Quotations tagged andrejkarpathy, generativeai
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Turns out that LLMs learn a lot better and faster from educational content as well. This is partly because the average Common Crawl article (internet pages) is not of very high value and distracts the training, packing in too much irrelevant information. The average webpage on the internet is so random and terrible it’s not even clear how prior LLMs learn anything at all.
— Andrej Karpathy # 2nd June 2024, 9:09 pm
The realization hit me [when the GPT-3 paper came out] that an important property of the field flipped. In ~2011, progress in AI felt constrained primarily by algorithms. We needed better ideas, better modeling, better approaches to make further progress. If you offered me a 10X bigger computer, I’m not sure what I would have even used it for. GPT-3 paper showed that there was this thing that would just become better on a large variety of practical tasks, if you only trained a bigger one. Better algorithms become a bonus, not a necessity for progress in AGI. Possibly not forever and going forward, but at least locally and for the time being, in a very practical sense. Today, if you gave me a 10X bigger computer I would know exactly what to do with it, and then I’d ask for more.
— Andrej Karpathy # 30th May 2024, 7:27 am
[on GitHub Copilot] It’s like insisting to walk when you can take a bike. It gets the hard things wrong but all the easy things right, very helpful and much faster. You have to learn what it can and can’t do.
— Andrej Karpathy # 11th April 2024, 1:27 am
I always struggle a bit with I’m asked about the “hallucination problem” in LLMs. Because, in some sense, hallucination is all LLMs do. They are dream machines.
We direct their dreams with prompts. The prompts start the dream, and based on the LLM’s hazy recollection of its training documents, most of the time the result goes someplace useful.
It’s only when the dreams go into deemed factually incorrect territory that we label it a “hallucination”. It looks like a bug, but it’s just the LLM doing what it always does.
— Andrej Karpathy # 9th December 2023, 6:08 am
Looking at LLMs as chatbots is the same as looking at early computers as calculators. We’re seeing an emergence of a whole new computing paradigm, and it is very early.
— Andrej Karpathy # 28th September 2023, 8:50 pm
llama.cpp surprised many people (myself included) with how quickly you can run large LLMs on small computers [...] TLDR at batch_size=1 (i.e. just generating a single stream of prediction on your computer), the inference is super duper memory-bound. The on-chip compute units are twiddling their thumbs while sucking model weights through a straw from DRAM. [...] A100: 1935 GB/s memory bandwidth, 1248 TOPS. MacBook M2: 100 GB/s, 7 TFLOPS. The compute is ~200X but the memory bandwidth only ~20X. So the little M2 chip that could will only be about ~20X slower than a mighty A100.
— Andrej Karpathy # 16th August 2023, 4:13 am
The most dramatic optimization to nanoGPT so far (~25% speedup) is to simply increase vocab size from 50257 to 50304 (nearest multiple of 64). This calculates added useless dimensions but goes down a different kernel path with much higher occupancy. Careful with your Powers of 2.
— Andrej Karpathy # 4th February 2023, 12:08 am