-19/","title":"Quivr + Ollama 本地部署知识库"},"https://king-key.github.io/Blog/2024/Q3/2024-09-27/":{"body":"\n在zola主题博客中，引入搜索功能\n\nhtml添加搜索按钮，并引入css,js文件\n\n同时需要引入js,css文件\n\nsearch.js文件\nSearch.js会生zola build的时候生成elasticlunr.min.js和search_index.en.js\n\ncss样式文件（style.css）\n\n效果\n\n\n","id":"https://king-key.github.io/Blog/2024/Q3/2024-09-27/","title":"在zola博客里面增加搜索功能"},"https://king-key.github.io/Blog/2025/":{"body":"","id":"https://king-key.github.io/Blog/2025/","title":"index"},"https://king-key.github.io/Blog/2025/02-05/":{"body":"\npip安装open webui报错\n\n报错信息\n\n原来是，OpenWebui只支持Python3.11\n\n解决方案：https://github.com/open-webui/open-webui/discussions/2596\n\n用pipx install --fetch-missing-python --python 3.11 open-webui安装即可！\n","id":"https://king-key.github.io/Blog/2025/02-05/","title":"OpenWebui只支持Python 3.11版本"},"https://king-key.github.io/Blog/2025/02-13/":{"body":"\n之前的Zola都是本地build之后，再push到github上，这种方式很明显的弊端就是只能在本地编辑，而不能通过github编辑，再pull到本地，缺乏了灵活性。因此将zola用workflows来部署。\n\n主要就是增加workflows文件\n完整的 .github/workflows/main.yml 文件\n\n本地运行workflows\n\ngh需要安装和登录验证。Mac环境下：\n\n可以选择跳转到浏览器进行登录验证，之后即可本地用命令运行workflows了\n","id":"https://king-key.github.io/Blog/2025/02-13/","title":"zola + github page,用 workflows 部署"},"https://king-key.github.io/Blog/2025/02-14/":{"body":"\nDeepSeek R1最近很火，可以试试用来编程（前提是有VSCode）\n\nDeepSeek R1本地部署\n可以通过ollama实现（ollama官网下载安装即可）\n使用终端pull & 运行 r1。官网有不同参数版本的r1模型，量力选择！\n\n\n在VSCode中使用\nvscode插件中，搜索Continue插件安装\n\n选择本地的 ollama 服务，并选择 r1 模型即可\n\nContinue可以通过 @ 实现文档内容交互\n","id":"https://king-key.github.io/Blog/2025/02-14/","title":"VSCode中使用DeepSeek R1"},"https://king-key.github.io/Blog/2025/02-15/":{"body":"\n青稞AI 公众号一直是以纯粹的技术分享为主，当然中间也会掺杂广告（青稞Talk的宣传），所以本身对公众号的体量没有太高的要求。\n但是今天突然看到了长读用户的数据，突然觉得一切都是有价值的！\n一直不太喜欢“流量运行”的逻辑，把所有来关注你的人当作一个数字，忽略了作为人的个性化。\n继续加油吧，看看纯粹以技术分享为主的公众号，到底能有多大的体量？\n","id":"https://king-key.github.io/Blog/2025/02-15/","title":"微信公众号常读用户增长到19%"},"https://king-key.github.io/Blog/2025/03-06/":{"body":"html模版\n\n效果\n\n\n第一篇\n\n\n\n中间\n\n\n\n","id":"https://king-key.github.io/Blog/2025/03-06/","title":"zola中增加上一页、下一页的功能"},"https://king-key.github.io/Blog/2025/05-30/":{"body":"这是一篇来自上海人工智能实验室青年科学家崔淦渠发在朋友圈的文案。\n\n在经过作者本人同意的情况下，将内容整理后，发布在青稞社区的渠道上，帮助宣传！以下为编辑后的内容：\n\n在RL训练LLM的过程中，普遍存在模型entropy迅速下降/性能迅速饱和的现象。这导致模型过度自信，进而削弱其探索能力，最终限制了性能的进一步提升。\n\n我们通过广泛的实验，在不加熵干预的前提下建立了一个描述模型性能（R）与策略熵（H）关系的经验公式：\n$$\nR = -a * exp(H) + b\n$$\n揭示了两者间的“权衡”关系，即性能的提升往往伴随着策略熵的消耗。更重要的是，这一关系预示了性能的理论上限（当 $H=0$ 时，$R = -a+b$ ）。因此，若不有效进行熵管理，单纯增加RL训练的计算资源，其效益可能受限。\n\n针对熵单调下降的现象，我们从理论与实验两方面深入分析了其动态机制。研究的核心结论是：熵的动态变化主要由所选动作的概率与对应logits变化量（在策略梯度类算法中，该变化量与优势advantage成正比）之间的协方差所驱动。\n\n具体而言，高概率且具备高优势的动作倾向于降低熵，反之，低概率但具备高优势的动作则有助于提升熵。实验发现，在训练过程中，此协方差多数情况下为正值，从而导致熵持续下降。\n\n基于对熵动态机制的理解，我们提出了两种针对性的熵控制方法：Clip-Cov 与 KL-Cov。这两种方法通过对高协方差的token更新进行约束来控制熵，防止其过早崩溃。实验结果表明我们提出的熵控制方法效果显著，能够促使模型进行更持续的探索，从而摆脱“低熵陷阱”，并取得更优性能。\n以Qwen2.5-32B模型为例，与基线GRPO相比，我们的方法实现了平均6.4%的性能提升；在AIME24等高难度基准测试中，提升高达15.0%。\n\n\n\n大模型在为强化学习提供强大先验的同时，其更为狭窄的输出分布是一把双刃剑。最近许多工作表明简单地使模型的输出分布更加狭窄就可以提升模型表现，但这同时也榨干了模型继续探索和提升的空间。我们希望为scale大模型的强化学习提供一个以熵为核心的全新视角。\n相关论文与代码均已公开发布，欢迎大家阅读、指正与交流！\n\n","id":"https://king-key.github.io/Blog/2025/05-30/","title":"上海人工智能实验室青年科学家崔淦渠:在大模型推理强化学习中熵机制上的探索"},"https://king-key.github.io/Blog/2025/05-31/":{"body":"前言\n\n这是一篇来自香港大学博士生吴成岳发在朋友圈的文案。\n首发：https://mp.weixin.qq.com/s/o0a-swHZOplknnNxpqlsaA\n\n\n\n正文\n最近的Gemini Diffusion语言模型展现了惊人的throughput和效果，但是开源的扩散语言模型由于缺少kv cache以及在并行解码的时候性能严重下降等问题速度严重受影响。\n\n我们基于这两点出发，提出了Fast-dLLM，并设计相应的解决策略加速当前的扩散语言模型。\n\n核心设计\n分块KV缓存机制\n针对双向扩散模型设计，通过缓存前缀和后缀token的注意力激活值（DualCache），实现90%+激活重用，大幅减少冗余计算。相邻步骤的KV激活余弦相似度接近1，验证了缓存复用的高效性\n\n置信度感知并行解码\n动态筛选置信度超过阈值（如≥0.9）的token批量解码，既规避了条件独立假设导致的依赖破坏，又通过理论证明当阈值和解码的token数满足条件时并行解码与顺序解码结果一致，保证生成质量，实现13.3倍速度提升\n\n实测效果\n\n\n速度：在LLaDA模型上，1024token长文本生成实现27.6倍端到端加速，单步延迟从0.26秒降至0.09秒\n精度：GSM8K等基准测试中，准确率损失控制在2%以内，如LLaDA+Fast-dLLM在5-shot场景下仍保持78.5%准确率\n兼容性：支持LLaDA、Dream等主流扩散模型，覆盖数学推理（MATH）、代码生成（HumanEval）等多场景\n\n应用价值\n无需重新训练模型，直接提升扩散LLM的推理效率，缩短长文本生成耗时，为实际部署提供可行性。例如，8-shot提示的1024token生成任务，从266秒压缩至12秒，效率提升显著。\n\n","id":"https://king-key.github.io/Blog/2025/05-31/","title":"香港大学博士生吴成岳:无需重新训练模型，直接提升扩散语言模型的推理效率的Fast-dLLM"},"https://king-key.github.io/Blog/2025/06-03/":{"body":"前言\n\n这是一篇来自刁诗哲(research scientist @ NVIDIA Research)的文案。\n首发：https://mp.weixin.qq.com/s/o0a-swHZOplknnNxpqlsaA\n\n\n正文\n\n\n最近LLM + RL的研究越来越火，但关于“RL是否真的能提升模型的推理能力”这一问题，社区仍存在不少争议。