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Shibai-700M-Base is a 700M parameter LLaMA-based model pre-trained on 18B tokens of English, math, and Python code, achieving coherent generation despite being under-trained compared to larger models, and notable for its efficient training on a single RTX 5070 Ti GPU.
TabPFN-3, a pre-trained tabular foundation model, was released with support for up to 1 million rows on a single GPU, 10x-1000x faster inference, and a 93% win rate over classical ML in benchmarks.