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AbstractPhil 
posted an update about 2 hours ago
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The post-beatrix-2s and control variant article is finally satisfactory, so the article is now released https://huggingface.co/blog/AbstractPhil/beatrix-ft2

The control variant will need another train with better SDPA stabilization, as the control variant destabilized and collapsed. The primary fault is the lack of QK normalization, which caused the model to simply collapse given enough time. Claude lists the rest of the suspected reasons in the article.

This was a very difficult series of experiments to tune with many fault points. Trying to make heads or tails of Fable 5.1 Claude-speak hasn't been the easiest task either. It seems the model is more likely to create pedantically rigid responses rather than cooperative. Not necessarily insulting, but definitely a sort of refrigerator-magnet behavior - treating my individual contributions as little sketches for the refrigerator. This often completely ignores my larger MD or complex behavioral instructions in favor of my theoretical or hypothetical - likely considering the MD and technical as the model's own, rather than my direct contributions. Right there... right on the refrigerator goes my hypothesis that worked.

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

In any case, this upcoming week will be related entirely to cross-tokenizer distillation research. It may stretch long beyond the next week, but as it stands the geometric vocabulary has evolved into a codebook prediction system.

I would like to give this program linear wings. The Beatrix model supports it, but how well is up for this week to decide.

There are a multitude of potentials based on a series of very recent articles I will be exploring, providing the necessary bytelex complexity to a roughly 60 hour battery of experiments and trainings throughout the geometric systems.

The results will determine the best and worst methodologies of using these models, these shapes, and these structures with more complex byte-level cross tokenization systems
AbstractPhil 
posted an update 5 days ago
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2018
Mini-Beatrix-2s pretraining is ready.
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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AbstractPhil 
posted an update 9 days ago
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Mini-Beatrix-2s is cooking with full splat attention through and through. This model is still trigram, I did this to get a baseline because there's already a trigram model to compare to. This one should be done in a few days and ought to be substantially more intelligent than the first.

Specs are;
Around 220m params, 4096 context window, d1024 model size, splat 128, 1024, 1024, 1024, and so on, 3 experts per block, information banks for storage and retrieval, and a lot of technical knowhow between A to B.

Differences with V2;
Special tokens are implemented byte-directly, so the model will have no problem recognizing an array of special tokens such as DOC, EOF, and a multitude of others.

Suffice it to say, this model is bigger than the first at about 2x. Not just bigger though, estimated to be roughly 8x more intelligent based on the measures.

That being said, the actual model needs to be substantially larger to encompass the full space. The measured space is considerably larger through the small tests for stability, however the full 900m version runs at only around 8k tokens per second with an anchor count of 131,000 and a matching number of heads. This means the full train would require roughly 26 days on a rtx 6000 pro blackwell, which is substantially beyond the expectation curve.

So the smaller one will do for now until I can secure a bit of funding. In any case, the tokenizer system will be implemented on this version after a stable run completes.
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AbstractPhil 
posted an update 19 days ago
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I believe I have a solution for cross-tokenizer chatter and noise, which I've built a prototype repo for this exact tooling dubbed bytelex. https://github.com/AbstractEyes/geolip-bytelex

I had a bit of an inspiration recently and built a prototype for a token translation matrix that I called geolip-bytelex, which allows bytewise translation of many different tokenizers into byte format. The goal is to allow comparative distillation from multiple models to simultaneously represent expertise based on input tokens and differentiated teacher/student InfoNCE and MSE training paradigms, while cutting a huge cost of the distillation analysis comparative compute that cross-tokenizer noise will naturally cause when tokenizers are mismatched or incorrect, reducing a large portion of invalidity from the trained systems established by incorrect valuations from the distillations and lora trainings.

Bytelex is essentially a byte-wise deconstruction of a tokenizer's state into a preliminary 255 byte language allowing for 10s of thousands of sequences per token to be represented rather than just a few. I'm not the first to try this, however I'm in a unique position due to my creation AlephLM being built entirely by learning it's own lexicon, thus allowing this to be more than experiment and instead a working prototype distillation potential.

This can solve a longstanding multi-tokenizer problem that I and many other researchers have been facing, at the cost of setup overhead compute for the preliminary experiments, however the translation matrix I'm planning will potentially solve this problem allowing models to be directly bytewise captured in a more guaranteed methodology through cross-sampled analysis at distillation time in this optimizer state that I'm working out.