一些研究指出，RL只是在让模型变得更会“猜答案”，而非真正学会新能力。\n我们认为这些质疑可能来自两个“隐藏问题”：\n\n太多研究只在数学领域做实验，而这类任务基础模型早就见过，根本没有探索新能力的空间；\nRL训练都太短了——几百步就停止，模型还没开始“发掘潜能”就被叫停了。\n\n\n因此，我们认为应该在更加多样的任务上做更加长期的强化学习训练。然而，让模型在强化学习中实现长期且稳定的训练并不容易。传统方法常常面临梯度爆炸或训练坍塌等问题。为此，我们将 模型的 entropy（熵） 作为衡量训练健康程度的关键指标。Entropy 反映了模型在探索与利用之间的平衡能力，也代表了模型对问题理解的深度。\n训练过程中，我们力求保持 entropy 在一个合理且稳定的区间：过高的 entropy 意味着模型在做出决策时过于随机，缺乏有效判断；过低的 entropy 则表明模型可能陷入某个解法，失去了进一步探索的能力。\n通过持续监控并调控 entropy，我们有效促进了 RL 训练的稳定性和推理能力的提升。在此基础上，我们提出了ProRL（Prolonged Reinforcement Learning），一个长期强化学习训练框架，核心包括：\n\nKL控制（保持模型不跑偏）\n参考策略重置（防止陷入局部最优）\n多样化任务集合（激发模型学习不同推理策略）\n\n我们基于 DeepSeek-Distilled-Qwen-1.5B，训练了一个专注于推理能力的模型 —— Nemotron-Research-Reasoning-Qwen-1.5B，在数学/编程/逻辑题/STEM推理/任务指令理解等多个领域展现出显著提升。\n\n更关键的是：\n我们发现 RL 模型能解出 base model 无论怎么 sampling 都完全答不出的题，甚至做到 pass rate 100%。这不是随机波动，而是新能力的诞生。\n我们还用 Creativity Index 量化了“新解法”的创意程度，发现训练越久，模型“跳出预训练语料”的能力越强，推理路径越来越有创造性。\n\n此外，强化学习的效果与基础模型的初始表现呈负相关关系：初始 pass@k 较低的任务，在推理边界扩展方面的提升最大；而在诸如数学和代码等高表现领域（其创造力指数较低），推理边界的扩展则较为有限。对于改善幅度较小的任务（即图中显示为“Diminished Area”的部分），基础模型通常具有较低的创造力指数，这表明这些任务可能已在预训练数据中得到了充分覆盖，因此在推理边界上的扩展潜力有限。\n\n我们希望这项工作为“如何通过RL真正提升大模型的推理能力”提供一个新的思考方向，欢迎大家交流！\n","id":"https://king-key.github.io/Blog/2025/06-03/","title":"如何通过RL真正提升大模型的推理能力？NVIDIA提出长期强化学习训练框架ProRL"},"https://king-key.github.io/Blog/2025/06-29/":{"body":"\n\n每次开启需要命令太麻烦，直接可以用mac的自动化操作解决，参考：https://wangguo.site/Blog/2023/Q3/macwei-xin-shuang-kai/\n微信每次更新后，需要先执行\n\n然后重新执行上面的四行代码～～\n","id":"https://king-key.github.io/Blog/2025/06-29/","title":"四行命令解决微信4.0版本双开问题"},"https://king-key.github.io/Blog/2025/07-24/":{"body":"安装streamlit、PyMuPDF\n\n代码\n\n效果\n\n还可以一键下载图片.zip\n\n","id":"https://king-key.github.io/Blog/2025/07-24/","title":"基于streamlit将pdf转换为png"},"https://king-key.github.io/Blog/2025/07-29/":{"body":"\n阅读链接：https://www.sohu.com/a/83171459_422230\n\n种子用户的获取：亲朋好友+裂变（精英圈）- 解决需求：想要联系潜在的投资人和认识大牛\n\n需求放大：联系和资源获取；同时人员变动造成人员信息的变动\n病毒传播：邮件\n新人注册，可以导入通讯录，通过邮件方式邀请用户进行注册使用\n\n双重病毒传播循环“Reconnect Flow”\n以老带新：老人上传通信录；以新促老：新人认证关系\n\n搜索引擎开放\n\n\nhttps://36kr.com/p/2199386703759236\n\n\n\n\n\n单纯的从社交的属性上，现在似乎并没有市场。国内微信是大头，海外linkedin。但是为什么像slack，discard还是可以做呢？因为社交不只是和认识的人交流，更多的是需要向外扩展认识更多不认识的人～\n所以推荐机制很重要。linkedin会议学校、公司这些共同的属性推荐；微信完全隐私；X会根据二度人脉关系推荐。\n而dinq的社交需求细分场景应该是：合作。重点在合作，而不是社交，合作是需求，社交不是！\n\nPM愿意和工程师沟通合作，一起创业\n大模型愿意和强化学习合作，打通技术分界线\n创业者愿意和VC合作，因为可以融资\n\n","id":"https://king-key.github.io/Blog/2025/07-29/","title":"关于LinkedIn的发家史"},"https://king-key.github.io/Blog/2025/11-12/":{"body":"\n","id":"https://king-key.github.io/Blog/2025/11-12/","title":"两行代码瘦身Mac系统"},"https://king-key.github.io/Blog/2025/12-08/":{"body":"\n前提，已经安装了obs，以及微信视频号直播助手（在Windows版微信中才有）、小红书直播助手、Bilibili的直播姬、抖音的直播伴侣\n\n核心是通过obs搭建直播间，然后推流到其他平台。obs直播间画面更改，其他平台会同步更改。\nobs配置直播间\n\n\n添加背景图片。obs中，新建场景，在场景中添加图片作为背景。背景图片可以根据需要调整大小，直至刚好全屏\n\n\n\n\n\n添加画面。在源中新加显示器采集，采集方式默认自动，显示器选择需要共享的显示器。将显示器调整大小后放在画面中\n\n\n\n\n\n\n\n裁剪PPT画面。在显示器采集源上，右键选择滤镜 - 点击 + ，裁剪/填充 - 名称默认即可 - 输入裁剪的范围（参考：左：0；右：320；上下：90，实际以最终显示的效果为准）\n\n\n\n\n裁剪摄像头画面。和裁剪PPT画面一样，裁剪范围为（左：1600，右：0；上下：360）这个大小裁剪的双人出镜画面，需要保证腾讯会议端：有双人开了摄像头，且隐藏未开摄像头的用户\n\n\n\n最终，调整好大小PPT画面和摄像头的画面大小后，放到合适的位置即可\n\n\n\nobs画面推到第三方直播间中，以视频号为例\n\n启动obs虚拟摄像头\n\n\n\n在视频号直播助手中的源，添加摄像头 - 摄像头选择：obs  - 点击确认 - 将画面调整到窗口大小即可\n\n\n\n\n","id":"https://king-key.github.io/Blog/2025/12-08/","title":"obs+视频号/小红书/Bilibili/抖音直播，加背景图"},"https://king-key.github.io/Essay/":{"body":"","id":"https://king-key.github.io/Essay/","title":"index"},"https://king-key.github.io/Essay/2022/":{"body":"","id":"https://king-key.github.io/Essay/2022/","title":"index"},"https://king-key.github.io/Essay/2022/ling-yun-xiu-jing-cang-lan/":{"body":"夜登神岳人无力，伴行五人谱成歌。\n青山秀水涤尘心，云卷成舒醉惊鸿。\n","id":"https://king-key.github.io/Essay/2022/ling-yun-xiu-jing-cang-lan/","title":"凌云岫 敬沧澜"},"https://king-key.github.io/Essay/2022/ling-yun-xiu-zhi-bi-ye/":{"body":"倚山听风雨，淡看江湖路。\n醉酒论古今，笑弄人间情。\n真情本无价，只因有缘人。\n兄弟长无言，但求莫相忘。\n","id":"https://king-key.github.io/Essay/2022/ling-yun-xiu-zhi-bi-ye/","title":"凌云岫 致毕业"},"https://king-key.github.io/Essay/2022/sui-feng-liu-gui/":{"body":"游子归故里，不识旧人路。\n堂前走飞雁，远方看满月。\n","id":"https://king-key.github.io/Essay/2022/sui-feng-liu-gui/","title":"随风流 归"},"https://king-key.github.io/Essay/2022/sui-feng-liu-hong-dou/":{"body":"梦里长安红豆开，远赴惊鸿意踌躇。\n不似三钱二两酒，杯酒换盏自逍遥。\n一夜妩梦好难得，却道何时可成家。\n难言不语问是谁，霓裳羽衣鸣翠鸾。