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AbstractPhila PRO

AbstractPhil

19 6 28

https://civitai.com/user/AbstractPhila

AbstractEyes

AI & ML interests

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

Recent Activity

updated a collection about 5 hours ago

Distillery

posted an update about 7 hours ago

12 day cook for mini beatrix v3 begins. This model's byte input is formatted using a method dubbed atlas input. ETA OCTOBER 2 2026 https://huggingface.co/AbstractPhil/alephllm-mini-beatrix-training/tree/main/mini-beatrix-3 https://github.com/AbstractEyes/geolip-bytelex https://github.com/AbstractEyes/alephllm Upgrades: * 46 billion byte training pipeline up from 16 billion * 32 block depth 376.0M in v3 up from 20 block 237.1M in 2s. * Active aleph head, repaired via the 2s faults and a large series of tests. * Byte atlas gateway router, explained below. * Guaranteed convergence follow-up AMOE arms on pretrain, fused into the final form, trained together over time to increase the collective capacity. * Multi-tokenizer oriented post-training arms distilled from multiple experts; E.G. Qwen 3.8 27b multi-layer teacher/student arms, CLIP big_g, Bert Code, and more. * Special token word implementation via AMOE arms is now tested up to 240 special tokens for routing. Theoretically each can implement it's own sub-arm aka nested commands. E.G; <think><think_symbolic> ... </think_symbolic></think> * Fused words post-training for faster inference. # The Atlas This atlas structure contains the conjoined shape of 12 tokenizers represented in the trigram format. This is used to predict difficulty in the overlaps, as per determined by the average byte overlap measured via corpus text and the compared overlap. This accuracy is only related to difficulty but it provides pre-training difficulty assessment that we will use to test post-training accuracy with it. This will determine if we can precalculate the likelihood of byte difficulty via tokenizer shape in byte form, for the multibyte fusion upcoming arm experiments for v3. The reason for this, is distillation. We need to train Beatrix to behave with multiple tokenizers, and this theory is showing accuracy with v1 and v2, but the 32 block depth of v3 will answer many questions alongside of the structure.

updated a model about 8 hours ago

AbstractPhil/alephllm-mini-beatrix-training

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12 day cook for mini beatrix v3 begins. This model's byte input is formatted using a method dubbed atlas input.

ETA OCTOBER 2 2026

AbstractPhil/alephllm-mini-beatrix-training

https://github.com/AbstractEyes/geolip-bytelex
https://github.com/AbstractEyes/alephllm

Upgrades:
* 46 billion byte training pipeline up from 16 billion
* 32 block depth 376.0M in v3 up from 20 block 237.1M in 2s.
* Active aleph head, repaired via the 2s faults and a large series of tests.
* Byte atlas gateway router, explained below.
* Guaranteed convergence follow-up AMOE arms on pretrain, fused into the final form, trained together over time to increase the collective capacity.
* Multi-tokenizer oriented post-training arms distilled from multiple experts; E.G. Qwen 3.8 27b multi-layer teacher/student arms, CLIP big_g, Bert Code, and more.
* Special token word implementation via AMOE arms is now tested up to 240 special tokens for routing. Theoretically each can implement it's own sub-arm aka nested commands. E.G; <think><think_symbolic> ... </think_symbolic></think>
* Fused words post-training for faster inference.

# The Atlas
This atlas structure contains the conjoined shape of 12 tokenizers represented in the trigram format. This is used to predict difficulty in the overlaps, as per determined by the average byte overlap measured via corpus text and the compared overlap. This accuracy is only related to difficulty but it provides pre-training difficulty assessment that we will use to test post-training accuracy with it. This will determine if we can precalculate the likelihood of byte difficulty via tokenizer shape in byte form, for the multibyte fusion upcoming arm experiments for v3.

The reason for this, is distillation. We need to train Beatrix to behave with multiple tokenizers, and this theory is showing accuracy with v1 and v2, but the 32 block depth of v3 will answer many questions alongside of the structure.