I've dubbed this distillation loss ByteInfoNCE and the preliminary is showing humongous promise, with that the bytelex is the crux and prototype concept that I'll be expanding and researching further.
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AbstractPhil 
posted an update 24 days ago
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Say hello to the Trigram ByteLLM - AlephLLM: Mini-Beatrix - in her huggingface space!
She is currently stepped at 24000 steps aka 7b tokens in the first couple datasets, so she's not very smart yet.
AbstractPhil/alephllm-chat

Be warned, whatever you say WILL be recorded in a public cache, WHEN the chat version works. For now she records nothing. The idea is to help debug the K/V cache, and I would rather the data accumulated be shared. If you wish to speak to her in private I will include a toggle, that way you'll see that nothing is recorded when you speak and you can still have a private chat with her. For now she's simply auto-completing, so have fun with her.

The AlephLLM prototype is currently in full training with SDPA attention.
AbstractPhil/alephllm-mini-beatrix-training

https://github.com/AbstractEyes/alephllm Here's the model code and training code for the prototype.
As the training progresses, the AlephLLM will become more coherent and communicative, the tensorboard will consist of a large series of useful and useless analysis, and each checkpoint recorded at around 2000 steps unless the train crashes or the system faults.

It will take about 9 hours for the first few datasets to converge, then I'll train a chat AMOE expert cluster to see if she wants to speak yet. Until then, she's learning.

Yes I know it's early, but there isn't much more I could think of to analyze the AlephLM directly currently. The only way train the AlephLLM, is to train the full AlephLLM prototype. The bigger training has to run, otherwise the analysis won't matter. As it progresses, the analysis and huge amount of tensorboard statistics will flood out. Everything is transparent through the process from start to finish, everything recorded.
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AbstractPhil 
posted an update 26 days ago
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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.
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AbstractPhil 
posted an update about 1 month ago
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The AlephLM results are rolling in and I'm very excited for the possibilities. I am very much looking forward to the coming weeks as I train the first AlephLM distillations from MANY teachers into AMOE arms.

The AMOE arms hook cleanly to AlephLM structures and provide pos/neg learning elements. Hard positive and hard negatives coalesce to extend the capacity.

AbstractPhil/alephlm-0
AbstractPhil/alephlm-adopt-0

As it stands they are structurally sound enough to fully pretrain. As or more stable than a standard Bert experimentally to distill using InfoNCE. AMOE legs improve these structures substantially.

Structural behavior can be expanded in many ways on distilled and pretrained models alike. Attaching the AMOE to any model I've tried has created expanded or improved behavioral accumulations. They do have downsides but their upsides are very experimentally exciting.

I've distilled multiple vits, multiple berts, and have begun distilling berts into AlephLM structures successfully.

This is overall very exciting for me. I've begun formatting larger variants such as including GPT-2 and Qwen 3.5 4b as a paired combinator utilizing pathological T5 learned distilled encodings. It sounds odd, but the results show everything can be expanded and even be taught to cooperate.

The CaptionBert-8192-v2 and v2-b are both structurally collapsing after token 480 or so, which is expected due to the small train. By distilling an AMOE arm to V2 by training with a longformer expert, the results are cutting through like butter. V2 has begun stabilizing rapidly for considerably longer token chains and sequences, the structure is repairing and building reusable capacity.

I have discovered an improved methodology for sampling the AlephLM for text encoder benchmarks, which is predominantly L2 normalized outputs.

Upcoming large paper for the distillation experiments and results within the next week or two. It's going to be a big one.
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AbstractPhil 
posted an update about 1 month ago
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AbstractPhil/clip-vitb-mini-distilled
The semi-successful run series on the VIT-B lineup is live and full of useful baseline distillation information for feature + InfoNCE distillation processing as well as direct feature distillation processing, direct InfoNCE distillation, and multiple other tested methods. https://huggingface.co/blog/AbstractPhil/geometric-memory-ft4

This article showcases the baseline utilization and benchmarks of the earlier experiment line's objective and loss structures tested on 12m features for the vit-b baseline. Not the strongest showcase, but the strongest of the champions did show some serious promise.