\n","id":"https://king-key.github.io/Essay/2022/sui-feng-liu-hong-dou/","title":"随风流 红豆"},"https://king-key.github.io/Essay/2022/sui-feng-liu-huan/":{"body":"相遇，虚无缥缈的缘分使然\n相识，擦肩而过的一面之缘\n记忆，埋藏深处\n不经意间的触动\n或许就是一段旅行\n可悲，\n于相遇中擦肩而过\n不识记忆中的她\n然而\n仅有的余念\n是为等待着什么？\n也许\n此为梦与现实的交错\n一切为幻\n一切为实\n在梦中的相识\n于现实中相遇\n于是没有了记忆\n再回头\n梦还是梦\n现实的路还要走\n","id":"https://king-key.github.io/Essay/2022/sui-feng-liu-huan/","title":"随风流 幻"},"https://king-key.github.io/Essay/2022/sui-feng-liu-jiang-hu/":{"body":"少持长剑别故里，誓与齐天荣锦还。\n情仇三许断了谁，一樽清酒惘天下。\n看人间岁月流逝，听风中万物超然。\n一身白衣魂欲归，莫识前路依本心。\n","id":"https://king-key.github.io/Essay/2022/sui-feng-liu-jiang-hu/","title":"随风流 江湖"},"https://king-key.github.io/Essay/2022/sui-feng-liu-kong-ji/":{"body":"十月的家乡，\n比我想象中要冷很多。\n当我走在这片土地时，\n我的内心毫无波澜。\n其实，\n我并不知道这次选择的理由，\n或许是潜意识的选择，\n又或者是鸿燕归巢的依赖。\n看到了夜晚回来的母亲，\n年过半百的她，更加的憔悴，\n脸上的皱纹多了，也瘦了。\n奶奶还在，身体还算硬朗，\n只是还是不停的咳嗽。\n父亲也老了，\n头上的白发更多了，\n走路还是高低不平。\n看着熟悉的家，熟悉的人，\n我最亲的人，\n我的心在不安中感到平静。\n从来不曾后悔，\n背井离乡也好，短暂的归乡也好，\n选择，就会有所承受。\n在这短暂的时间里，\n我放纵自己，不去理会各种问题，\n正在面临的，将要面临的。\n认真的聆听内心深处的思想，\n让自己空寂。\n时间是不变的，\n这些都需要我去弥补回来，\n虽然会很麻烦，\n但是，这样的选择是有必要的!\n离别，是我生命中的主旋律，\n不曾后悔，\n不曾怨言，\n只有义无反顾的前行……\n","id":"https://king-key.github.io/Essay/2022/sui-feng-liu-kong-ji/","title":"随风流 空寂"},"https://king-key.github.io/Essay/2022/sui-feng-liu-ming-yin/":{"body":"一曲长歌不愿醒，仙鹤白鹿入梦来。\n清音袅袅尽留人，伴我青衫泪垂帘。\n","id":"https://king-key.github.io/Essay/2022/sui-feng-liu-ming-yin/","title":"随风流 明音"},"https://king-key.github.io/Essay/2022/sui-feng-liu-qiu-han/":{"body":"自九月起，北京的雨就一直在下\n虽然不大，但一阵雨之后天总会寒了些\n不知道从什么时候开始\n我已经慢慢的适应了这里的一切\n国庆假期来临\n因为没有买上火车票\n所以选择了没有坐过的飞机\n虽然并没有什么太大的感觉\n从高空往下看\n人间风景不一样\n灯火千万人家\n是多少人的理想与现实\n航行千里，归根故里\n纵使异乡漂泊\n到家得那一刻\n母亲一碗小米粥足以\n山河故里\n庆幸安之\n","id":"https://king-key.github.io/Essay/2022/sui-feng-liu-qiu-han/","title":"随风流 秋寒"},"https://king-key.github.io/Essay/2022/sui-feng-liu-wen-xin-gui-tu/":{"body":"君问归期何时归，他乡异客谈风声。\n扪心自问安身处，我思之处便是吾。\n","id":"https://king-key.github.io/Essay/2022/sui-feng-liu-wen-xin-gui-tu/","title":"随风流 问心归途"},"https://king-key.github.io/Essay/2022/sui-feng-liu-wu/":{"body":"人生这一路，可长可短。\n曾经有人和我说：\n“树欲静而风不止，子欲养而亲不待”\n我们追求的这一生，也似乎都想尽善尽美\n而且路即使再多，我们的选择似乎也只能是一个\n世间的道理真多、很大\n然而生活却很小\n何必要想那么多大道理\n在意别人的看法\n做纯粹的自己可好？\n... ...\n","id":"https://king-key.github.io/Essay/2022/sui-feng-liu-wu/","title":"随风流-悟"},"https://king-key.github.io/Essay/2022/sui-feng-liu-xi-guan-liao-xi-guan/":{"body":"也没有什么习惯是改不了的\n当你说“不可能”的时候\n我只能回复“谢谢”\n什么是遗憾\n什么是错过\n两个人明明相爱却错过，便是遗憾\n两个人的遗憾便是错过\n而我，有了答案\n自然没有错过\n也不会留有遗憾\n很想知道为什么\n但最后的尊严告诉我：不可以\n原本已经习惯了你的存在\n可现在又要习惯你的离去\n我习惯了又习惯\n习惯的背后是大悲与大喜\n恰巧我只遇到了大悲\n","id":"https://king-key.github.io/Essay/2022/sui-feng-liu-xi-guan-liao-xi-guan/","title":"随风流 习惯了习惯"},"https://king-key.github.io/Essay/2022/sui-feng-liu-xiang-si-xu/":{"body":"笑看云上月，醉饮手中酒。\n遥思归故里，孤影我独行。\n","id":"https://king-key.github.io/Essay/2022/sui-feng-liu-xiang-si-xu/","title":"随风流 相思序"},"https://king-key.github.io/Essay/2022/sui-feng-liu-xiang-si-yin/":{"body":"一曲相思引，两杯肝肠断。\n三人缺明月，四海我独行。\n","id":"https://king-key.github.io/Essay/2022/sui-feng-liu-xiang-si-yin/","title":"随风流 相思引"},"https://king-key.github.io/Essay/2022/sui-feng-liu-zhi-jing-cang-lan/":{"body":"郁结于心难自弃，赖有青山豁我怀。\n三杯过往昨日流，莫渡沧澜翻云海。\n","id":"https://king-key.github.io/Essay/2022/sui-feng-liu-zhi-jing-cang-lan/","title":"随风流 致敬沧澜"},"https://king-key.github.io/Essay/2022/time/":{"body":"\n\t\n\t\t\n​\t\n\n\n\n\n2025年10月1日：我们结婚了🎎\n\n\n2025年5月2日：我们订婚了💍\n\n\n2024年6月18日：今天，我们坐上了去我家的飞机（带着你的父母）\n\n\n2024年5月27日：我们买了自己的车：比亚迪·唐-DMI 荣耀尊荣版\n\n\n2024年5月1日：我带父母去了你家，我们的父母见面了！\n\n\n2023年10月1日：我们第一次去见了父母！\n\n\n2022年1月19日：我们在一起啦\n\n\n2021年7月5日：茫茫人海中，我终于和你相遇！\n\n\n","id":"https://king-key.github.io/Essay/2022/time/","title":"💑 我们在一起了"},"https://king-key.github.io/Essay/2022/xian-nan-shan/":{"body":"不以心守净土，何以跋涉疆土。\n难有自立之身，不有成家之人。\n","id":"https://king-key.github.io/Essay/2022/xian-nan-shan/","title":"羡南山"},"https://king-key.github.io/Essay/2023/":{"body":"","id":"https://king-key.github.io/Essay/2023/","title":"index"},"https://king-key.github.io/Essay/2023/ji-yi-ci-hui-jia-2023nian-10yue-1ri/":{"body":"\n越长大，回家的次数越少～\n\n1、不知从何时起，回家的频率越来越低\n小的时候，我们总想着远离家乡，去看看外面的世界。\n可长大之后才发现，原来，家乡才是最美的风景。\n高考之后，志愿填报了外省。年轻的自己总觉得这样就能离开这个从未踏出过的村庄，才能脱离父母的约束。\n所以，上大学的时候，是不想回家。\n每到暑假与寒假回家的时候。那时候离家太远，也为了省钱，每一次回家也只买最普通的硬座，一坐就是12个小时。\n开始工作后，工作城市比学校离家更远，但却更想回家。\n但每一年也只有五一、十一和春节，可以有相对充裕的时间回家。