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微调之道

选择 LLM 完成一个 NLP 任务,如何下手? 从下图中就能很好的明白哪个操作适合完成你当前的任务: 如果你有时间和大量数据,你完全可以重新训练模型;一定量的数据,可以对预训练模型进行微调;数据不多,最好的选择是 “in context learning”,上下文学习…
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LLM演进史(四):WaveNet——序列模型的卷积革新

本节内容的源代码仓库。 我们在前面的部分搭建了一个多层感知机字符级的语言模型,现在是时候把它的结构变的更复杂了。现在的目标是,输入序列能够输入更多字符,而不是现在的 3 个。除此之外,我们不想把它们都放到一个隐藏层中,避免压缩太多信息。这样得到一个类似WaveNet的更深的模型。…
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LLM演进史(三):批归一化——激活与梯度的统计调和

本节的重点在于,要对于训练过程中神经网络的激活,特别是向下流动的梯度有深刻的印象和理解。理解这些结构的发展历史是很重要的,因为 RNN (循环神经网络),作为一个通用逼近器 (universal approximator),它原则上可以实现所有的算法…
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GPT的现状

本文是对 Andrej Karpathy 的在 2023 年 3 月份的 Microsoft Build 演讲的整理。 演讲 Beamer 可见于:https://karpathy.ai/stateofgpt.pdf 演讲介绍了 GPT 的训练过程,发展地步,当前的 LLM…
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LLM演进史(二):词嵌入——多层感知器与语言的深层连接

本节的源代码仓库地址 本文算是训练语言模型的经典之作,Bengio 将神经网络引入语言模型的训练中,并得到了词嵌入这个副产物。词嵌入对后面深度学习在自然语言处理方面有很大的贡献,也是获取词的语义特征的有效方法。 论文的提出源于解决原词向量(one-hot 表示…
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LLM演进史(一):Bigram的简洁之道

本节的源代码仓库地址 前面我们通过实现micrograd,弄明白了梯度的意义和如何优化。现在我们可以进入到语言模型的学习阶段,了解初级阶段的语言模型是如何设计、建模的。 Bigram (一个字符通过一个计数的查找表来预测下一个字符。) MLP, 根据 Bengio et al…
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从0实现一个极简的自动微分框架

代码仓库:https://github.com/karpathy/nn-zero-to-hero Andrej Karpathy 是著名深度学习课程 Stanford CS 231n 的作者与主讲师,也是 OpenAI 创始人之一,"micrograd" 是他创建的一个小型…
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