<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>LLM - 标签 - 研发日志 · R&amp;D Log</title><link>https://rd163.visword.com/tags/llm/</link><description>LLM - 标签 - 研发日志 · R&amp;D Log</description><generator>Hugo -- gohugo.io</generator><language>zh-CN</language><managingEditor>whutluohui@gmail.com (小智晖)</managingEditor><webMaster>whutluohui@gmail.com (小智晖)</webMaster><copyright>本作品采用知识共享署名-非商业性使用 4.0 国际许可协议进行许可。</copyright><lastBuildDate>Sat, 08 Mar 2025 00:00:00 +0800</lastBuildDate><atom:link href="https://rd163.visword.com/tags/llm/" rel="self" type="application/rss+xml"/><item><title>llama.cpp 使用</title><link>https://rd163.visword.com/posts/llama-cpp-get-started/</link><pubDate>Sat, 08 Mar 2025 00:00:00 +0800</pubDate><author><name>小智晖</name></author><guid>https://rd163.visword.com/posts/llama-cpp-get-started/</guid><description><![CDATA[<h2 id="install" class="headerLink">
    <a href="#install" class="header-mark"></a>Install</h2><div class="code-block highlight is-open show-line-numbers  tw:group tw:my-2">
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  <pre style="counter-reset: codeblock;" class="tw:block tw:m-0 tw:p-0"><code 
    id="codeblock-id-28" 
    class="
      chroma 
      tw:block! 
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      "><span class="line"><span class="cl">git clone https://github.com/ggml-org/llama.cpp.git
</span></span><span class="line"><span class="cl">mkdir build &amp;&amp; cd build
</span></span><span class="line"><span class="cl">cmake .. 
</span></span><span class="line"><span class="cl">make &amp;&amp; make install</span></span></code></pre>
</div>
<h2 id="gguf量化" class="headerLink">
    <a href="#gguf%e9%87%8f%e5%8c%96" class="header-mark"></a>GGUF量化</h2><p><strong>一、GGUF量化过程</strong></p>]]></description></item><item><title>LLM Retrieval-Augmented Generation(RAG)</title><link>https://rd163.visword.com/posts/llm-retrieval-augmented-generation/</link><pubDate>Fri, 14 Feb 2025 00:00:00 +0800</pubDate><author><name>小智晖</name></author><guid>https://rd163.visword.com/posts/llm-retrieval-augmented-generation/</guid><description><![CDATA[<p>相关论文：<a href="https://arxiv.org/abs/2005.11401" target="_blank" rel="noopener noreferrer">Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks</a>
这里有个 <a href="https://github.com/SmartFlowAI/Hand-on-RAG" target="_blank" rel="noopener noreferrer">手搓的RAG</a> github项目可以一窥RAG的全貌。</p>
<h2 id="什么是检索增强生成" class="headerLink">
    <a href="#%e4%bb%80%e4%b9%88%e6%98%af%e6%a3%80%e7%b4%a2%e5%a2%9e%e5%bc%ba%e7%94%9f%e6%88%90" class="header-mark"></a>什么是检索增强生成？</h2><p>检索增强生成（RAG）是一个概念，它旨在为大语言模型（LLM）提供额外的、来自外部知识源的信息。这样，LLM 在生成更精确、更贴合上下文的答案的同时，也能有效减少产生误导性信息的可能。</p>]]></description></item><item><title>LLM 工作流编排方案</title><link>https://rd163.visword.com/posts/llm-workflow-compose/</link><pubDate>Fri, 14 Feb 2025 00:00:00 +0800</pubDate><author><name>小智晖</name></author><guid>https://rd163.visword.com/posts/llm-workflow-compose/</guid><description><![CDATA[<h2 id="商业方案" class="headerLink">
    <a href="#%e5%95%86%e4%b8%9a%e6%96%b9%e6%a1%88" class="header-mark"></a>商业方案</h2><ul>
<li><a href="https://yuanqi.tencent.com/agent-shop" target="_blank" rel="noopener noreferrer">腾讯元器</a></li>
<li><a href="https://www.coze.cn/home" target="_blank" rel="noopener noreferrer">字节扣子</a></li>
</ul>
<h2 id="开源" class="headerLink">
    <a href="#%e5%bc%80%e6%ba%90" class="header-mark"></a>开源</h2><ul>
<li><a href="https://github.com/langgenius/dify" target="_blank" rel="noopener noreferrer">Dify</a></li>
<li><a href="https://github.com/infiniflow/ragflow" target="_blank" rel="noopener noreferrer">RAGFLOW</a></li>
<li><a href="https://github.com/deepset-ai/haystack" target="_blank" rel="noopener noreferrer">Haystack</a></li>
<li><a href="https://github.com/labring/FastGPT" target="_blank" rel="noopener noreferrer">FastGPT</a></li>
<li><a href="https://github.com/netease-youdao/QAnything" target="_blank" rel="noopener noreferrer">QAnything</a></li>
</ul>]]></description></item><item><title>LLM时代下的AI网关</title><link>https://rd163.visword.com/posts/gateway-for-ai/</link><pubDate>Thu, 23 Jan 2025 00:00:00 +0800</pubDate><author><name>小智晖</name></author><guid>https://rd163.visword.com/posts/gateway-for-ai/</guid><description><![CDATA[<h2 id="ai-网关" class="headerLink">
    <a href="#ai-%e7%bd%91%e5%85%b3" class="header-mark"></a>AI 网关</h2><p>AI Gateway 也被称为大模型网关、AI 网关，是一个用于部署和管理人工智能（AI）模型的平台，在开源社区有对应技术实现。它为用户提供了一种方便的方式来部署和管理 AI 模型，无论这些模型是预训练的模型，还是用户自己开发的模型。AI Gateway 还提供了一种方式，让用户能够在需要的时候轻松地调用这些模型，例如在进行数据分析或开发新的 AI 应用程序时。此外，AI Gateway 还提供了各种工具，可以帮助用户监控模型的性能，以及进行模型的优化。</p>]]></description></item><item><title>vLLM</title><link>https://rd163.visword.com/posts/vllm/</link><pubDate>Sun, 12 Jan 2025 00:00:00 +0800</pubDate><author><name>小智晖</name></author><guid>https://rd163.visword.com/posts/vllm/</guid><description><![CDATA[<h1 id="vllm" class="headerLink">
    <a href="#vllm" class="header-mark"></a>vLLM</h1><p>vLLM 是一个快速且易于使用的库，用于 LLM 推理和服务，和 HuggingFace 无缝集成。区别于 chatglm.cpp 和 llama.cpp，仅是在 GPU 上的模型推理加速，没有 CPU 上的加速。</p>
<p>在吞吐量方面，vLLM 的性能比 HuggingFace Transformers  （HF） 高出 24 倍，文本生成推理 （TGI） 高出 3.5 倍。</p>]]></description></item></channel></rss>