Wondering how long it takes to train a 1B-param LM from scratch on your GPUs? 🧵 See our paper to learn about the current state of academic compute and how to efficiently train models! Use our code to test your own models/GPUs! arxiv.org/abs/2410.23261 github.com/apoorvkh/acade…

Building/customizing your own LLM? You'll want to curate training data for it, but how do you know what makes the data good? You can try out recipes👩‍🍳 iterate on vibes✨ but we can't actually test all possible combos of tweaks,,, right?? 🙅‍♂️WRONG! arxiv.org/abs/2410.15661 (1/n) 🧵

At #NAACL2025 🌵till Sunday! Love to chat about interpretability, understanding model internals, and finding vegan food 🥬

Presenting this today at the poster session at #NAACL2025! Come chat about interpretability, trustworthiness, and tool-using agents! 🗓️ - Thursday May 1st (today) 📍 - Hall 3 🕑 - 200-330pm

Excited to announce that I started at Google Cloud as a student researcher last month working with Hamid Palangi on actionable #interpretability 🔍 to build better tool using #agents ⚒️🤖

🚨New #interpretability paper with Nishant Subramani@ACL🇦🇹 :🕵️Model Internal Sleuthing: Finding Lexical Identity and Inflectional Morphology in Modern Language Models

🚨 Check out our new #interpretability paper: 🕵🏽 Model Internal Sleuthing led by the amazing Michael Li who is an undergrad at CMU School of Computer Science Language Technologies Institute | @CarnegieMellon!

At #ICML2025 till Sunday! Love to chat about #interpretability, understanding model internals, and finding yummy vegan food in Vancouver 🥬🍜

At #ACL2025 in Vienna 🇦🇹 till next Saturday! Love to chat about anything #interpretability 🔎, understanding model internals 🔬, and finding yummy vegan food 🥬