Important; This new Aurora lineup under AuroraAI-Research, and is the replacement to the inefficient and old ones on my profile, expect more sizes coming soon.
This tiny language model has exactly 80 thousand parameters, and a large (relative to its parameter count) 4096 vocab size using a factorized vocabulary representation.
The benchmark scores are the following:
Wikitext-2 BPB: 3.2902
BLiMP: 52.31%
Arc-Easy: 26.05%
The model was trained on around 40M tokens of fineweb-edu filtered to an educational score of 4 and above, for 2 epochs, 80 million effective tokens.
The training hardware used was the Xiaomi 14T Pro, pinned to its 4 cortex-X4 CPU cores, the time taken was around 6 hours including the preprocessing, tokenizer training.
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