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AbstractPhil
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https://civitai.com/user/AbstractPhila

AI & ML interests

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

Recent Activity

replied to their post about 1 hour ago
geolip-captionbert-8192 This bert is currently being distilled using 5 bert teachers using the conceptual captions dataset. The recall accuracy is based on the whitened procrustes alignment, and the losses reflect keeping that rotation aligned correctly. The expectation from the smaller prototypes show this model will align to 100% accuracy recall based on the most optimal opinions based on the correct answer, aligning specifically to the correct answers in conjunction with all the geometric losses. No joke, this may be the smallest, least computation, most accurate, and fastest bert I've trained thus far - and it will be based entirely on five teachers simultaneously feeding opinions through a relay hub.
replied to their post about 2 hours ago
geolip-captionbert-8192 This bert is currently being distilled using 5 bert teachers using the conceptual captions dataset. The recall accuracy is based on the whitened procrustes alignment, and the losses reflect keeping that rotation aligned correctly. The expectation from the smaller prototypes show this model will align to 100% accuracy recall based on the most optimal opinions based on the correct answer, aligning specifically to the correct answers in conjunction with all the geometric losses. No joke, this may be the smallest, least computation, most accurate, and fastest bert I've trained thus far - and it will be based entirely on five teachers simultaneously feeding opinions through a relay hub.
replied to their post about 3 hours ago
geolip-captionbert-8192 This bert is currently being distilled using 5 bert teachers using the conceptual captions dataset. The recall accuracy is based on the whitened procrustes alignment, and the losses reflect keeping that rotation aligned correctly. The expectation from the smaller prototypes show this model will align to 100% accuracy recall based on the most optimal opinions based on the correct answer, aligning specifically to the correct answers in conjunction with all the geometric losses. No joke, this may be the smallest, least computation, most accurate, and fastest bert I've trained thus far - and it will be based entirely on five teachers simultaneously feeding opinions through a relay hub.
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