\n高铁也好，飞机也好，只要能回家，我都会回去！\n但是呢，从大学到现在，回家的次数真的屈指可数～\n2、越长大，越想念家\n年少的叛逆，是我们现在的思念之源。\n不知道从什么时候开始，越来越想念父母。\n总想寻找时间，回到那曾经想要逃离的村庄，陪在父母身旁，哪怕不说一句话。\n不知道从什么时候开始，越来越心疼父母。\n每一次看到父母那新增的皱纹与白发，都让人心颤，千言万语却难以表达～\n“子欲养而亲不待”\n这是现在最害怕的事情～\n","id":"https://king-key.github.io/Essay/2023/ji-yi-ci-hui-jia-2023nian-10yue-1ri/","title":"记一次回家（2023年10月1日）"},"https://king-key.github.io/Essay/2023/ji-yi-xie-nian-xiang/":{"body":"\n人生可长可短，然而生而为人，总是悲喜同在，我们自当感触\n\n\n人心似猿，想这想那，躁动不断。\n时而慨叹人生多舛，生活苦难，回忆过往，似惊魂入梦。\n不知身前事，难量思索，不知所求何，迷迷惘惘矣。\n1、家与远方\n孔子曰：父母在，不远行，游必有方。\n每次看到父亲那越来越瘦、越来越皱，也越来越黑的身体，让人不禁唏嘘，时光的刻刃似乎总是那么沉重。那跛着的身影、以及那断了两根手指的手，总是压的人难以喘息。\n而年过半百的母亲，她的工作总是比我还多，每次回家也只能见寥寥数眼。即使晚上12点多才能回家，上午9点还要上班，她也总要回家，就为了上班前给我做的那一顿我喜欢吃的饭。\n在远方的我，似乎并没有什么能帮上他们，当他们有需要的时候，也不能马上出现在他们的面前。\n有时候真的很想回到故乡，回到父母的身边，哪怕只是静静地看着他们，我也心中满足。\n2、理想与现实\n常说；没有梦想的人，和咸鱼有什么区别。\n可是，做咸鱼的人不累呀。理想和现实的差距很大～\n当然，这些都是不可能的事情。我们既然身处红尘人世，就必然要面对。所以，所有的事情无外乎是一种选择，一种需要承担的选择，一种无法反悔的选择，仅此而已～\n","id":"https://king-key.github.io/Essay/2023/ji-yi-xie-nian-xiang/","title":"记一些念想"},"https://king-key.github.io/Essay/2023/sui-feng-liu-feng-liu/":{"body":"月陌红尘牵私语，难是长歌酒作陪。\n望尽天涯忘白衣，一饮一剑一风流。\n","id":"https://king-key.github.io/Essay/2023/sui-feng-liu-feng-liu/","title":"随风流 风流"},"https://king-key.github.io/Essay/2023/sui-feng-liu-qiu-dao-wen-xin/":{"body":"三五年，剑与酒，一家人，不是兄弟，却是兄弟，更似兄弟。\n三五日，山与水，一场雨，不是离别，却是离别，更似离别。\n","id":"https://king-key.github.io/Essay/2023/sui-feng-liu-qiu-dao-wen-xin/","title":"随风流 求道问心"},"https://king-key.github.io/Essay/2023/sui-feng-liu-yuan-xing/":{"body":"迢迢千里路，悠悠浪子情。\n皎皎冷月光，戚戚悲凉意。\n","id":"https://king-key.github.io/Essay/2023/sui-feng-liu-yuan-xing/","title":"随风流 远行"},"https://king-key.github.io/Essay/2023/zui-hong-chen-qing-xin/":{"body":"愿予你听风红尘，我自待吹雪满坟。\n但求卿淡抹红妆，君人伴白首难离。\n","id":"https://king-key.github.io/Essay/2023/zui-hong-chen-qing-xin/","title":"醉红尘 倾心"},"https://king-key.github.io/Essay/2024/":{"body":"","id":"https://king-key.github.io/Essay/2024/","title":"index"},"https://king-key.github.io/Essay/2024/2024-01-05/":{"body":"腊雪寒光暖花容，裘衣难抵身憔悴。\n半斤浊酒入心来，难得人生几回来。\n","id":"https://king-key.github.io/Essay/2024/2024-01-05/","title":"一场大雪"},"https://king-key.github.io/Essay/2024/2024-03-07/":{"body":"\n思绪万千，当断则断，不断其乱\n\n1、似迷茫，又清醒。知行易，且行难！理想与现实之间的反复横跳～\n在北京这样的一线城市，我们很难拥有”生活“，每天睁眼想着的是工作，闭眼想着的也是工作。在这里，会有很大的成本和压力，会强迫你努力向前追赶，唯恐自己被公司淘汰，被这个城市淘汰。\n去一个五线城市摆一个“烤肠摊”，每天只需要轻松的出摊3-5个小时，基本上可以有500元的收入。这样的生活很惬意，因为你会有很多的自由时间，工作相对也不会太累，更不会有大城市的那种紧迫感和压力。\n或者开着房车，带着自己的猫，去看看这个世界有多大，看看这人世间的美好。\n然而细想，这样的生活我们又能持续多久呢？一年、三年、十年，还是二十年、一辈子？\n生活本就是艰苦的，虽然会压的人喘不过气，但也会督促你前行～\n“采菊东篱下，悠然见南山”，陶渊明的田园生活或许并非自愿，但那惬意、豁达的态度却是人在低谷时应有的一种心境。\n当然，这一切的一切，归根到底还是一个选择题！什么样的选择，就会有什么样的生活，也将会面临什么样的困难。\n2、与其犹豫不决，不如勇敢一试，结局未知，成功或失败的概率永远只有50%\n内耗，是现在工作不开心的主要原因。人生的很多道理我们都明白，然而却也会有各种各样的顾虑和小心翼翼，因此总是在自我否定，迟迟不敢做出选择，迈出下一步～\n可是，过多的思考或许只会让我们变得犹豫、迟钝。一味的对结果做设想似乎会让人失去判断力，甚至失去兴趣。\n正所谓：尽人事听天命！我只是想尝试一下，哪怕最终的结果是失败，那也是我选择后的结果。\n3、更新于2024-03-18\n最终还是提了离职！\n为什么要提离职？\n1、现有的工作没有了价值，不仅是工作内容本身，还有这个工作对于公司，也看不到任何价值，至少公司没有人在关注这个事。\n2、工作没了前途。没有任何的晋升空间，包括岗位，薪资待遇。在岗位上，美名曰给你一个Leader的角色，但却只是干着更多的活，没有Leader的权利。\n在薪资上，入职时说的是一年两次调薪机会，入职后实际只有一次，而且今年（2024年）截止到现在，还有任何要准备述职的规划。意味着今年的调薪机会可能没有。而且公司的所有待遇，比如五险一金，能按照最低标准绝对是最低标准！\n3、老板性格问题。不喜欢没有准备的变化，也不喜欢各种加班，甚至是不到下班不开会的加班。而且老板各种PUA，自以为是，所有的权利抓在自己手里，所有人的工作推进都需要和他确认，很费时间，主要是老板自己还觉得自己很累，觉得别人做不好，所以反馈的很慢。\n4、还有各种不近人情！员同事回家奔丧，还不能放下工作，而且是奔丧完之后马上返工。每天各种看谁早退晚到，然后施压，却从来没有想过加班有加班费的问题。\n当我提了离职之后，身心确实感受到了许久未曾感受到的愉悦、轻快～\n","id":"https://king-key.github.io/Essay/2024/2024-03-07/","title":"一点碎碎念"},"https://king-key.github.io/Essay/2024/2024-09-25/":{"body":"\n难以想象，已经离职6个月，在这期间也没有任何的收入～\n\n3月底离职，到现在9月底，中间已经过去了半年之久～\n在此期间：\n\n\n和朋友们听着许嵩的蓝莲花，去青海旅游，翻过了祁连雪山，在北京最热的时候看到了大雪埋山，来了一场说走就走的旅行～\n\n\n和女朋友安排双方父母见面游玩，去了天安门，去了秦皇岛，也去了壶口瀑布、秦始皇兵马俑、大唐不夜城...\n\n\n买了属于我们的第一辆车（比亚迪 唐DMI），并且开启了我们第一次的自驾旅行：张北草原天路\n\n\n...\n\n\nPs：这也是拿到驾照后第一次这么长、这么密集的驾车，这不～马上就要换驾照了\n","id":"https://king-key.github.io/Essay/2024/2024-09-25/","title":"离职半年，体验到了生命的自由"},"https://king-key.github.io/Essay/2024/2024-12-31/":{"body":"\n变化太多，事情太多～\n\n\n4月份离职，离开了干了快五年的公司，这也是毕业后的第一份工作。同时开始重点运营🔥“青稞社区”。\n4月底和女朋友，以及朋友们一起去青海旅游，翻过了凤凰传奇口中的祁连山⛰️，也是体验到了在夏天玩雪❄️的反差。\n五一期间，父母自驾12个小时（顺便带父母去了天安门）去了女朋友家。我和女朋友也带着两家人去了秦皇岛游玩，也是让我这个北方旱鸭子第一次体验到了海上🌊游艇的乐趣。\n五月底，我们提了第一辆车🚗：比亚迪唐·DMI。\n6月初高考结束，安排女朋友的父母去了我家，顺便也去了大唐不夜城、秦始皇兵马俑以及壶口瀑布玩了玩。\n6月底，开着我们自己的车，自家去了张家口的草原天路。（第一次长时间开车，还是略微有点紧张的）。\n9月初，比较偶然的机会，开始面试蚂蚁🐜。\n10月国庆节，自驾12个小时从北京回老家（长途车真不好开，尤其还是晚上）。