I trained a 44M parameter quantized LLM from scratch on 45B tokens. It ships in 19.8 MB and runs at ~1,900 tok/s on CPU. [P]

Overview

Three weeks back , i posted SHADOW-250M here. It got 360 upvotes, 293 on r/LocalLLaMA and 94 GitHub stars. Thank you. That model was 60 MB, ran around 400 tok/s on CPU and could retrieve records from an archive on disk. What it couldn’t do reliably was reason over what it retrieved or compute. So I built a smaller one to experiment with those two problems. SHADOW-50M is actually 44M parameters, trained from scratch on 45B tokens. 19.8 MB complete model, ~1,900 tok/s on laptop CPU, ~41 MB RAM, te

SpinGraph analysis pending — check back after processing.