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Tinyflux Lailah
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An experiment with tiny transformers and small experts.
AbstractPhila PRO
AbstractPhil
AI & ML interests
datasets, research papers, experimentation, vision, classification, text encoders, tokenization, llms, diffusion, distillation, and more.
Recent Activity
repliedto their post about 7 hours ago
geolip-ryan-spearman, the first dedicated protein observation structure meant to expand the tooling of the observer modeling system and introducing additional introspective analysis to the equation for genetic mutation and abnormality.
https://huggingface.co/AbstractPhil/geolip-esm2_t33_650M_UR50D
This model is based on edm2 33 650m from facebook, assessed with specific benchmarks to be around 50% accurate or so. I'll be improving those numbers by self distillation spectrum. The models will never see the validation data while unfrozen. The full spectrum of training tools are visible.
This is the first self-distillation observer prototype, and it works. Not as rapidly as I had hoped, but it most definitely works. The SVD was the missing piece of geometric solidity required to preserve full rotational behavioral control. The kernel made this possible for rapid iteration, and the first results are coming in.
This inherits much of the functionality from the CLIP_L and CLIP_G memory banks, while benefitting from the advanced research I performed while extracting CaptionBert 5x bert pooled captions for target points.
The primary driving point here is the sheer data size - and the important contributions of that data size to a full construct of geometric aligned data. There is a massive amount of very specific information, all curated, perfectly labeled, and organized in a way that can be... well not so easily accessed, but I did find a few ways in.
This data is highly accurate and forged through life for billions of years. This is what is there, this is what is expected, and I have the tooling - stage by stage, to not only develop a solution for the problem, but to fully contribute to an improved version with minimal hardware requirement for training.
This is real expectation and the results are pouring in hourly, this can improve models beyond a reasonable baseline while preserving the baseline's correctness. updated a dataset about 9 hours ago
AbstractPhil/ryan-spearman-prepared-features repliedto their post about 9 hours ago
geolip-ryan-spearman, the first dedicated protein observation structure meant to expand the tooling of the observer modeling system and introducing additional introspective analysis to the equation for genetic mutation and abnormality.
https://huggingface.co/AbstractPhil/geolip-esm2_t33_650M_UR50D
This model is based on edm2 33 650m from facebook, assessed with specific benchmarks to be around 50% accurate or so. I'll be improving those numbers by self distillation spectrum. The models will never see the validation data while unfrozen. The full spectrum of training tools are visible.
This is the first self-distillation observer prototype, and it works. Not as rapidly as I had hoped, but it most definitely works. The SVD was the missing piece of geometric solidity required to preserve full rotational behavioral control. The kernel made this possible for rapid iteration, and the first results are coming in.
This inherits much of the functionality from the CLIP_L and CLIP_G memory banks, while benefitting from the advanced research I performed while extracting CaptionBert 5x bert pooled captions for target points.
The primary driving point here is the sheer data size - and the important contributions of that data size to a full construct of geometric aligned data. There is a massive amount of very specific information, all curated, perfectly labeled, and organized in a way that can be... well not so easily accessed, but I did find a few ways in.
This data is highly accurate and forged through life for billions of years. This is what is there, this is what is expected, and I have the tooling - stage by stage, to not only develop a solution for the problem, but to fully contribute to an improved version with minimal hardware requirement for training.
This is real expectation and the results are pouring in hourly, this can improve models beyond a reasonable baseline while preserving the baseline's correctness.Organizations
