Current project
Tiny Frontier
Cheap memory in small LLMs
How do modern LLMs keep memory cheap? I'm rebuilding sliding-window attention and attention sinks from scratch, testing them with controlled multi-seed experiments, all on a $0 compute budget.
- 1.8e-4
- max logit difference loading Google's Gemma 3 270M into my own code, with identical greedy decoding
- 5.8×
- less KV cache at 4K tokens for a 5:1 local:global model that also beats full attention
- 56
- tests, 3 seeds per claim, and every run logged with its config