AbstractPhil
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AI & ML interests
datasets, research papers, experimentation, vision, classification, text encoders, tokenization, llms, diffusion, distillation, and more.
Recent Activity
posted an update about 8 hours ago The upcoming AlephLM LLM prototype "Mini-Beatrix" is based on protocols, rules, and laws established through the process of training AlephLM systems. This will be a first attempt at a smaller full pretrain/finetune of the AlephLLM on raw data, and this will require over a billion unigram tokens.
Mini-Beatrix will inherit an appropriately adapted AlephLM MOE structure containing a multitude of trained experts, a gating system, a long context RoPE system, MHA attention, and a series of hypothesis to answer upon Mini-Beatrix's pretrain and finetune completion.
While focusing on resolving corruptions and invalidity possibly present in the splat attention, the solutions raised SDPA attention protocol token recall ceiling from 0.91 to 0.993. With that the splat attention raised from 0.81 to 0.89~ splat being around 3x the speed is still imperfect.
So far so good. The corruptions have resolved multiple core component overlapping problems causing the AlephLM's inability to handle the trigram system, the structure of the SVAE having faulty trigram structures, and additionally a multitude of other systems in the lineup that were inheriting the corruptions from the core experiment sets.
These corruptions resolved show that the accuracy of standard multiheaded attention will provide the necessary token recall for full LM capacity, and with that If and WHEN I solve the Rorschach Splat attention will be the faster alternative at >=r1 0.99%, only then. The splat attention's considerably larger head count still contains unresolved inconsistencies.
That being said the SDPA MHA attention will be present for the first attempted mini-llm train, which will be named "Mini-Beatrix" with the appropriate sizing associated with this.
The only thing that will change Mini-Beatrix's trajectory will be if Splat attention is perfected between today and next week, which will likely take longer unless I run into a core corruption that has been overlooked through hundreds of analysis. View all activity Organizations
view article Agreement, Anchors, Addresses: A Week of Geometric Training
AbstractPhil
• view article Geometric Memory FT4 — Distill Against a Consensus, Ship a Rotation
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view article The Loss Manifest: A Field History of Objective Functions, and What a Machine Can Actually Be Asked to Compute
AbstractPhil
• view article Aleph Differentiation, Parts 3 & 3-D: Two Laws, Five Days, One Framework
AbstractPhil
• view article The Aleph Moves Into a Pretrained Trunk: Relays, Registers, and the Two-Regime Dispatch Law
AbstractPhil
• published an article about 1 month ago view article The Aleph Under Autoregressive Pressure: Bottleneck Priors, Sign Codes, and the Consumption Law
published an article about 2 months ago view article Subject Bucketing: Teaching a Diffusion Model New Prompt Languages Without Forgetting
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view article geolip-aleph-void: The First Relational Geometric Vocabulary Patchwork
view article Reading the Voids: Topological Contribution Signals in Frozen Geometric Codebooks
view article Fused Batched Thin SVD, Part II: Extending the Jacobi Pipeline to N=6 with Configurable Convergence
view article H2 Omega Confirmed, Paradigm Shift: Attempting to Disprove Omega As A Whole
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view article The Polygonal Omega: Trained Sphere-Solvers Are Projective Codebooks
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view article Three Geometric Bands in a Sphere-Normalized Patch Autoencoder
view article The Geometric Engine: Structural Attractors in Neural Network Weight Space
view article FL Hybrid Eigendecomposition Beating cuSOLVER's Mathematical Purity with Compilable PyTorch
view article Ryan Spearman: Geometric Variant Effect Prediction Through Quaternion-Composed Dual Expert Alignment
view article Fused Batched Thin SVD: Engineering a 5000× Speedup with Triton Kernels
view article A geometric encoder's toolkit: deterministic primitives for hyperspherical image encoding