Jeremy Howard
8 HN recommenders · recommended in 9 months, 2023–2024
1:31:13 · 580K views · Sep 24, 2023
Jeremy Howard's 90-minute practical tour of language models. It covers how they work, GPT-4's capabilities and limits, using the OpenAI API, code writing, data analysis and fine-tuning.
Repeatedly named the best practical tutorial for using LLMs, with a well-liked fine-tuning section.
13 comments from Hacker News
I understand the sadness around not understanding it, it's fucking hard. however, there are more and more resources online getting published for how to get started understanding it that help with understanding the math at an abstraction that helps with learning how to build with it. I would strongly strongly strongly recommend starting with karpathy's from 0 to hero neural networks youtube course - it starts with building a tensor library and back propagation, explaining it in a way that finally clicked for me [LINK] jeremy howard also has a fantastic video that is more around how to use LLMs and such called a hacker's guide to language models - this video as i've dove more and more into it, i would strongly recommend trying to run things on your local machine too (llama.cpp, ollama, LM studio). that has helped me fight that feeling of like "are we all just going to be open AI developers in the end" and made me feel like you _can_ integrate these things into stuff you build by your self. I can't imagine how fucked we'd all be if llama was never opensourced. being old does not mean that you can't continue to grow, and remember that it's okay to feel overwhelmed about all this - many people are.
Thanks a lot for this video, best LLM usage tutorial I've seen so far. At this video when talking about valid use cases for a local model vs GPT4 is: "You might want to create your own model that's particularly good at solving the kinds of problems that you need to solve using fine tuning, and these are all things that you absolutely can get better than GPT4 performance". In regards to this, there's an idea I've been thinking about for some time: Imagine a chatbot that is backed by multiple "small" models (such as 7B parameters), where each model is fine tuned for a specific task. Could such a system outperform GPT4? Here's a high level overview how I imagine this to work: - Context/prompt is sent to a "router model", which is trained to determine what kind of expert model can best answer/complete the prompt. - The system then passes the context/prompt to the expert model and returns that answer. - If no expert model is found, just use a generic instruct tuned general purpose LLM to answer If you can theoretically get better than GPT4 performance on a small models fine tuned for that task, maybe a cluster of such small models could collectively outperform GPT4. Does that make sense?
An interesting solution to the blind spot error (taken directly from Jeremy Howard's amazing guide to language models - this video ) is to erase the chat history and try again. Once GPT has made an error (or as the author of this article says, the early layers have irreversibly pruned some important data), it will very often start to be even more wrong.
If you're looking for a practical guide on how to use LLMs, highly recommend "Hackers Guide to language models" by Jeremy Howard. 1.5h video packed with practical information: this video
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