Tonight we brought 's Living Weights to GLM-5.3.
Living Weights lets a running model learn facts straight into its own weights. Ash shipped it in TensorFold 1.0.3 for Nemotron. We ported it to GLM.. (Lets be honest, we are "me and my coding agent")
Full GLM-5.3 (753B), split across four M3 Ultra Mac Studios, learned a fact in about 24 seconds and answered from it, including a phrasing it was never taught. Its 270K-token warm context was never touched.
GLM-5.3-Flash (320B) learns on a single Mac. We taught it "I like blue." Asked "what's my favorite color?", it answered "You like blue."
Two PRs are up for Ash's consideration:
#574 GLM-5.3-Flash
#575 full GLM-5.3
Thinking-mode recall is the next frontier. But it's a working starting point, proven on real hardware. Excited to see what the community builds with it.
And most of all, thank you, . A selfless open-source prodigy. You could have sold this to any lab on earth, and you gave it to all of us instead. Everything above stands on your work.
We're donating $300 to Ash and the TensorFold Foundation. I don't tell anyone what to do with their money, but to me this is an investment. I hope others see what I see in him.
PS: This is getting exciting.. Open Source is winning!
github.com/ashhart/Tensor
github.com/ashhart/Tensor
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This is super cool. I wonder if there could be a negative side effect. Like some finetuned models loose knowledge they originally had given that the weights are modified. Do you expect something similar?
This is a great question. I’m testing 100 memories. To see how those fundamental weight mods work over time.. so far.. this is incredible.
Just checked the PR for flash. It says a a learn takes 7.5 minutes and that lessons couldn't be recalled? Did something change between the PR and this post?
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