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<rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:media="http://search.yahoo.com/mrss/" version="2.0"><channel><title>标签：模型 - qingyao的博客</title><link>https://yaoblog.site/tags/mo-xing</link><atom:link href="https://yaoblog.site/tags/mo-xing/feed/tags/mo-xing.xml" rel="self" type="application/rss+xml"/><description>qingyao的博客</description><generator>Halo v2.24.1</generator><language>zh-cn</language><image><url>https://yaoblog.site/upload/logo.avif</url><title>标签：模型 - qingyao的博客</title><link>https://yaoblog.site/tags/mo-xing</link></image><lastBuildDate>Sat, 20 Jun 2026 23:32:30 GMT</lastBuildDate><item><title><![CDATA[Qwen3.6-27B 模型效果实测]]></title><link>https://yaoblog.site/archives/qwen3.6-27bmo-xing-xiao-guo</link><description><![CDATA[<img src="https://yaoblog.site/plugins/feed/assets/telemetry.gif?title=Qwen3.6-27B%20%E6%A8%A1%E5%9E%8B%E6%95%88%E6%9E%9C%E5%AE%9E%E6%B5%8B&amp;url=/archives/qwen3.6-27bmo-xing-xiao-guo" width="1" height="1" alt="" style="opacity:0;">模型部署 模型下载 用魔塔命令下载模型文件，Qwen/Qwen3.6-27B modelscope download --model Qwen/Qwen3.6-27B --local_dir /data/Qwen3.6-27B docker-compose.y]]></description><guid isPermaLink="false">/archives/qwen3.6-27bmo-xing-xiao-guo</guid><dc:creator>Administrator</dc:creator><category>AI</category><pubDate>Mon, 1 Jun 2026 03:59:51 GMT</pubDate></item><item><title><![CDATA[模型的调优]]></title><link>https://yaoblog.site/archives/mo-xing-de-diao-you</link><description><![CDATA[<img src="https://yaoblog.site/plugins/feed/assets/telemetry.gif?title=%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%B0%83%E4%BC%98&amp;url=/archives/mo-xing-de-diao-you" width="1" height="1" alt="" style="opacity:0;">目前的生产场景中，模型调优分为两种，第一种就是公网模型的调优，第二种就是机器学习的模型调优，按照目前的情况来看，我做公网模型的调优的场景会多一点，第二种在生产场景中几乎没有遇到； 公网模型调优： 往往在用 MaxKB 智能体平台中，客户在使用一些公网模型老是发现回答出现幻觉（答非所问、胡编乱造）的场]]></description><guid isPermaLink="false">/archives/mo-xing-de-diao-you</guid><dc:creator>Administrator</dc:creator><category>AI</category><pubDate>Fri, 29 May 2026 03:05:10 GMT</pubDate></item><item><title><![CDATA[混元 HY-MT2 翻译模型]]></title><link>https://yaoblog.site/archives/hun-yuan-hy-mt2fan-yi-mo-xing</link><description><![CDATA[<img src="https://yaoblog.site/plugins/feed/assets/telemetry.gif?title=%E6%B7%B7%E5%85%83%20HY-MT2%20%E7%BF%BB%E8%AF%91%E6%A8%A1%E5%9E%8B&amp;url=/archives/hun-yuan-hy-mt2fan-yi-mo-xing" width="1" height="1" alt="" style="opacity:0;">5月22号，腾子发布并开源了一款专注于支持 33 种语言之间互译的翻译模型。其中，HY-MT2-7B 是在 WMT25 夺冠模型HY-MT-7B 基础上的升级版本，针对解释性翻译和混合语言场景进行了优化，新增了术语干预、上下文翻译和格式化翻译功能。Hy-MT2 在通用翻译、实际业务、专业领域及指令对]]></description><guid isPermaLink="false">/archives/hun-yuan-hy-mt2fan-yi-mo-xing</guid><dc:creator>Administrator</dc:creator><category>AI</category><pubDate>Wed, 27 May 2026 02:08:03 GMT</pubDate></item><item><title><![CDATA[探讨一下不同行业对于模型的选型]]></title><link>https://yaoblog.site/archives/tan-tao-yi-xia-bu-tong-xing-ye-dui-yu-mo-xing-de-xuan-xing</link><description><![CDATA[<img src="https://yaoblog.site/plugins/feed/assets/telemetry.gif?title=%E6%8E%A2%E8%AE%A8%E4%B8%80%E4%B8%8B%E4%B8%8D%E5%90%8C%E8%A1%8C%E4%B8%9A%E5%AF%B9%E4%BA%8E%E6%A8%A1%E5%9E%8B%E7%9A%84%E9%80%89%E5%9E%8B&amp;url=/archives/tan-tao-yi-xia-bu-tong-xing-ye-dui-yu-mo-xing-de-xuan-xing" width="1" height="1" alt="" style="opacity:0;">本章是用来总结一下，AI 迅速发展的这两年，我负责过的项目客户，他们在自己行业中选择的模型和我对这些模型在使用方面的一下看法吧。 