中间也去参加了我弟（标准的00后啊）的婚礼。\n11月中旬，入职蚂蚁技术研究院（在此，也要感谢大佬的推荐）。开始一段新的工作旅程～\n\n期间也做了很多事情：\n\n一直运营青稞社区（搭建静态网站，做运营、内容策划等等），甚至尝试变现，可惜效果甚至。\n尝试做了个人小红书，好几个帖子的阅读量都上万了，甚至还有一个已经 27w 阅读量，但很可惜这个帖子最终因被举报下架（到现在还是莫名其妙）。\n当然，公众号（青稞AI）的文章也出现了好几篇上万阅读量的文章（略感欣慰）。总阅读量在30w。\n认识了很多新朋友，也和一些大佬线上、线下的进行交流，收获良多。\n\n2025，又是新的一年，也期待认识更多有趣的人、做更多有意思的事情、赚更多的💰！\n","id":"https://king-key.github.io/Essay/2024/2024-12-31/","title":"记录一下这“变换莫测”的2024"},"https://king-key.github.io/Essay/2024/birthday/":{"body":"\n\t\n\t\t\n\t\n\n","id":"https://king-key.github.io/Essay/2024/birthday/","title":"人间凑数的日子"},"https://king-key.github.io/Essay/2025/":{"body":"","id":"https://king-key.github.io/Essay/2025/","title":"index"},"https://king-key.github.io/Essay/2025/07-25/":{"body":"今天和老高聊到了关于Dinq的问题，让我突然有了一点感触，期间也想起了和另一位做算法朋友的交流，以及和智谱一位做投资朋友的聊天，不禁在想：\nAI/AGI时代真的来了吗？\n现在的我们，天天都泡在了AI的信息茧房里，关注的全是AI的人和事，周边的人也都在AI这个圈子里。但是我们回过头来想想，AI这个圈子到底有多大？\n从目前的理解来看，想要了解AI，乃至接触、从事AI这个行业，基本都是需要上过大学的人，但是这个数据能有多大，在中国，乃至全球又能占到多大的比例\n\n这是来自ChatGPT的一个数据，也就说中国有大学教育经历的有20%（比预想的要高），全球不到（7%）。\n我们假设这些人都在AI圈子里，或者说都有机会、都有能力认知去了解AI，那剩下的人呢？他们很大程度上是无法理解AI，以及使用AI，AI似乎还是“少数人”的游戏。\n现在的AI研究的是怎么刷分、怎么去解决高认知人群所能解决、所需要结局的问题，比如设计、广告、程序.......用最牛逼的人去解决最牛逼的人的问题，这个逻辑很难说“AI时代的来临“吧！！！\n什么时候，AI能让三四线城市的普通人用到，对他们产生价值的时候，或许才是AI时代真正的来临。\n这里就不得不提到之前的算法朋友，他的一个愿景就是将AI应用于中国制造业（很宏伟的想法，震撼）。\n在这个时代，我又想做什么\n我不是AI研究人员，甚至连技术人员也算不上，更没有研究生甚至博士的学历，不懂AI又为什么还要处在AI这个行业呢？\n很现实的想法当然是赚钱，但宏伟的想法自然是“以青稞之火，起燎原之势”\n当时在和智谱的朋友聊的时候（其实是面试，但是最后没有面上），就有聊到相关的事情。\n我为什么一定要加入到AI这个领域呢？或者应该说怎们理解是否在AI这个领域？难道只有做AI技术研究的才算？在AI这股浪潮中，我用自己的力量去让更多的人了解、学习AI，难道不算价值体现吗？我构建青稞社区这个平台，让更多人看到AI前沿成果，难道这不算价值吗？\n参与不代表一定要做技术～～～每个人都有分工，每个人都可以按照自己的能力、兴趣，去选择这个链条上的某一环完成。我不会做超出自己能力之外的事情，前提是有足够的试错成本。认清自己的能力，定位自己想做的事情，然后朝着这个目标努力就好～～\n选择不一样，但是都有价值～\n","id":"https://king-key.github.io/Essay/2025/07-25/","title":"深夜里的一些胡思乱想"},"https://king-key.github.io/about/":{"body":"\n\n\n\n \n\n\n胡思乱想🤔💡 ｜ 在线简历📄\nHi there 👋\n🤔 我是Wang Guo(王過，字改之),一个INTJ。\n工作经历\n\n2019.07 - 2024.03：🦍智猩猩/智东西公开课 资深课程经理\n2024.04 - 现在：🔥青稞社区 主理人\n2024.11 - 2025.04：🐜蚂蚁技术研究院 资深技术运营\n2025.04 - now：\n\n联系方式\n📫 Email: wangguo113@gmail.com\n💬 WeChat: 15633578977\n我有一只猫🐈\n因为是800块钱买的，所以我给它取名：八仔。它是一只折耳猫、英短蓝白，已经陪伴了我四年了！\n\n","id":"https://king-key.github.io/about/","title":"About"},"https://king-key.github.io/about/jianli/":{"body":"MBTI性格类型-INTJ ｜ 📮邮箱：guo__wang__113@163.com ｜主页：wangguo.site\n\n🙋‍♂️ 个人简介\n现为青稞社区主理人、同时参与DINQ的设计运营；2019年7月入职北京智一科技，主要负责AI领域的课程产品研发，关注LLM&AIGC、计算机视觉、机器人算法、自动驾驶算法等。后加入蚂蚁技术研究院，负责技术PR运营。喜欢开源，追踪最新的AI技术。\n🧱 工作经历\n\n\n2024.11 - 2025.04 蚂蚁技术研究院 资深技术运营\n\n\n2019.07 - 2024.03  北京智一科技 资深课程经理/智东西公开课教研组Leader\n\n\n\n🔥项目经历\n2024.04 - 至今：青稞社区 主理/运营（定位于青年 AI 研究员 Idea 加油站，AI 开发者的新能源充电桩，是国内外知名的高质量 AI 技术交流社区）\n\n社区公众号（青稞AI）关注达到19000+、私域用户5000+、视频号关注2400+、小红书用户1000+\n组织青稞Talk（线上）70+期，青稞Tea（线下）2期；单场活动报名150+\n\n2022.01 - 2024.03：ToC产品·新青年讲座 负责人\n\n关注前沿技术发展趋势，策划了AI新青年讲座、自动驾驶新青年讲座等12档讲座栏目。\n其中AI新青年讲座125期，受众15000+、点击量200000+；自动驾驶新青年讲座14期，受众7000+、点击量55000+。\n连接博士生/助理教授等青年讲者500+名，覆盖海内外高校如MIT、CMU、TUM、NUS、斯坦福、清华、北大等100+余所。\n\n2022.11 - 2023.06：大型语言模型技术公开课 负责人\n\n调研 LLM/VLM/MLLM 产品 ，以及大模型产业和相关技术，制作大模型产业图谱和技术图谱，在ChatGPT爆火前就对其进行立项。\n策划大型语言模型技术公开课方案，联系并沟通邀请企业大模型负责人。\n本课程共策划8期内容，受众1700+，点击量15000+。\n\n2023.02 - 2023.03：GTIC 2023中国AIGC创新峰会 委员会成员\n\n参与议程与议题设计。\n寻找并沟通联系大模型/AIGC领域创新企业，邀请参与峰会主题分享。包括北京智谱华章科技、潞晨科技、aiXcoder、计算美学（Nolibox）等。\n\n\n🤺 个人技能\n\n了解AI、大模型、机器人等前沿算法和技术应用。\n熟悉开源社区，熟悉Linux系统的操作使用，可以跑AI模型。\n优秀的团队管理、跨部门沟通及协同能力。\n结果导向，强执行力和自我驱动能力、理解能力和学习能力。\nXMind、PPT、Keynote等。\n\n${update:2025.09}$\n","id":"https://king-key.github.io/about/jianli/","title":"王过·简历"},"https://king-key.github.io/about/timeline/":{"body":"","id":"https://king-key.github.io/about/timeline/","title":"Idea's"},"https://king-key.github.io/archive/":{"body":"","id":"https://king-key.github.io/archive/","title":"archive"},"https://king-key.github.io/link/":{"body":"Chat with LLM in Web\n\n   \n      \n         \n         \t\n            \n               \n               \n                  \n                     通义\n                  \n                  阿里通义实验室\n               \n            \n            \n            \n               \n               \n                  \n                     Kimi\n                  \n                  月之暗面\n               \n            \n            \n            \n               \n               \n                  \n                     