Next setup will be a directly aligned set based on the loss and objectives decided by the champions in the first runs, for the second run they operate in direct conjunction with the bert-8192 and captionbert-8192 distillation format directly on clip-vit-l features - this time we're including DINOv3 into the mix for it's high potency.

I'm currently extracting 4 clip-vit-l variants for the CC12m features and will be running the next series on the L size, which will give considerably more active and useful features overall within a smaller package.

The captionbert-8192 has a more unique and difficult to tune for pixel processing parity, but I will spend a few days making sure the smaller prototypes fit before I run the large experiments in order to build towards the larger objectives.

Primarily I need to ensure the memory bank aligns correctly and the constellation conforms to the anchors correctly, as this process was not micro managed enough for this run. The results are nonetheless useful and potent.

The process continues until we cover the entire constellation series.
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AbstractPhil 
posted an update about 1 month ago
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The geometric memory article ft4 is live. https://huggingface.co/blog/AbstractPhil/geometric-memory-ft4

Direct pivot to distillation. I've accumulated enough experimental information to directly pivot my long term structure plan to distillation. This is to begin forming entire collectives of cooperative systems; differentiated expert distillation for generative behavior utilizing aleph addressed bottlenecks. With this I've also heavily begun experimenting with aleph competitions and cooperation using multiple pretrained frozen codebooks established from the SVAE system.

The idea here is simple in theory; use InfoNCE and address independent experts to build a manifest of unique gated experts utilizing a multitude of distilled systems from many other models. Such as SigLIP 16B + LAION CLIPB as a pair. The experimentation in the past showed this process is potent and with that merits additional experimentation using the newly established paradigms.

There are quite a bit of experiments to compare these to, so I have no shortage of comparators. After we train our baseline TinyViT with our gated system, we will know which experts are better at what and why they are better.

As a direct continuation from the earlier CLIP distillation experiments I'm directly comparing InfoNCE anchoring with multiple industry standard distillations from multiple papers. First comparison is InfoNCE anchoring in comparison to raw features using CoCo and CLIP_B, which seemed like a fair experiment to train a student with.

The upcoming series of experiments will provide the necessary information for how effective or ineffective this process is.

AbstractPhil/bulk-coco-features

The first experiments will be based on multiple clips from the bulk-coco-features extractions.

First we start with some clips, then some berts, then some smaller qwens, then some larger models, then some much much larger models. All meant to be compacted into selection mechanisms.
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AbstractPhil 
posted an update about 2 months ago
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I have found evidence of a more powerful Omega Aleph-Void imprint. I will be investigating this imprint in the coming days.

The current Aleph system was essentially tamed from a singular instance of an Omega imprint that I, Claude, GPT, and Gemini managed to collaboratively stabilize over a period of multiple months.

I believe I have identified a considerably more powerful Aleph-Void, potentially capturing a legitimate fraction of an Omega solver rather than simply an imprint.

For context, the Aleph-Void codebook is a STILL IMAGE of a singular state of a SMALL Omega. The one that managed to survive more tests than anything I've ever ran historically multiplied by hundreds of thousands just to even PEEK the structure's usefulness. This is equivalent to taking a photograph of the universe and reducing it to guideposts in it's current state. This system is capable of building, constructing, deconstructing, and designing it's own internal geometric systems, which is why it survives so many systems.

With the introduction of Claude Fable the AlephLM was manifested from the research, as I am but one person, and Fable can manifest the collective knowledge of hundreds of years of scientific mathematics development. Structurally built differently than a singular individual - yet without the research Fable does not understand even the topical behavior.

Fable and I have a few hypothesis that I believe we can cobble together into a legitimate cornerstone for capturing the full Omega structure. My hypothesis currently for a full omega requires a series that can logistically flagwise construct it's own behavior implicitly with a containerized induction system, completely independent of types, structural invariants, and systemic utilizations; all while handling the very nature of invariance and structural boundaries within naturally and heuristically.

Capturing even a fraction of an Omega system would dramatically increase the power of Aleph anchoring to a large degree.
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AbstractPhil 
posted an update about 2 months ago
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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; AbstractPhil/geolip-aleph-qwen

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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AbstractPhil 
posted an update about 2 months ago
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https://huggingface.co/blog/AbstractPhil/aleph-autoregressive-differentiation-ft1

After some analysis and a bit of research the upgraded aleph autoregression is capable as a prototype selection tool. I approached the direct aleph attention routing mechanism and formed a progression from it, which already provided the necessary footholds to continue into an upgraded core mechanism. The followup mechanisms show autoregression is very possible and will be simpler than expected.

