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AbstractPhil
ronantakizawa's profile picture FanchL's profile picture EricRollei'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.

Recent Activity

posted an update about 6 hours ago
Happy Holidays all! geofractal architectural expansions; timm is now a core component for experimenting. As it stands, the system is growing rapidly in one direction, and timm brings a whole lot to the table in another rapid-prototyping direction. Therefore, timm is now a core component for ease-of-use. BaseUtil is a new core component; aka src.geofractal.router.base_util inherits BaseComponent's behavior, so it should allow device movement for util operations which will direct utilization for device-to-device behavior for the upcoming accelerate integration. I'm trying to mitigate the base component structure as much as possible, but the need to chain components in specific orders presented a unique problem. By compartmentalizing utils into structures that can be delegated and moved, these structures can be repurposed, expanded autonomously, reduced autonomously, and more. ChainComponent inherits a subsystem specifically designed to organize multi-system multi-device formulas designated for inception and synchronization purposes. This is meant to allow distributed tasking to multiple-devices in chained utilization. This also enables ease-of-integration into nn.ModuleList with a few other caveats that will be ironed out meant to target wide-distributed models. FusionComponent is specifically dedicated to the new fusion processing system meant for experimental expansion. This includes sub-module schedule control, Component and Tower functional control, device-movement, and will be packaged under the term "gfu.UtilType" as a standard naming convention. "gfc.ComponentTypeName" "gfr.RouterTypeName" "gfu.UtilityTypeName" "gft.TowerTypeName" All of which are basically just import thing as. "gf.AnythingTopLevelPackaged" which will include the core. Better debugging for compilation I'm in prototyping phases of a better debugging for compiled wide models and will prepare a baseline component readout structure by the end of the day today or tomorrow.
published an article 1 day ago
Geometric Manifold Walking: Stable High-Accuracy Multi-Encoder Fusion Without Backbone Training
replied to their post 3 days ago
geofractal getting started guide available, bulk ablation for fusion, simple towers, oscillator capacity, and substructure systemic associative capacity. Many formulas were tested, 92 tests for collectives, oscillation bulk experiments, and more. All of them either coalesce into the correct behavior or the failures are directly visible, which means the system is robust enough to declare some tools functionally valid but not scalable yet. ai-crash course available; https://github.com/AbstractEyes/geofractal/blob/main/ai_helpers/v101_claude_helpers.txt Feed GPT, Claude, or Grokk and they will assist. getting started guide; https://github.com/AbstractEyes/geofractal/blob/main/src/geofractal/router/GETTING_STARTED.md geofractal router architecture is in prototype phases; https://github.com/AbstractEyes/geofractal This is likely one of it's final growing phases before full production capacity is ramped up. The architecture is not for the novice, it's meant for experts to either get ideas, borrow code, utilize library capacity, or simply tell AI what to do. MOST files in current production have good descriptions for AI integration. Transfer learning notebook available here; https://github.com/AbstractEyes/geofractal/blob/main/src/geofractal/router/Router_Transfer_Learning-12_19_25.ipynb Stress test and multiple diagnostics available here; https://github.com/AbstractEyes/geofractal/blob/main/src/geofractal/router/components/diagnostics/ WideRouter compilation capacity available; https://github.com/AbstractEyes/geofractal/blob/main/src/geofractal/router/wide_router.py The wide router compiler organizes similar towers into stacked staged combinations before compiling with torch.compile. This is experimental, but has shown increased speed with multiple structures of wide models and will serve it's purpose in the future.
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