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<h1 class="index-header">
<a href="/2023/11/28/wsl-proxy/" target="_self">
在 WSL 中优雅地使用宿主机的代理
</a>
</h1>
<a class="index-excerpt index-excerpt__noimg" href="/2023/11/28/wsl-proxy/" target="_self">
<div>
打开 WSL 终端的时候，会出现下面的一个报错： 1wsl: 检测到 localhost 代理配置，但未镜像到 WSL。NAT 模式下的 WSL 不支持 localhost 代理。 这个报错告诉我们，不能用 localhost 直接访问宿主机的代理。 但是如果在 WSL 中能够访问宿主机的 IP，并且宿主机的代理服务“允许来自局域网的连接”，那么 WSL 就可以轻松使用宿主机的代理了。 宿主机的
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<i class="iconfont icon-date"></i>
<time datetime="2023-11-28 17:50" pubdate>
2023-11-28
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<span class="category-chains">
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<a href="/categories/O-M/" class="category-chain-item">O&M</a>
</span>
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<i class="iconfont icon-tags"></i>
<a href="/tags/wsl/">#wsl</a>
<a href="/tags/proxy/">#proxy</a>
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<a href="/2023/06/08/rnn-gru-lstm/" target="_self">
<img src="/img/rnn-gru-lstm.png" srcset="/img/loading.gif" lazyload alt="一文梳理 RNN、GRU 和 LSTM">
</a>
</div>
<article class="col-12 col-md-8 mx-auto index-info">
<h1 class="index-header">
<a href="/2023/06/08/rnn-gru-lstm/" target="_self">
一文梳理 RNN、GRU 和 LSTM
</a>
</h1>
<a class="index-excerpt " href="/2023/06/08/rnn-gru-lstm/" target="_self">
<div>
RNN RNN 的全称是 Recurrent Neural Network，翻译成“循环神经网络”。它是一种用于处理序列数据的神经网络模型。与传统的前馈神经网络不同，RNN 具有向后连接的特性，可以将前面的输出作为后面的输入，从而实现对序列数据的建模和处理。RNN 被广泛应用于自然语言处理、语音识别、机器翻译等领域。一个典型的 RNN 结构图如下： 为了统一描述，本文规定：上标 \(\langl
</div>
</a>
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<div class="post-meta mr-3">
<i class="iconfont icon-date"></i>
<time datetime="2023-06-08 16:07" pubdate>
2023-06-08
</time>
</div>
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<span class="category-chains">
<span class="category-chain">
<a href="/categories/NLP/" class="category-chain-item">NLP</a>
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<i class="iconfont icon-tags"></i>
<a href="/tags/LLM/">#LLM</a>
<a href="/tags/RNN/">#RNN</a>
<a href="/tags/GRU/">#GRU</a>
<a href="/tags/LSTM/">#LSTM</a>
</div>
</div>
</article>
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<article class="col-12 col-md-12 mx-auto index-info">
<h1 class="index-header">
<a href="/2023/04/27/pytorch-random-mask/" target="_self">
PyTorch 随机 Mask 的技巧
</a>
</h1>
<a class="index-excerpt index-excerpt__noimg" href="/2023/04/27/pytorch-random-mask/" target="_self">
<div>
本文记录一下用 PyTorch 随机 Mask 的技巧。 这里假设数值低于 2 的 token 都是特殊 token，不做处理。 123456789101112131415161718import torch# 定义 mask 的 token idmask_token_id = 4def mlm(tensor): # 克隆一份数据，避免修改原始输入数据 tensor = tensor.
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<i class="iconfont icon-date"></i>
<time datetime="2023-04-27 10:51" pubdate>
2023-04-27
</time>
</div>
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<i class="iconfont icon-tags"></i>
<a href="/tags/PyTorch/">#PyTorch</a>
</div>
</div>
</article>
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<h1 class="index-header">
<a href="/2023/04/19/drl-study-notes-basic-concept/" target="_self">
【学习笔记】强化学习基本概念
</a>
</h1>
<a class="index-excerpt index-excerpt__noimg" href="/2023/04/19/drl-study-notes-basic-concept/" target="_self">
<div>
为了加强理解，我们以《超级玛丽》游戏为例，让 AI 使用强化学习，操作马里奥走动和跳跃，完成通关任务。 术语 Agent 用强化学习驱动的作用对象，也就是下面 Action 的作用对象。在《超级玛丽》游戏中，这个 Agent 就是 AI 控制的马里奥。 State 当前系统状态，用 \(s\) 表示。 Action 作用对象执行的操作或动作，用 \(a\) 表示。例如，在《超级玛丽》游戏中，所有
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<i class="iconfont icon-date"></i>
<time datetime="2023-04-19 18:01" pubdate>
2023-04-19
</time>
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<a href="/categories/DRL/" class="category-chain-item">DRL</a>
</span>
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<i class="iconfont icon-tags"></i>
<a href="/tags/Agent/">#Agent</a>
<a href="/tags/Environment/">#Environment</a>
<a href="/tags/State/">#State</a>
<a href="/tags/Action/">#Action</a>
<a href="/tags/Reward/">#Reward</a>
<a href="/tags/Policy/">#Policy</a>
<a href="/tags/StateTransition/">#StateTransition</a>
<a href="/tags/Return/">#Return</a>
<a href="/tags/ValueFunctions/">#ValueFunctions</a>
</div>
</div>
</article>
</div>
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<h1 class="index-header">
<a href="/2023/04/18/flask-uwsgi-docker/" target="_self">
在生产环境中使用 Docker 部署 Flask 服务
</a>
</h1>
<a class="index-excerpt index-excerpt__noimg" href="/2023/04/18/flask-uwsgi-docker/" target="_self">
<div>
用 Anaconda 开启一个新的环境 创建一个新的 conda 环境，并取名为 flask 1conda create --name flask python=3 激活 flask 环境 1conda activate flask 开启一个简单的 Flask 服务 用 pip 安装 Flask 1pip install flask 12345678910111213141516# main.py
</div>
</a>
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<i class="iconfont icon-date"></i>
<time datetime="2023-04-18 17:57" pubdate>
2023-04-18
</time>
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<span class="category-chains">
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<a href="/categories/Docker/" class="category-chain-item">Docker</a>
</span>
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</div>
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<i class="iconfont icon-tags"></i>
<a href="/tags/Docker/">#Docker</a>
<a href="/tags/Flask/">#Flask</a>
<a href="/tags/uWSGI/">#uWSGI</a>
</div>
</div>
</article>
</div>
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<h1 class="index-header">
<a href="/2023/03/30/chatglm-6b-lora/" target="_self">
对 ChatGLM-6B 做 LoRA fine tuning
</a>
</h1>
<a class="index-excerpt index-excerpt__noimg" href="/2023/03/30/chatglm-6b-lora/" target="_self">
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ChatGLM-6B 是一个支持中英双语的对话语言模型，基于 GLM (General Language Model)。它只有 62 亿个参数，量化后最低 (INT4 量化) 只需要 6GB 的显存，完全可以部署到消费级显卡上。在实际使用这个模型一段时间以后，我们发现模型的对话表现能力确实非常不错。那么，基于这个模型做 Fine-tuning 就非常有价值了。
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<time datetime="2023-03-30 09:48" pubdate>
2023-03-30
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<a href="/tags/LLM/">#LLM</a>
<a href="/tags/LoRA/">#LoRA</a>
<a href="/tags/ChatGLM/">#ChatGLM</a>
<a href="/tags/Transformer/">#Transformer</a>
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