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大语言模型入门：先建立整体认识
学习资料
这个精选视频的资料正在准备中，可以先观看视频并阅读学习导读。
PPT 资料为文字大纲，尚非完整演示文稿。
视频概览
Andrej Karpathy 面向大众的一小时大语言模型入门，介绍模型是什么、如何训练、与操作系统的类比，以及越狱等安全挑战。
HN 讨论的原因
HN 推荐它作为内容密集但容易理解的 LLM 入门，也经常引用其中关于规模扩展与训练的解释。
观看前准备
想先理解语言模型的基本概念，可以从这里开始。具备基本的计算机常识即可；这是一节概览课。
学习建议
看完后，用自己的话描述语言模型，并区分模型训练与产品中的实际使用。想了解更多背景可继续看深入讲解，想打好实现基础则可学习 micrograd。
最新优先
所在讨论： The Unbearable Cheapness of Open Weight Models
The weights are just numbers. I don't what technical background you have in other areas of computing, but I think that this is a good, short introduction that doesn't assume too much: [LINK] To quote part of it, Training a model can be thought of as tuning the dials on a really big machine. The way that a language model behaves is entirely determined by these many different continuous values, usually called…The weights are just numbers. I don't what technical background you have in other areas of computing, but I think that this is a good, short introduction that doesn't assume too much: [LINK] To quote part of it, Training a model can be thought of as tuning the dials on a really big machine. The way that a language model behaves is entirely determined by these many different continuous values, usually called parameters or weights. Longer and slightly more technical, "Intro to Large Language Models" by Andrej Karpathy: [VIDEO]
所在讨论： The Intuition Behind How Large Language Models Work, Part I
* "[1hr Talk] Intro to Large Language Models" by Andrej Karpathy: [VIDEO] from [LINK] * Andrej Karpathy: [LINK]
所在讨论： Andrej Karpathy: Software in the era of AI [video]
I watched Karpathy's Intro to Large Language Models [0] not so long ago and must say that I'm a bit confused by this presentation, and it's a bit unclear to me what it adds. 1,5 years ago he saw all the tool uses in agent systems as the future of LLMs, which seemed reasonable to me. There was (and maybe still is) potential for a lot of business cases to be explored, but every system is defined by its boundaries…I watched Karpathy's Intro to Large Language Models [0] not so long ago and must say that I'm a bit confused by this presentation, and it's a bit unclear to me what it adds. 1,5 years ago he saw all the tool uses in agent systems as the future of LLMs, which seemed reasonable to me. There was (and maybe still is) potential for a lot of business cases to be explored, but every system is defined by its boundaries nonetheless. We still don't know all the challenges we face at that boundaries, whether these could be modelled into a virtual space, handled by software, and therefor also potentially AI and businesses. Now it all just seems to be analogies and what role LLMs could play in our modern landscape. We should treat LLMs as encapsulated systems of their own ...but sometimes an LLM becomes the operating system, sometimes it's the CPU, sometimes it's the mainframe from the 60s with time-sharing, a big fab complex, or even outright electricity itself? He's showing an iOS app, which seems to be, sorry for the dismissive tone, an example for a better looking counter. This demo app was in a presentable state for a demo after a day, and it took him a week to implement Googles OAuth2 stuff. Is that somehow exciting? What was that? The only way I could interpret this is that it just shows a big divide we're currently in. LLMs are a final API product for some, but an unoptimized generative software-model with sophisticated-but-opaque algorithms for others. Both are utterly in need fo
所在讨论： TexTube: Chat with any YouTube video transcript in ChatGPT fast
It might be a preview or something because I have YT premium and doesn't show up that anywhere. Can you share a video that works for that? Like this one. [VIDEO]
所在讨论： OpenDevin: An Open Platform for AI Software Developers as Generalist Agents
Actually the improvement graphs are still scaling exponentially with training/compute being the bottleneck. So there isn't yet any evidence of diminishing returns. source: [VIDEO]
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