智谱清言\n                  \n                  智谱AI\n               \n            \n            \n            \n               \n               \n                  \n                     ChatGPT\n                  \n                  OpenAI（需要科学上网）\n               \n            \n         \n      \n   \n\n>>大佬的世界\n\nYifan Liu,🕯️🕯️🕯️\nYufeng Zheng,PhD @ ETH Zürich\nYafei (Jeffrey) Hu,PhD @ CMU\nShiwei Liu,postdoctoral @ UT Austin\nChen Wang,Assistant Professor @ New York at Buffalo\nRunsheng Xu,Research Scientist @ Waymo\nXiang Li (李翔),Associate Professor @ Nankai University\nChenyang Lei,Assistant Professor @ CAIR, CAS\nCongyue Deng,  Ph.D. Student @ Stanford University\nXin Kong (孔 昕),  PhD study @ Dyson Robotics Lab at Imperial College London\nRuoshi Liu 刘若石,  PhD student @ Columbia University\nZiqi Huang,  PhD Student @ MMLab@NTU\nZhenyu Chen,  PhD candidate @ TUM Visual Computing\nHuan Wang,  Ph.D. candidate @ SMILE Lab,  Northeastern University\nZhengzhe Liu,  PhD candidate @ The Chinese University of Hong Kong\nYiyang Zhao,  Ph.D candidate @ Worcester Polytechnic\nYinyu Nie,  post-doctoral researcher @ TUM Visual Computing Group\nWuwei Lan,  Applied Scientist @ AWS AI Labs\nHaotong Qin,  Ph.D. canditate\nHaotian Tang,  Ph.D @ HAN LAB of MIT EECS\nZhijian Liu,  PhD student @ MIT\nJi Lin,  Ph.D @ MIT EECS\nHan Cai,  Ph.D @ MIT\nLigeng Zhu,  Ph.D @ MIT\nHanrui Wang,  Ph.D @ MIT\nJiapeng Tang,  Ph.D @ TUM\nJinyang Guo,  Assistant Professor @ Beihang University\nHanjiang Hu , Ph.D candidate at Safe AI Lab,  Carnegie Mellon University.\nChangqian Yu , Research Expert at Meituan,  focusing on artificial intelligence and machine learning.\nLixin YANG , Ph.D candidate at Shanghai Jiao Tong University (SJTU),  specializing in computer vision and deep learning.\nZhen Dong , Ph.D candidate at BAIR,  UC Berkeley.\nWei Niu , Assistant Professor at University of Georgia,  with expertise in computer science and artificial intelligence.\nJindong Gu , Postdoctoral Researcher at University of Oxford\nJian Weng , Ph.D candidate at UCLA,  Assistant Professor at KAUST\nZhiyu Huang , Ph.D candidate at NTU,\nJiemin Fang , Ph.D candidate at Huazhong University of Science and Technology,  researching in the field of artificial intelligence.\nJia-Wang Bian , Postdoctoral Researcher at Oxford\nYuexin Ma , Assistant Professor at ShanghaiTech University\nXinyu Huang , Ph.D candidate at Fudan University\nRuihao Gong , Senior Research Manager of Artificial Intelligence at SenseTime Research\nJianhao Jiao , Senior Research Fellow @ University College London (UCL)\nChenyang Qi , Ph.D candidate at HKUST\nHaoyu He , Ph.D candidate at Monash University\nShang Yuzhang , Ph.D candidate at IIT,\nZining Zhu , Assistant Professor at Stevens Institute of Technology\nHongming Zhang , Senior Research Scientist at Tencent AI Lab\nQingkai Min , Ph.D candidate at Westlake University\nFreda Shi , Assistant Professor at University of Waterloo\nYixiao Ge , Senior Researcher at Tencent ARC Lab and Tencent AI Lab,\nShaoshuai Shi , Postdoctoral Researcher at Department of Computer Vision and Machine Learning Max Planck Institute for Informatics\nXiaohang Zhan , Researcher in AI Lab,  Tencent\nFangwei Zhong , Postdoctoral Researcher at Peking University\nXianzhong Ding , Ph.D candidate at University of California,  Merced (UCM)\nSitao Luan,  Ph.D candidate at McGill University\nMeng Qu,  Ph.D @ Mila - Quebec AI Institute\nHUAXIU YAO,  Assistant Professor @ University of North Carolina at Chapel Hill\nYuchen Lu,  Ph.D @ the Mila lab of University of Montreal\n游宇宁,  Ph.D @ Texas A&M University\nJingfeng Zhang,  Tenured Assistant Professor @ University of Auckland\nYu Liu,  General Manager & Senior Director,  SenseTime / PI,  Shanghai AI Lab\nKeze Wang,  Associate Professor @ Sun Yat-sen University\nLI,  Wen,  Professor @ UESTC\nYujun Shen,  senior research scientist @ Ant Research\nYantao SHEN,  Applied Scientist @ Amazon Web Services (AWS)\nYaoyu Hu,\n刘禹良,  华中科技大学人工智能与自动化学院, 研究员, CSIG优博\nHao Chen,\nFei Pan,\nHao Luo,\nXi Shen,  Chief Scientist @ Intellindust\nGuoguang Du,\nZhihao Xia,  research scientist @ Adobe\nHang Zhou,  senior researcher @ baidu\nRen Yang,  Senior Researcher @ SenseTime\nZhaowei Zhu,  PhD\nYue Zhao,  Assistant Professor @ University of Southern