volotat/mini-AGI
Continual learning model trained from scratch on 8GB VRAM laptop with batch-1 stream of data.
From the publisher
README & documentation
Source previewRead the project’s overview, installation instructions and usage examples. The original README is the source of truth.
mini-AGI - is a continual learning byte-level language model that assembles its own architecture, trains from scratch on a single 8 GB VRAM GPU, and keeps learning from everything it reads. It stores its weights as ordinary files on disk and pages them onto the card as it needs them, so the parameter count is bounded by free disk space rather than by VRAM. It grows new capacity while training when it runs short, prunes what nothing asks for, and reads through exactly the same code path it serves on. Targeted at a PC or laptop with at least an 8 GB VRAM GPU on the board. NOTE: as of now this is a small toy-level model. Do not expect a frontier level capabilities. This is rather a small experiment to show, that continual learning from the single stream of data without catastrophic forgetting is possible. Furthermore it is possible…
Read the full README ↗ · Preview checked 2026-09-22T13:26:18.333Z
Inside the original README — Document outline
- mini-AGI
- Motivation
- How the architecture works
- ...and the same thing while it writes
- How paging works
- How growth and pruning work
- How continual learning works
- Reading your own files
- Benchmarks
- Data Scaling
- Running it
- Everything else
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What this repository does
Continual learning model trained from scratch on 8GB VRAM laptop with batch-1 stream of data.
Repository facts
- Owner
- volotat
- Primary language
- Python
- Stars
- 448
- Forks
- 42
- Open issues + pull requests
- 2
- License
- MIT
- Archived
- No
- Default branch
- main
- Created
- 2026-09-19T00:25:59.000Z
- Last push
- 2026-09-22T00:41:36.000Z
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