aarush
66 posts
Sold out! But I had Claude create and deploy all 80 volumes of The Weights to the site as well-formatted PDFs, so you can download them for free if you want. 58,276 pages in total. 117 million floating point numbers. This is everything that makes GPT-1. weights-press.netlify.app
recently, i spent some time working on cross-lingual alignment for LLMs via encoder injection! treating languages as modalities is a compute-efficient way to extend understanding of low-resource languages without extending pre-training or the tokenizer
Excited to release new repo: nanochat! (it's among the most unhinged I've written). Unlike my earlier similar repo nanoGPT which only covered pretraining, nanochat is a minimal, from scratch, full-stack training/inference pipeline of a simple ChatGPT clone in a single, dependency-minimal codebase. You boot up a cloud GPU box, run a single script and in as little as 4 hours later you can talk to your own LLM in a ChatGPT-like web UI. It weighs ~8,000 lines of imo quite clean code to: - Train the tokenizer using a new Rust implementation - Pretrain a Transformer LLM on FineWeb, evaluate CORE score across a number of metrics - Midtrain on user-assistant conversations from SmolTalk, multiple choice questions, tool use. - SFT, evaluate the chat model on world knowledge multiple choice (ARC-E/C, MMLU), math (GSM8K), code (HumanEval) - RL the model optionally on GSM8K with "GRPO" - Efficient inference the model in an Engine with KV cache, simple prefill/decode, tool use (Python interpreter in a lightweight sandbox), talk to it over CLI or ChatGPT-like WebUI. - Write a single markdown report card, summarizing and gamifying the whole thing. Even for as low as ~$100 in cost (~4 hours on an 8XH100 node), you can train a little ChatGPT clone that you can kind of talk to, and which can write stories/poems, answer simple questions. About ~12 hours surpasses GPT-2 CORE metric. As you further scale up towards ~$1000 (~41.6 hours of training), it quickly becomes a lot more coherent and can solve simple math/code problems and take multiple choice tests. E.g. a depth 30 model trained for 24 hours (this is about equal to FLOPs of GPT-3 Small 125M and 1/1000th of GPT-3) gets into 40s on MMLU and 70s on ARC-Easy, 20s on GSM8K, etc. My goal is to get the full "strong baseline" stack into one cohesive, minimal, readable, hackable, maximally forkable repo. nanochat will be the capstone project of LLM101n (which is still being developed). I think it also has potential to grow into a research harness, or a benchmark, similar to nanoGPT before it. It is by no means finished, tuned or optimized (actually I think there's likely quite a bit of low-hanging fruit), but I think it's at a place where the overall skeleton is ok enough that it can go up on GitHub where all the parts of it can be improved. Link to repo and a detailed walkthrough of the nanochat speedrun is in the reply.
In the last four weeks, @tryramp Research (our AI analyst agent in slack) has answered 1,476 questions, compared to 66 in our help data channel. This is one of my favorite projects that we've shipped. Our team built an agentic solution based on tools to index domain docs, to search tables and columns, to run SQL iteratively: reasoning through data challenges the way a human would. As @bennstancil wrote: data's constant presence in an organization is like knowing the count of the deck. Though it makes us a bit more informed in each decision, the effect is only felt in the aggregate, as the small edge compounds over time. We are making a lot more data-driven decisions at Ramp than we were two months ago!