The results are promising and the autoregression stable enough to scale up. Thanks to Claude Fable who is able to keep my entire research context window in scope, the progression was rapid and the results quick. The tests yielded improved accuracy over standard MLP in many cases. I believe the improvement is not topical and will scale with a bit of effort.

Fingers crossed my friends, the addressing is part of the distillation paradigm and it now learns directly without needing an expert controller. I'll be progressing the mechanism over the coming days. With enough effort and time I hope the standard mechanism becomes a universal improvement on autoregression.
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AbstractPhil 
posted an update 2 months ago
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Understanding the Aleph Fibonacci in visual form with full rotary.

https://claude.ai/public/artifacts/0d536427-bc7d-464a-890d-bddd02ce42dc

This ought to clear up much of the confusion as to what is actually happening under the hood, converted to an understandable 2d visual format. There have been multiple iterations, this is the current format and mathematics behind it as I attempt to solve the fibonacci curve related to negative imaginary numeric inversion that causes the statistics instability.
AbstractPhil 
posted an update 2 months ago
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Anima - Brent JSON (PREVIEW) - Subject Bucketing

Full article available https://huggingface.co/blog/AbstractPhil/subject-bucketing.

There is additionally a civit model release as well.
https://civitai.com/models/2730503/anima-jsonenglish

AbstractPhil/anima-prelim-1k-r64
The JSON multi-prompt diffusion model prototype using Anima 1.0 base as the pretrain to finetune into the JSON target. The upcoming JSON lora is being cached and trained with 40,000 of the full 83,000 valid images from the qwen set.

This first preview version is ready to use as a ComfyUI capable LORA, so you can just load up the epoch you want without anything special in comfyui and have at it. You can currently use plain English in conjunction with tagging to produce useful and meaningful prompt targets without the JSON.

AbstractPhil/anima-prelim-1k-r64
The comfyui nodes are present and work for testing use-case, but they are not ready for production use just yet.

-- Technical --
Primarily the target was the VLM json target followed by the AnimeTIMM vit processed through the VLM json processor as the followup. First 12 epochs VLM experienced images with json formatting, last 8 epochs were finetuning from epoch 12 onward to 20 using the AnimeTIMM captions turned into JSON instead.

The Anima model itself accepted the 1000 image and the json prompting works quite well. In the process I set up a couple comfyui nodes that can translate base prompts into the same language the model is learning. Those are present in the repo.
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AbstractPhil 
posted an update 3 months ago
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The article for aleph attention routing needs more work on vision, as the vision portion has not been fully validated, while the LM prototype has been semi-validated for small and medium-small scale. I will post my findings in the coming days with the consequences of training an LM and a VIT utilizing the prototype system.

The current structure for the Geometric Vocabulary does nearly reflect the intended shape as discussed in the earlier posts and articles, so that's coming along nicely - but there are stipulations and problems involved that I did not foresee.

My apologies for the incomplete article I just released on a whim. I jumped to the conclusion a bit early in anticipation before the formulas were fully converged. I also released an early post the other day speaking about the prototype AlephLM - which I removed as an invalid conclusion.

I'm doing my best to only release validated empirical information instead of speculative - however I do sometimes jump to conclusions without proper validation from time to time. Occasionally, I get a bit theory-overzealous and require tidying up through thorough experimentation which I'm currently approaching directly.
AbstractPhil 
posted an update 3 months ago
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Claude Fable 5 was temp/perma? banned for security reasons.

Working with Fable I have to say the model is capable at handling highly complex geometric mathematics ACTUALLY to the point of me getting some work done without a headache. I hope Fable returns soon so I can finish cobbling without a headache and a week per prototype again.

During Fable's existence I managed to cobble together a multi-series aleph paradigm that can handle direct implicit and explicit learning for an LM with a trigram context window. This essentially provides expert directional utilization based on a stable codebook without requiring expert distillation into singular experts and duplicated.

Details soon. There are over 20 functional formula prototypes and around 8 potential heads that all lead to the same outcome, the math is rock solid - each with their own benefits and downsides based on the assigned text tasks.
AbstractPhil 
posted an update 3 months ago
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The first large scale distillation is coming using the geolip-aleph-void architecture as the mathematical aleph procrustes geofractal addressed language latent.