California\nTianfei Zhou,  postdoctoral @ ETH Zurich\nShuting He,  Ph.D @ Zhejiang University\nChen Zhao,  PhD @ EPFL\nWenhan Yang,\nZhiqi Li,  PhD @ Nanjing University\nHao-Shu FANG,  PhD @ SJTU\nZiji SHI,  PhD @ NTU\nFuzhao Xue,  PhD @ HPC-AI、NUS\nYanwei Li,  PhD @ CUHK\nZhengzhong Tu,\n施宇钧,  PhD @ NUS\nTianlong Chen,  Incoming Assistant Professor\nDaiheng Gao,\nWenhai Wang,\nHaoxuan You,\nZihui (Sherry) Xue,  PhD @ UTAustin\nMingdong Wu,  PhD @ Peking University (PKU)\nAo Zhang,  PhD @ NUS\nShengqiong Wu,  PhD @ NUS\n Ruizhi Shao (邵睿智),  Phd @ Tsinghua\n Shangchen Zhou ,  PhD @ MMLab@NTU\n FANXU MENG（孟繁续）,  Ph.D @ Peking University\nHaotian Liu Ph.D@University of Wisconsin-Madison\nZhu, Deyao 朱德尧,PhD @ KAUST\nXianzhong Ding,PhD @ UCM\nYuchen Lu,PhD @ MILA\n游宇宁 (Yuning You),Ph.D @ Texas A&M University\nJiawang Bian,边佳旺,Postdoctoral Researcher  Torr Vision Group & Active Vision Lab Department of Engineering Science University of Oxford\nYukang (Raikkonen) Cao   操雨康,Ph.D @ HUK\nShiming Chen-陈使明,Postdoctoral Research Fellow\nYukang Chen|陈玉康,Ph.D  @ CUHK\nYifu Ding,PhD @ Beihang University\nHao-Shu FANG,Postdoc researcher @ MIT CSAIL\nShanyan Guan (官善琰)\nZhiyu Huang,visiting student researcher @  UC Berkeley\nZiyuan Huang (黄子渊),Ph.D @ NUS\nBowen Li,PhD @ CMU\nKejie Li,postdoctoral researcher @ the University of Oxford\nYuheng Li,Ph.D @ University of Wisconsin-Madison\nFeng (Jeff) Liang, 梁丰,PhD @ UT Austin\nJing Liu,Ph.D @  Monash University\nJun Lv,Ph.D @  Shanghai Jiao Tong University (SJTU)\nLi MA 马力,Ph.D @ HKUST，will join Scanline VFX Studio as a Research Scientist in 2024.\nCan Qin,Salesforce AI Research\nHanqing Wang\nGuangming Wang,Research Associate @ Cambridge\nTengfei WANG,researcher @ Shanghai AI Lab\nQianyi Wu |吴潜溢,PhD @ Monash University\nBinhui Xie,Ph.D. student in the School of Computer Science and Technology at Beijing Institute of Technology\nTianbao Xie，Ph.D @ the University of Hong Kong\n[Dejia Xu],Ph.D student at VITA Group, The University of Texas at Austin\nShuai Yang (杨 帅),Research Assistant Professor@ NYU\nManyuan Zhang\nQihang Zhang\nShibo (Edward) Zhao 赵世博,PhD @ CMU\nYe Zhu|竺 烨,postdoc research associate in the Computer Science Department at Princeton University\nWuyang Chen,Assistant Professor in the School of Computing Science at Simon Fraser University (Fall 2024)\nHOU Yuenan (侯跃南),Researcher of Shanghai AI Laboratory\nLinchao Zhu (朱霖潮) ,Assistant Professor @  Zhejiang University\nBohan Zhuang,Assistant Professor @ Monash University\nJing Liu,PhD @ Monash University\nJi Hou (侯骥),a Research Scientist @ Meta GenAI\nGuilin Liu,Research Scientist @ NVIDIA\nming-yu liu 劉洺堉,Vice President of Research at NVIDIA and a Fellow of IEEE.\nZhiding Yu  (禹之鼎),Senior Research Scientist @ NVIDIA\nHongxin Zhang (张洪鑫),Ph.D @ UMass Amherst\nYining Hong,PhD @ UCLA\nJinjie Ni,PhD @ NUS\nShizhe Diao,PhD @ 港科大\nJinjie Ni,Research Fellow @ NUS\nYuming Jiang 姜瑜铭,Ph.D MMLab@NTU\nChi Zhang,scientist @ Tencent (Shanghai)\nHao Shao   邵昊,PhD @ Multimedia Laboratory in the Chinese University of Hong Kong\nYing Sheng(盛颖),Ph.D @ Stanford University\nJiaming Tang (唐嘉铭)/,Ph.D @ MIT EECS\nShuai Yang,Ph.D. student Artificial Intelligence Thrust, Information Hub The Hong Kong Univerisity of Science and Technology (Guangzhou)\nShiyi Cao 「曹诗怡」,Ph.D @ UC Berkeley \nChaoyou Fu,Assistant Professor @ Nanjing University\nGanqu Cui,a researcher in Shanghai AI Laboratory\n刘子纬, Associate Professor @ NTU\nZuxin Liu,Research Scientist at Salesforce AI Research\n\n>>联系企业\n\n 潞晨科技\n 智谱AI\n 一流科技\n 联汇科技\n\n","id":"https://king-key.github.io/link/","title":"Link"}},"docInfo":{"https://king-key.github.io/":{"body":0,"title":1},"https://king-key.github.io/Blog/":{"body":0,"title":1},"https://king-key.github.io/Blog/2023/":{"body":0,"title":1},"https://king-key.github.io/Blog/2023/Q1/":{"body":0,"title":1},"https://king-key.github.io/Blog/2023/Q1/cmakebian-yi-build/":{"body":14,"title":1},"https://king-key.github.io/Blog/2023/Q1/darknet-yolo/":{"body":26,"title":1},"https://king-key.github.io/Blog/2023/Q1/darknetkuang-jia-gpubian-yi-an-zhuang/":{"body":42,"title":1},"https://king-key.github.io/Blog/2023/Q1/ffmpegjiang-mp4zhuan-wei-gif/":{"body":3,"title":1},"https://king-key.github.io/Blog/2023/Q1/googlecolab-stablediffusion/":{"body":76,"title":