AbstractPhil
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AI & ML interests
datasets, research papers, experimentation, vision, classification, text encoders, tokenization, llms, diffusion, distillation, and more.
Recent Activity
repliedto their post about 12 hours ago Mini-Beatrix-2s pretraining is ready.
https://huggingface.co/AbstractPhil/mini-beatrix-2s
The model passed a great deal of rigor and hardship, trained roughly 16 billion tokens or so. The full writeup for the model including the arms training for the first version arms and the second version arms will be drafted and prepared as soon as the v2 arms are done training and testing.
There are many possibilities present with such a model. The hub itself has been marked capable of potentially operating as similarity comparison, 87% of the capacity retained within a 256 dim structure. Along with this, the multi-dimensional hub attention system shows serious promise with controlling diffusion model inference, which I look forward to see the results of.
Additionally, sentence similarity, next token prediction, and a large array of prediction formats have been heavily improved by introducing the full model with splat attention. The model not only improved, the structure complemented everything measured, along with the more effective training regiment for version 2.
Beatrix 2s is essentially an autoregression decoder, however the attention mechanism houses a dual-stage encoder/decoder structure internally. Each adopting the SVAE as a core component, revamped and fitted to the exact rules of AlephLM. So there are essentially 20 SVAE in this structure, each with their own independent encoders, residually learning from the last.
Upcoming tests will include finetunes to bring out the strengths of all special tokens, presented in the upcoming article. The full experiment battery will be completed within a few days and the findings presented.
Modularization, compartmentalization, secularized behavior, and everything between are to be tested with rigor. This model is a rapid learner, there will likely be byproduct problems with that, and I look forward to solving the corewise problems one at a time until the model is strong enough to be useful for all the tested tasks.
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view article Raising Beatrix: A Byte-Level Model's Measured Childhood
AbstractPhil
• view article Agreement, Anchors, Addresses: A Week of Geometric Training
AbstractPhil
• published an article about 1 month ago view article Geometric Memory FT4 — Distill Against a Consensus, Ship a Rotation
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published an article about 1 month ago view article The Loss Manifest: A Field History of Objective Functions, and What a Machine Can Actually Be Asked to Compute
published an article about 2 months ago view article Aleph Differentiation, Parts 3 & 3-D: Two Laws, Five Days, One Framework
published an article about 2 months ago view article The Aleph Moves Into a Pretrained Trunk: Relays, Registers, and the Two-Regime Dispatch Law
published an article about 2 months ago view article The Aleph Under Autoregressive Pressure: Bottleneck Priors, Sign Codes, and the Consumption Law
view article Subject Bucketing: Teaching a Diffusion Model New Prompt Languages Without Forgetting
AbstractPhil
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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
AbstractPhil
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view article The Polygonal Omega: Trained Sphere-Solvers Are Projective Codebooks
AbstractPhil
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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