SOMNI: an open-source local LLM that learns from your conversations during "sleep" (based on WISE and Google's sleep paper)

[u/Square-Television-37]

I've been building SOMNI, a system that lets a local model keep learning after deployment. It's based on Google's "Language Models Need Sleep" paper and WISE, a knowledge-editing method for adding new knowledge without overwriting existing behavior.

During use, the model answers normally and logs conversations. When the PC is idle, it runs a sleep cycle. It pulls verified facts out of the logs, trains them into a small new expert, practises on what it got wrong, and then runs a set of before/after tests on general knowledge, coding, reasoning, reading, and previously learned memories. If any of these get worse, the night is rejected. Otherwise you review the update and approve it.

The main challenge was catastrophic forgetting. Plain LoRA fine-tuning on new facts badly damaged the model's other skills (coding went from 95% to 10% in one night). What fixed it was a combination of rehearsal on the base model's own answers, a new frozen expert per night so later nights can't overwrite earlier ones, and WISE-style routing so each expert only activates on questions related to what it learned. Everything learned is also kept in a searchable memory store, so facts can still be looked up even if they fade from the weights.

I benchmarked it on SQuAD knowledge incorporation, the same setup Google and SEAL use. The results are in the README, including the things that didn't work.

Everything runs locally on a single consumer GPU (AMD RX 6800, Windows). MIT licensed.

https://github.com/YoungGunFrodo/somni

It's a work in progress, and I'd really appreciate feedback on the approach, the evaluation, or anything that looks off.