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cyberunit
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https://dzen.ru/cyberunit

AI & ML interests

Autonomous Emergency Mobile AI

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reacted to HannesVonEssen's post with ā¤ļø 1 day ago
šŸ“£ Hugging Face Visualizer, now as Chrome extension! https://hfviewer.com āœØ After installing, Hugging Face model pages will have an architecture visualization on the model page itself! šŸ”— Link: https://chromewebstore.google.com/detail/hugging-face-viewer/mmadlggmpkpiockpjfepaohcllbnakej Thanks for all the nice feedback so far! ā¤ļø
reacted to HannesVonEssen's post with šŸ”„ 1 day ago
šŸ“£ Hugging Face Visualizer, now as Chrome extension! https://hfviewer.com āœØ After installing, Hugging Face model pages will have an architecture visualization on the model page itself! šŸ”— Link: https://chromewebstore.google.com/detail/hugging-face-viewer/mmadlggmpkpiockpjfepaohcllbnakej Thanks for all the nice feedback so far! ā¤ļø
reacted to AbstractPhil's post with šŸš€ 10 days ago
By trying to disprove the Omega H2 battery I have discovered; * Each topology formed by the H2 battery is deviant, none have a uniformly shared substrate of behavior. They are each uniquely independent per training set all with perfect recon. * Image recon can be tracked and mapped, yielding a consistently mapped and response 16.77m vocabulary potential. In the current spectrum testing at around 5 million unicode bytes. * The model scale shows patch size is related to how much data you want the model to represent within the model itself, and this has yet to see a capacity to this day. The MSE recons and yields - and the more data fed, the more they yield. * The scaling principle shows that the model indefinitely scales upward and each level of the model can be iteratively captured upward to form deviant and uniformly consistent repeatable pathways of implicit codewise response, not just arbitrary bitwise recall. Meaningful implicit learned utility. * Image recon patch size should match the slice of image you want to represent, as it uses patch smoothing per patch internally from identity. * byte trigrams are channel-agnostic, they do not require a channel count just a formula for recall at nGram recall 99.6% for byte-by-byte representations. With those comes an adjacently capable codebook. * sentencepiece preliminary tests show validity and reconstruction just like the byte trigrams, using the new byte trigram this would be arbitrarily convenient to recon a codebook for the structure. * binary trees learn a uniformly potent and powerful gating mechanism that required further exploration, each of them produces direct responsive independent capacity and the responses are controllable. * ternary experiments show the models are directly responsive to -1, 0, +1 behavior, so the quantization is very much a valid potential. * preliminary tests with the H2O1 series of batteries show the models are responding similar to natural universal elements in the universe itself
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