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<a href="/2024/03/24/dl-llm-ft/" target="_self">
【大语言模型】大模型监督微调
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<a class="index-excerpt index-excerpt__noimg" href="/2024/03/24/dl-llm-ft/" target="_self">
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大语言模型微调 1. 大模型微调简介 1.1 微调方法定义 微调（Fine-tuning）是一种迁移学习的方法，用于在一个预训练模型的基础上，通过在特定任务的数据上进行有监督训练，来适应该任务的要求并提高模型性能。微调利用了预训练模型在大规模通用数据上学习到的语言知识和表示能力，将其迁移到特定任务上。 下面是一般的微调步骤： 预训练模型选择：选择一个在大规模数据上进行预训练的模型作为基础模型。
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<time datetime="2024-03-24 14:34" pubdate>
2024-03-24
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<a href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">#深度学习</a>
<a href="/tags/%E5%A4%A7%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B/">#大语言模型</a>
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<h2 class="index-header">
<a href="/2024/03/08/dl_llm_model/" target="_self">
【大语言模型】基础模型概念
</a>
</h2>
<a class="index-excerpt index-excerpt__noimg" href="/2024/03/08/dl_llm_model/" target="_self">
<div>
大语言模型简介 1.llm概念 1.1 主流的开源模型体系 目前主流的开源LLM（语言模型）模型体系包括以下几个： GPT（Generative Pre-trained Transformer）系列：由OpenAI发布的一系列基于Transformer架构的语言模型，包括GPT、GPT-2、GPT-3等。GPT模型通过在大规模无标签文本上进行预训练，然后在特定任务上进行微调，具有很强的生成能力
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2024-03-08
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<a href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">#深度学习</a>
<a href="/tags/%E5%A4%A7%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B/">#大语言模型</a>
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<article class="col-12 col-md-12 mx-auto index-info">
<h2 class="index-header">
<a href="/2024/03/05/dl_llm_basic/" target="_self">
【深度学习】DeepL｜LLM基础知识
</a>
</h2>
<a class="index-excerpt index-excerpt__noimg" href="/2024/03/05/dl_llm_basic/" target="_self">
<div>
这里将分享深度学习和大语言模型的基础知识和内容 深度学习基础 训练细节 1.逻辑回归和线性回归 线性回归解决的是回归问题，逻辑回归相当于是线性回归的基础上，来解决分类问题。 线性回归(Linear Regression) \[ \begin{aligned} &f_{w, b}(x)=\sum_i w_i x_i+b\\ \end{aligned} \] 逻辑回归(Logistic Re
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2024-03-05
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<a href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">#深度学习</a>
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<a href="/2024/02/04/dl_rec_al/" target="_self">
【深度学习】推荐系统算法总结
</a>
</h2>
<a class="index-excerpt index-excerpt__noimg" href="/2024/02/04/dl_rec_al/" target="_self">
<div>
推荐系统算法总结 推荐系统近几年有了深度学习的助推发展之势迅猛， 从前深度学习的传统推荐模型(协同过滤，矩阵分解，LR, FM, FFM, GBDT)到深度学习的浪潮之巅(DNN, Deep Crossing, DIN, DIEN, Wide&Deep, Deep&Cross, DeepFM, AFM, NFM, PNN, FNN, DRN)。推荐系统通过分析用户的历史行为给用户
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<time datetime="2024-02-04 00:37" pubdate>
2024-02-04
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<a href="/tags/%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F/">#推荐系统</a>
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<a href="/2024/02/02/develop_consul/" target="_self">
【后端开发】Consul服务与配置
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<a class="index-excerpt index-excerpt__noimg" href="/2024/02/02/develop_consul/" target="_self">
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Consul介绍 Consul是hashicorp公司推出的开源工具，用于实现分布式系统的服务发现与配置。 内置了服务注册与发现框架、分布一致性协议实现、健康检查、key/value存储、多数据中心方案，不再需要依赖其它工具。 Consul是一个服务网络解决方案，它使团队能够管理服务之间以及跨多云环境和运行时的安全网络连接。Consul提供服务发现、基于身份的授权、L7流量管理和服务到服务加密。
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<time datetime="2024-02-02 22:51" pubdate>
2024-02-02
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<a href="/2024/01/23/develop_go_kit/" target="_self">
【后端开发】Go-kit与Gin框架
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<a class="index-excerpt index-excerpt__noimg" href="/2024/01/23/develop_go_kit/" target="_self">
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Go-kit框架 Go-kit 是一个功能丰富、易于使用的分布式微服务框架，旨在帮助开发者构建健壮、可维护和可测试的分布式系统。它通过提供一系列可组合的组件，解决了分布式系统中的常见问题，使开发者能够专注于业务逻辑。Go-kit 的核心理念是通过可插拔的组件来实现微服务的功能，这些组件包括服务发现、负载均衡、请求追踪、日志记录和监控等。 Go-kit基本架构 Go-kit 包含了一些基本的组件，
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<time datetime="2024-01-23 16:50" pubdate>
2024-01-23
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Pymarl代码结构 本文章主要介绍多智能体强化学习中的PyMarl框架的代码结构以及训练流程 Main Pymarl的主文件（main.py)主要的作用是构建一个 sacred.Experiment 类的对象 ex ，ex 包含三个重要的内置变量： _run：表示当前实验运行时的 run 对象，_run.info 可用于记录实验中产生的结果，实验初始时是空字典{}； _c
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<time datetime="2024-01-19 12:21" pubdate>
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<a href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">#深度学习</a>
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参考文档 Docker介绍 Docker 是一个应用打包、分发、部署的工具，也可以把它理解为一个轻量的虚拟机，它只虚拟你软件需要的运行环境，多余的一点都不要，而普通虚拟机则是一个完整而庞大的系统，包含各种不管你要不要的软件。 特性 普通虚拟机 Docker 跨平台 通常只能在桌面级系统运行，例如 Windows/Mac，无法在不带图形界面的服务器上运行 支持的系统非常多，各类
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推荐系统学习资料 从零单排推荐系统文章汇总目录 深度推荐系统 王喆 推荐系统算法实战课程 推荐系统简介 搜索弓|擎需要用户主动输入自己的意图，有时候，用户并不知道自己需要什么，有些需求、意愿，是用户自己都意识不到的。因为用户提不出需求，就"无所事事"，显然这是对宝贵流量的巨大浪费，不利于建立用户粘性。将自己拥有的、用户可能喜欢的内容主动展示给用户，从而留住用户花费更多的时间与金钱。这就是推荐
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<a href="/2024/01/12/dl_gnn/" target="_self">
【深度学习】图神经网络
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图神经网络 GNN全称----图神经网络，它是一种直接作用于图结构上的神经网络。我们可以把图中的每一个节点 \(V\) 当作个体对象，而每一条边 \(E\) 当作个体与个体间的某种联系，所有节点组成的关系网就是最后的图 \(U\) GNN的输入一般是每个节点的起始特征向量和表示节点间关系的邻接矩阵，有了这两个输入信息，接下来就是聚合操作了。所谓的聚合，其实就是将周边与节点 V i ViVi 有关
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<time datetime="2024-01-12 12:03" pubdate>
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