A Hackers' Guide to Language Models

Jeremy Howard talks about it in his recent video "A Hackers' Guide to Language Models": this video That link goes directly to the timestamp where he discusses fine tuning, but the whole talk is great. Punchline, check out Axolotl: link

You're right about the basic mistakes they can make - they can also excel at the same tasks if prompted differently. But I was making a slightly different point. The point is they can reason about things in a better-than-chance way (I mean, a much-better-than-chance way) when given problems not in the training set. Have you read the Codex paper? Seriously, go look, an LLM even from years ago (which is like decades ago in ML-time) is often able to write code to solve novel programming problems that were handwritten to not be in the training set! Also, the benchmarks against which GPT3, InstructGPT, and GPT4 were tested are illuminating about its problem solving ability on novel problems, these are described in the three respective papers. Secondly, quality is highly variant and there are traps the context window can fall into which causes especially bad results. Jeremy Howard has a great video ( this video , starting at 18:05 the 'limitations and capabilities' section is only 13 minutes long) talking about how quality depends on: how you frame your prompts, model power (4 does a lot of stuff that 3.5 can't), and whether you're in a kind of "context trap" of repeated mistakes. Of course, some people like to point out that if it's so "finicky" and variant, it is "dumb." Sure, if you like. I'm not interested in whatever definitions you're using those things, the objective and observable point is that given well-known prompting practices, LLMs can do something functionally equivalent

Will you still be working on educational content on the side? (e.g. updating fast.ai and/or making one off lectures like this video ) Either way thank you for all the amazing free content you've already put out and good luck on the new endeavor!

For "citation needed", read "please link me to a blog post showing how, don't just tell me to Google for one". The internet is full of blog posts about this. That doesn't mean they're actually good - I'd love to be pointed at one that has proven itself useful for someone (and definitely isn't just LLM blog-spam). I don't care if it's trivial to fine-tune and get crap results - I care about fine-tuning where the result was worth the effort. For the record, my favourite guide to fine-tuning is the section of this Jeremy Howard video that shows how to train a text-to-SQL model: this video

I suspect these videos would be higher quality than most offerings from universities or business schools (given that it's a fast moving field, and most of the true experts are practitioners, and so if they're teaching, they're not teaching actual classes, at most they're doing youtube videos or interviews): - this video (Jeremy Howard Hacker's Guide to LLMs) - link (Karpathy Intro to LLMs)

It sounds like you want more broad stuff, not necessarily learning how to train models. More like learning to use them and how they work. link and Hacker's Guide to LLMs by Jeremy from Fast.ai - this video State of GPT by Karpathy - link LLMs by 3b1b - link Visualizing transformers by 3b1b - link How ChatGPT trained - link AI in a nutshell - link How Carlini uses LLMs - link For staying updated: X/Twitter & Bluesky. Go and follow people that work at OpenAI, Anthropic, Google DeepMind, and xAI. Podcasts: No Priors, Generally Intelligent, Dwarkesh Patel, Sequoia's "Training Data"