I count and report the lines in uv.lock, which is my attempt at a simple 80:20 proxy for this, as it includes all the packages recursively. Open to suggestions! I'd love to measure things like "cognitive complexity" (there was a great blog post on it a few months back that I
@karpathy
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Model Quantization for Inference: Technical Questions
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Thank you! Notably I didn't yet include model quantization for inference. I have questions 🙂
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Hugging Face Datasets Critical Infrastructure for AI Model Training
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Ty! huggingface work/infra/datasets are critical to projects like nanochat – to be accurate the source code of nanochat (e.g. at the $100 tier) is ~8KB of Python and ~30GB of fineweb/smoltalk.
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QK Norm and Embedding Weight Sharing in Model Optimization
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Very early on in the project I did a small run with/without QK norm and found that it helped. Same for the embedding weight sharing. I'll retry! I'm not tied to any details of the model and they weren't chosen any more carefully than a single run, I spent most of the time just
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LLM101n Experiences Significant Scope Creep Issues
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LLM101n is currently experiencing a lot of scope creep
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AI as intermediary layer: subsampling over AI integration
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AI would be an added layer between you and reality. In case the volume is too great (I doubt it), I'd much rather subsample/skim before I tried to add AI.
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Every company needs a DM point of contact executive
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Every company needs a DM POC – someone high up who you can just DM the most obvious things and who shortcuts the PM hierarchy.
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LLM Code Generation and Agent Classification Discussion
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Yeah one more person pointed out already. I'd say it falls under "agent" because an LLM is still giving you a large chunk of code that you're slotting in.
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NotebookLM Context Window vs RAG Trade-offs
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I'd be afraid that NotebookLM is wired up to do RAG. It's not a lot of tokens. I want them in the context window. All of them and for sure.
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LLM Training: RL vs SFT with In-Context Learning
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"Don’t be difficult. I mean this is obvious." Sutton is right ofc. The analogue in LLM land to what humans do is something along the lines of: Given this math problem AND human example solution in the context, solve the problem. Reward of 1 if correct. It's not SFT, it's RL.