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AbstractPhil
AbstractPowered
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mystifying's profile picture SelmaNajih001's profile picture FiditeNemini's profile picture
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https://civitai.com/user/AbstractPhila

AI & ML interests

datasets, research papers, experimentation, vision, classification, text encoders, tokenization, llms, diffusion, distillation, and more.

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updated a model about 3 hours ago
AbstractPhil/geolip-aleph-qwen-3.5-0.8b-instruct
published a model about 3 hours ago
AbstractPhil/geolip-aleph-qwen-3.5-0.8b-instruct
repliedto their post about 16 hours ago
Massive AlephLM success. The task collective is producing powerful MOE shared knowledge adapters. A serious success and a massive first step towards the next stage. The current family collective results are present here; https://huggingface.co/AbstractPhil/geolip-aleph-qwen/blob/main/exp009_family/results/results.json This is akin to a stackable non-intrusive lora that enables increased shared collective behavior. This includes the three mentioned json tasks, a math task, a tinystories task, and a diffusion task for cifar10. Each adapter anchored to the knowledge within model that already exists while enhancing the knowledge through anchored lookup systems and decision-driven hierarchical access trees. All tasks activate independently upon manual override, all tasks handle direct shared knowledge when left to greedy decoding, each task issued multiple tests alongside to determine fidelity and accuracy throughout the process. The results show the gating is more than willing to hop from sector to sector, using alternating weight shifts from the cooperative anchored systems - even systems never trained for the tasks contributing to the accuracy of the results for other tasks due to the lookup accuracy to the heuristic chains, never having seen the tasks before. Each structure is independently trained and the collective cooperates together through a dense activation network. Full writeup and article https://huggingface.co/blog/AbstractPhil/aleph-autoregression-differentiation-ft2.
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