Flash Attention 3: Tiling & Asynchrony

Deep dive into the specialized Transformer architecture used as the research baseline. Flash Attention 3: Tiling & Asynchrony turns that idea into something you can manipulate directly and watch respond.

Deep dive into the specialized Transformer architecture used as the research baseline. It focuses on Flash Attention 3, Value Embeddings (ResFormer), Rotary Positional Embeddings (RoPE), RMSNorm from the “The Autoresearch GPT Architecture” section.

The sliders change the key inputs; the “Start Training”, “Reset” buttons run actions or reset the demo. Every change updates the visualization in real time, so you can see exactly how each variable affects the outcome.

Autoresearch is an experimental framework developed by Andrej Karpathy that enables AI agents to autonomously conduct LLM research by iteratively modifying training code. The system utilizes a fixed five-minute training… This demo is the interactive piece for the “The Autoresearch GPT Architecture” section. Open the full Autoresearch: Autonomous AI Research Framework course wiki at https://www.getlykke.com/explore/public/autoresearch-autonomous-ai-research-framework-41216b8f-5a7e-4e9e-bb27-4c25e8cb3bb3 for notes, flashcards, quizzes and the other demos.

You'll build intuition for Flash Attention 3, Value Embeddings (ResFormer), Rotary Positional Embeddings (RoPE), RMSNorm — and, crucially, see how they behave when you change the inputs, which is hard to get from a textbook or lecture on Autoresearch: Autonomous AI Research Framework alone.