[1hr Talk] Intro to Large Language Models

Hey, imho best overall technical intro to LLMs (I guess that´s your main interest as you mentioned qlora + llama) is by Simon Willis [1]. Additionally or if you prefer videos, the recent 1h "busy persons intro" by Andrei Karpathy is great + dense as well [2]. [1] [link] [2] [this video] EDIT: Maybe I misunderstood as you asked about papers, not general intros. I don´t think that reading papers is the best way to "catch up" as the pace is rapid and knowledge very decentralized. I can confirm what Andrej recently wrote on X [3]: "Unknown to many people, a growing amount of alpha is now outside of Arxiv, sources include but are not limited to: - [link] - HN - that niche Discord server - anime profile picture anons on X - reddit" [3] [link]

My view is to focus on doing stuff. That's what I did. Pick up some task you want the model to do, try finetuning llama, playing with APIs from OpenAI, etc. Googling and asking GPT along the way. Foundational model training got so expensive that unless you can get hired by "owns nuclear power plant of GPUs" you are not going to get any "research" done. And as the area got white-hot those companies have more available talent than hardware nowadays. So just getting into the practitioner area is the best way to get productive with those models. And you improve as a practitioner by practicing, not by reading papers. If you're at the computer, your time is best spent writing code and interacting with those models in my opinion. If you cannot (e.g. commute) I listen to some stuff (e.g. [this video] - Anything from Karpathy on youtube, or [link] channel).

Current architectures have a lot of juice left across several axes, using extraordinarily accurate scaling laws. We're nowhere close to a wall. The industry has been focusing hard on 'System Two' approaches to augment our largely 'System One'-style models, where optimal decision policies make Q-learning a natural approach. The recent unlock here might be related to using the neural nets' general intelligence/flexibility to better broadly estimate their own states/rewards/actions (like humans). [EDIT: To be clear, the Q-learning stuff is my own speculation, whereas the 'System Two' stuff is well known.] Serendipitously, Karpathy broadly discussed these two issues yesterday ! Toward the lecture's end: [this video]

Well, I was discussing Large Language Models (LLMs) as a technology, and esp, via this video [0] with a friend, just now. He made a striking remark. LLMs compress the information they ingest in a variably-lossy way, and when you query them, they rebuild a representation from whole or part of this compressed data in a statistical manner. As a result they're a lossy storage medium. Not unlike some music formats which store audio data in a lossy way. You know, this data can be restored with mathematical and statistical wizardry. You don't get everything you put in, but 97%-99% out of it, and companies and RIAA went bonkers for years, because even if it's not an exact reproduction, it was a reproduction enough, and this is a copyright violation. If an LLM can reproduce what I have written, or coded with 97%-99% accuracy without any license information, and I licensed this thing with less than permissive licenses, and sue the maker of that LLM, what will happen? - Will it be a copyright infringement? - Will it be fair use? It's the first if you look at it fairly, but it'll be probably be the latter, because money, fame and other corporate points will be at stake, otherwise. [0]: [this video] Edit: I forgot to add the 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 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