⬢github Python · 776 ★ · pushed 12 h ago · MIT
volotat/mini-AGI
Continual learning model trained from scratch on 8GB VRAM laptop with batch-1 stream of data.
mini-AGI is an experimental byte-level language model that learns continually from a single stream of data without catastrophic forgetting. It trains from scratch on a single 8GB VRAM GPU, stores weights as files on disk and pages them to the GPU as needed, grows new capacity when short, and prunes unused parts. The README notes it's a toy-level experiment, with weights not yet published.
Why now: It was featured on Hacker News as a 'Show HN' post, likely attracting attention because it demonstrates continual learning on modest consumer hardware — a challenge to the assumption that training requires massive resources.
Who it is for: ML hobbyists and researchers curious about continual learning who want to train or extend a small language model on their own consumer GPU.
Stars over our 10 snapshots: 776 to 776, since 58 min ago.
Where people talked about it
- ⬢github new repos, most starred just now
- Yhn Show HN: Mini-AGI – Dynamic continual learning model trained on 8GB VRAM 17 min ago
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