AbstractPhil/thought-symbolic-image-captions
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AbstractPhil
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updated a dataset about 6 hours ago
AbstractPhil/thought-symbolic-image-captions repliedto their post about 10 hours ago
nano-castor and nano-pollux - (4 variants total) are training complete for the story versions. https://huggingface.co/AbstractPhil/alephllm-nano-castor-pollux
Trained with an identical dataset until framing as the mini-beatrix-2s variants, and finetuned for context and framing as an expected behavior.
The context windows and framing show the models can capture meaningful storage, read, copy, and recall capacity at 4096 byte depth. They aren't particularly smart, but they are functional, and even capable at driving Anima sliders in a meaningfully similar way as the mini-beatrix-3 variant can.
https://huggingface.co/AbstractPhil/alephllm-nano-castor-pollux/blob/main/nano-pollux-dual-v1.1-mx-cpx2/v1_boundaries.json
Anima sliders often do not have a bi-directional capacity, so the multi-slider arrays were created. Thus allowing the model to rebound naturally as expected when necessary, but this still isn't strong enough.
Up next is the full frame trained variant of the champion; nano-pollux-dual-hub and nano-castor. Both of them are the remaining champions. Neither canon models showed similar capacity as the dual-hub's capacity yielded, so we have our champions.
Before continuing to the micro variant, these two will be retrained and issued a full education in art. Everything from color theory, to structural awareness, to semantic relations, techniques, and everything between.
Our little nano twins here, will be a cooperative pair meant for the first diffusion edit experiments.
There were quite a few experiments, many yielded interesting potentials and information. The most powerful variant being the beatrix 3 as a conditioner, yet the nano variants showed a similarly powerful control.
https://huggingface.co/AbstractPhil/geolip-beatrix-anima/blob/main/experiments/e071_anima_setting_relay/sheet_1.jpg
Even just untrained regularization training on Anima shows yield. The model is pretty underfitted, which is both helpful and not helpful for this train. repliedto their post about 14 hours ago
nano-castor and nano-pollux - (4 variants total) are training complete for the story versions. https://huggingface.co/AbstractPhil/alephllm-nano-castor-pollux
Trained with an identical dataset until framing as the mini-beatrix-2s variants, and finetuned for context and framing as an expected behavior.
The context windows and framing show the models can capture meaningful storage, read, copy, and recall capacity at 4096 byte depth. They aren't particularly smart, but they are functional, and even capable at driving Anima sliders in a meaningfully similar way as the mini-beatrix-3 variant can.
https://huggingface.co/AbstractPhil/alephllm-nano-castor-pollux/blob/main/nano-pollux-dual-v1.1-mx-cpx2/v1_boundaries.json
Anima sliders often do not have a bi-directional capacity, so the multi-slider arrays were created. Thus allowing the model to rebound naturally as expected when necessary, but this still isn't strong enough.
Up next is the full frame trained variant of the champion; nano-pollux-dual-hub and nano-castor. Both of them are the remaining champions. Neither canon models showed similar capacity as the dual-hub's capacity yielded, so we have our champions.
Before continuing to the micro variant, these two will be retrained and issued a full education in art. Everything from color theory, to structural awareness, to semantic relations, techniques, and everything between.
Our little nano twins here, will be a cooperative pair meant for the first diffusion edit experiments.
There were quite a few experiments, many yielded interesting potentials and information. The most powerful variant being the beatrix 3 as a conditioner, yet the nano variants showed a similarly powerful control.
https://huggingface.co/AbstractPhil/geolip-beatrix-anima/blob/main/experiments/e071_anima_setting_relay/sheet_1.jpg
Even just untrained regularization training on Anima shows yield. The model is pretty underfitted, which is both helpful and not helpful for this train.Organizations