教育行业 国内大学中约50个客户左右：（一个客户可能使用的模型系列是多种的） 使用Qwen系列的占65%，使用ChatGPT的有42%，使用方舟的有18%，使用kimi的]]></description><guid isPermaLink="false">/archives/tan-tao-yi-xia-bu-tong-xing-ye-dui-yu-mo-xing-de-xuan-xing</guid><dc:creator>Administrator</dc:creator><category>AI</category><pubDate>Tue, 12 May 2026 07:39:39 GMT</pubDate></item><item><title><![CDATA[gpustack 部署本地视觉模型]]></title><link>https://yaoblog.site/archives/gpustack-bu-shu-ben-di-shi-jue-mo-xing</link><description><![CDATA[<img src="https://yaoblog.site/plugins/feed/assets/telemetry.gif?title=gpustack%20%E9%83%A8%E7%BD%B2%E6%9C%AC%E5%9C%B0%E8%A7%86%E8%A7%89%E6%A8%A1%E5%9E%8B&amp;url=/archives/gpustack-bu-shu-ben-di-shi-jue-mo-xing" width="1" height="1" alt="" style="opacity:0;">下载谷歌仓库的llama.cpp镜像（yusiwen/llama.cpp的镜像它的启动命令有点不一样目前还没试出来） docker pull ghcr.io/ggml-org/llama.cpp:server-cuda12-b7666 在gpustack 中导入下面yaml 说明： -m ：表示模型]]></description><guid isPermaLink="false">/archives/gpustack-bu-shu-ben-di-shi-jue-mo-xing</guid><dc:creator>Administrator</dc:creator><category>AI</category><pubDate>Wed, 29 Apr 2026 02:06:03 GMT</pubDate></item><item><title><![CDATA[模型压测]]></title><link>https://yaoblog.site/archives/mo-xing-ya-ce</link><description><![CDATA[<img src="https://yaoblog.site/plugins/feed/assets/telemetry.gif?title=%E6%A8%A1%E5%9E%8B%E5%8E%8B%E6%B5%8B&amp;url=/archives/mo-xing-ya-ce" width="1" height="1" alt="" style="opacity:0;">因为GB10小盒子的显存和内存是共享的，所以在压测前需要将内存损耗降到最低 sysctl -w vm.drop_caches=3 降低之后需要安装虚拟环境进行隔离环境，以前写了很多了就不赘述了 conda activate vllm 在虚拟环境安装 vllm 依赖 pip install vLLM]]></description><guid isPermaLink="false">/archives/mo-xing-ya-ce</guid><dc:creator>Administrator</dc:creator><category>AI</category><pubDate>Tue, 28 Apr 2026 09:17:25 GMT</pubDate></item><item><title><![CDATA[SGLang推理引擎部署z-image-turbo]]></title><link>https://yaoblog.site/archives/sglangtui-li-yin-qing-bu-shu-z-image-turbo</link><description><![CDATA[<img src="https://yaoblog.site/plugins/feed/assets/telemetry.gif?title=SGLang%E6%8E%A8%E7%90%86%E5%BC%95%E6%93%8E%E9%83%A8%E7%BD%B2z-image-turbo&amp;url=/archives/sglangtui-li-yin-qing-bu-shu-z-image-turbo" width="1" height="1" alt="" style="opacity:0;">下载镜像 我7元花钱买了50G的镜像加速包，轩辕镜像加速包 #登录轩辕的专属域名才能享用加速 docker login -u 账号 -p '密码' docker.xuanyuan.run docker pull docker.xuanyuan.run/lmsysorg/sglang:latest]]></description><guid isPermaLink="false">/archives/sglangtui-li-yin-qing-bu-shu-z-image-turbo</guid><dc:creator>Administrator</dc:creator><category>AI</category><pubDate>Tue, 28 Apr 2026 02:55:29 GMT</pubDate></item><item><title><![CDATA[LoRa模型微调实战]]></title><link>https://yaoblog.site/archives/loramo-xing-wei-diao-shi-zhan</link><description><![CDATA[<img src="https://yaoblog.site/plugins/feed/assets/telemetry.gif?title=LoRa%E6%A8%A1%E5%9E%8B%E5%BE%AE%E8%B0%83%E5%AE%9E%E6%88%98&amp;url=/archives/loramo-xing-wei-diao-shi-zhan" width="1" height="1" alt="" style="opacity:0;">使用 LLama-Factory 进行模型微调 安装 LLama-Factory git clone https://github.com/hiyouga/LLaMA-Factory.git cd LLaMA-Factory pip install -e ".[torch,metrics]" -]]></description><guid isPermaLink="false">/archives/loramo-xing-wei-diao-shi-zhan</guid><dc:creator>Administrator</dc:creator><category>AI</category><pubDate>Tue, 28 Apr 2026 02:52:52 GMT</pubDate></item><item><title><![CDATA[部署comfyUI 跑 Z_images_turbo 图生图]]></title><link>https://yaoblog.site/archives/bu-shu-comfyui-x-z_images_turbo-tu-sheng-tu</link><description><![CDATA[<img src="https://yaoblog.site/plugins/feed/assets/telemetry.gif?title=%E9%83%A8%E7%BD%B2comfyUI%20%E8%B7%91%20Z_images_turbo%20%E5%9B%BE%E7%94%9F%E5%9B%BE&amp;url=/archives/bu-shu-comfyui-x-z_images_turbo-tu-sheng-tu" width="1" height="1" alt="" style="opacity:0;">典型ComfyUI 安装流程 # 1. 创建Conda环境（生成在 /root/miniconda3/envs/comfyui） conda create -n comfyui python=3.10 conda activate comfyui #查询对应目录是否有足够空间（大于需要100G的空]]></description><guid isPermaLink="false">/archives/bu-shu-comfyui-x-z_images_turbo-tu-sheng-tu</guid><dc:creator>Administrator</dc:creator><category>AI</category><pubDate>Tue, 28 Apr 2026 02:51:35 GMT</pubDate></item></channel></rss>