Microgpt explained interactively
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Re: Microgpt explained interactively
#12Re: Microgpt explained interactively
#13"How wrong was the prediction? We need a single number that captures "the model thought the correct answer was unlikely." If the model assigns probability 0.9 to the correct next token, the loss is low (0.1). If it assigns probability 0.01, the loss is high (4.6). The formula is − log ( � ) −log(p) where � p is the probability the model assigned to the correct token. This is called cross-entropy loss."
Re: Microgpt explained interactively
#14I read through this entire article. There was some value in it, but I found it to be very "draw the rest of the owl". It read like introductions to conceptual elements or even proper segues had been edited out. That said, I appreciated the interactive components.
"The MLP (multilayer perceptron) is a two-layer feed-forward network: project up to 64 dimensions, apply ReLU (zero out negatives), project back to 16"
Which starts to feel pretty owly indeed.
I think the whole thing could be expanded to cover some more of it in greater depth.
Re: Microgpt explained interactively
#15Microgpt
Re: Microgpt explained interactively
#16The part that eludes me is how you get from this to the capability to debug arbitrary coding problems. How does statistical inference become reasoning? For a long time, it seemed the answer was it doesn't. But now, using Claude code daily, it seems it does.
Re: Microgpt explained interactively
#17The part that eludes me is how you get from this to the capability to debug arbitrary coding problems. How does statistical inference become reasoning? For a long time, it seemed the answer was it doesn't. But now, using Claude code daily, it seems it does.
An enormous amount of research+eng work (most of the work of frontier labs) is being poured into making that 'correct' modifier happen, rather than just predicting the next token from 'the internet' (naive original training corpus). This work takes the form of improved training data (e.g. expert annotations), human-feedback finetuning (e.g. RLHF), and most recently reinforcement learning (e.g. RLVR, meaning RL with verifiable rewards), where the model is trained to find the correct answer to a problem without 'token-level guidance'. RL for LLMs is a very hot research area and very tricky to solve correctly.
Re: Microgpt explained interactively
#18Re: Microgpt explained interactively
#19Is it becoming a thing to misspell and add grammatical mistakes on purpose to show that an LLM didn't write the blog post? I noticed several spelling mistakes in Karpathy's blog post that this article is based on and in this article.
Re: Microgpt explained interactively
#20Is it becoming a thing to misspell and add grammatical mistakes on purpose to show that an LLM didn't write the blog post? I noticed several spelling mistakes in Karpathy's blog post that this article is based on and in this article.