2},"https://king-key.github.io/Blog/2023/Q1/hao-wan-de-aikai-yuan-xiang-mu/":{"body":23,"title":1},"https://king-key.github.io/Blog/2023/Q1/hexo-galleryxiang-ce-zhong-pi-liang-tian-jia-zhao-pian/":{"body":4,"title":2},"https://king-key.github.io/Blog/2023/Q1/hexoxiu-gai-yin-yong-lian-jie-de-yang-shi/":{"body":13,"title":1},"https://king-key.github.io/Blog/2023/Q1/itchathuo-qu-qun-liao-yong-hu-de-xin-xi/":{"body":8,"title":1},"https://king-key.github.io/Blog/2023/Q1/itchathuo-qu-wei-xin-hao-you-tou-xiang-bing-jin-xing-ju-xing-pin-jie/":{"body":0,"title":1},"https://king-key.github.io/Blog/2023/Q1/itchathuo-qu-wei-xin-hao-you-zong-ren-shu-yi-ji-nan-nu-bi-li/":{"body":7,"title":1},"https://king-key.github.io/Blog/2023/Q1/linuxzhong-yi-xie-hao-yong-de-ming-ling/":{"body":6,"title":1},"https://king-key.github.io/Blog/2023/Q1/qtan-zhuang-shi-yong/":{"body":4,"title":1},"https://king-key.github.io/Blog/2023/Q1/rosan-zhuang/":{"body":28,"title":1},"https://king-key.github.io/Blog/2023/Q1/streamlit-appchuang-jian/":{"body":13,"title":2},"https://king-key.github.io/Blog/2023/Q1/vscodezhong-chuang-jian-markdownmo-ban/":{"body":5,"title":1},"https://king-key.github.io/Blog/2023/Q2/":{"body":0,"title":1},"https://king-key.github.io/Blog/2023/Q2/butterlfyzhong-ji-ge-hao-yong-de-wai-gua-biao-qian/":{"body":18,"title":2},"https://king-key.github.io/Blog/2023/Q2/chatglm2-6b-int4ben-di-bu-shu/":{"body":56,"title":3},"https://king-key.github.io/Blog/2023/Q2/googlecolab-aidemo/":{"body":23,"title":5},"https://king-key.github.io/Blog/2023/Q2/hexo-butterflyzhong-tian-jia-github-calendargong-xian-tu/":{"body":31,"title":3},"https://king-key.github.io/Blog/2023/Q2/promptzhi-ling/":{"body":24,"title":2},"https://king-key.github.io/Blog/2023/Q2/pythonzhong-yi-xie-hao-yong-de-kuo-zhan-bao/":{"body":161,"title":1},"https://king-key.github.io/Blog/2023/Q2/ru-he-she-ji-prompt/":{"body":24,"title":1},"https://king-key.github.io/Blog/2023/Q2/stable-diffusion-webui-googlecolab/":{"body":35,"title":5},"https://king-key.github.io/Blog/2023/Q2/streamlit-cloudbu-shu-yi-ge-streamlit-app/":{"body":19,"title":3},"https://king-key.github.io/Blog/2023/Q2/zheng-li-yi-xie-kai-yuan-de-llms/":{"body":76,"title":1},"https://king-key.github.io/Blog/2023/Q2/zi-ding-yi-shou-ye-qia-pian-gao-du/":{"body":2,"title":2},"https://king-key.github.io/Blog/2023/Q3/":{"body":0,"title":1},"https://king-key.github.io/Blog/2023/Q3/0821-langchainxue-xi-2/":{"body":17,"title":1},"https://king-key.github.io/Blog/2023/Q3/0824-langchain-agents/":{"body":23,"title":2},"https://king-key.github.io/Blog/2023/Q3/fastapichu-shi-yong/":{"body":33,"title":1},"https://king-key.github.io/Blog/2023/Q3/ffmpegjiang-gifzhuan-wei-mp4/":{"body":3,"title":1},"https://king-key.github.io/Blog/2023/Q3/hexo-butterflytian-jia-hua-ban-piao-luo-bei-jing/":{"body":3,"title":2},"https://king-key.github.io/Blog/2023/Q3/hexo-butterflyzhong-tian-jia-gong-gao-lan/":{"body":5,"title":2},"https://king-key.github.io/Blog/2023/Q3/langchainchu-xue-xi/":{"body":64,"title":1},"https://king-key.github.io/Blog/2023/Q3/macwei-xin-shuang-kai/":{"body":6,"title":1},"https://king-key.github.io/Blog/2023/Q4/":{"body":0,"title":1},"https://king-key.github.io/Blog/2023/Q4/1228-aigc/":{"body":2,"title":2},"https://king-key.github.io/Blog/2023/Q4/ben-di-hua-bu-shu-quivr/":{"body":8,"title":1},"https://king-key.github.io/Blog/2023/Q4/gitde-xie-zuo-shi-yong/":{"body":2,"title":1},"https://king-key.github.io/Blog/2023/Q4/zen-me-xie-ti-shi-ci/":{"body":108,"title":3},"https://king-key.github.io/Blog/2024/":{"body":0,"title":1},"https://king-key.github.io/Blog/2024/Q1/":{"body":0,"title":1},"https://king-key.github.io/Blog/2024/Q1/01-hf-torrent/":{"body":14,"title":1},"https://king-key.github.io/Blog/2024/Q1/2024-02-29/":{"body":8,"title":1},"https://king-key.github.io/Blog/2024/Q1/2024-03-12/":{"body":9,"title":2},"https://king-key.github.io/Blog/2024/Q1/2024-03-17/":{"body":1,"title":1},"https://king-key.github.io/Blog/2024/Q1/fastgpt-chatglm2-wechat/":{"body":60,"title":1},"https://king-key.github.io/Blog/2024/Q1/llmzhong-de-yi-xie-ji-ben-gai-nian/":{"body":59,"title":1},"https://king-key.github.io/Blog/2024/Q2/":{"body":0,"title":1},"https://king-key.github.io/Blog/2024/Q2/2024-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