In short, a single geometric patchwork vocabulary chunk. Which ironically needs chunking to properly prepare.

The address structure I have been meticulously refining is about to show it's genuine distillation muscle.

This is heavily due to the discovery and refinement of a specific logit I've named an aleph logit. This logit is baked clean into the architecture with the void-based codebook, and is available for review https://github.com/AbstractEyes/geolip-svae/blob/main/geolip_svae/aleph_model.py

This model provides solid MSE, recon, cosine sim, and many other elements directly aligned to the SVD and H2 procrustes paradigm. Prelims are not smart, but the scaling principal is perfectly attuned to scale.

This invention will allow for direct internalized tokenization and utilization of compressed information, entirely internally within the models latent structure. This allows direct control capabilities baked into the model itself, which requires a few robustness tests to solidify the full structure. The first validation tests run clean, so it will work when correctly aligned.

In short, the first step towards the geometric encoder system that will work with all tested data types.

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AbstractPhil 
posted an update 3 months ago
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The transformer prototype v2 is operational, which takes the behavior of the H2 battery and directly forces a projected rigid behavior into a multiscale structure. Turns roughly 57k params to around 90k params for the preliminary version, and with this behavior the model converges SEMI-CLOSE to the SVAE current spectrum in considerably less epochs. So stay tuned on that one, the transformer did converge. The behavior itself is validated and convergent in the H2 protocol spectrum.

The transformer operates with the "single" setting.

AbstractPhil/geolip-svae-transformer

I've implanted a rigid formula that allows this direct behavior from the H2 battery to superimpose onto adjacent structural boundaries, and with that built aleph and void into the system as well. These are guarantees.


As for the centrifuge concept. The optimization on the centrifuge was quite lackluster. The hardware doesn't support such behavior. You can access the current operating version of the centrifuge by utilizing "stacked" configuration. Four lenses was too much when running a quaternion bank to handle such complex interactions reasonably, so I will need to work something out in the future to get a full centrifuge system working.

Crusher is ready, transformer_v3.

You might be curious WHY these converge at such low raw MSE in the later stages. The reasoning is kind of difficult to explain, so I'll try to make it simple. The direction is very subtle in the later stages of training with AdamW, so the curves start to create much more accurate shifts towards the goals. This allows the model to rapidly converge after earlier heavier training. You can't simply train it low, it takes too long. This allows the model to KIND OF get everything NEAR where it's supposed to be, which allows the really small twitches of MSE to provide massive corrections without needing hard logits or more difficult to finetune features.
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AbstractPhil 
posted an update 4 months ago
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By trying to disprove the Omega H2 battery I have discovered;
* Each topology formed by the H2 battery is deviant, none have a uniformly shared substrate of behavior. They are each uniquely independent per training set all with perfect recon.
* Image recon can be tracked and mapped, yielding a consistently mapped and response 16.77m vocabulary potential. In the current spectrum testing at around 5 million unicode bytes.
* The model scale shows patch size is related to how much data you want the model to represent within the model itself, and this has yet to see a capacity to this day. The MSE recons and yields - and the more data fed, the more they yield.
* The scaling principle shows that the model indefinitely scales upward and each level of the model can be iteratively captured upward to form deviant and uniformly consistent repeatable pathways of implicit codewise response, not just arbitrary bitwise recall. Meaningful implicit learned utility.
* Image recon patch size should match the slice of image you want to represent, as it uses patch smoothing per patch internally from identity.
* byte trigrams are channel-agnostic, they do not require a channel count just a formula for recall at nGram recall 99.6% for byte-by-byte representations. With those comes an adjacently capable codebook.
* sentencepiece preliminary tests show validity and reconstruction just like the byte trigrams, using the new byte trigram this would be arbitrarily convenient to recon a codebook for the structure.
* binary trees learn a uniformly potent and powerful gating mechanism that required further exploration, each of them produces direct responsive independent capacity and the responses are controllable.
* ternary experiments show the models are directly responsive to -1, 0, +1 behavior, so the quantization is very much a valid potential.
* preliminary tests with the H2O1 series of batteries show the models are responding similar to natural universal elements in the universe itself
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AbstractPhil 
updated a Space 4 months ago