s1-32B revolutionizes AI development by achieving state-of-the-art reasoning performance using just 1,000 training examples and a novel test-time optimization technique called budget forcing. The mode
l demonstrates that efficient, targeted training and smart inference-time techniques can outperform massive models, while remaining fully open-source and accessible to researchers.
Reasons to Read -- Learn:
how to achieve state-of-the-art AI reasoning performance with just 1,000 training examples instead of millions, potentially reducing your model training costs and time by orders of magnitude
budget forcing, a simple yet powerful test-time scaling technique that can improve your AI model's reasoning abilities without any additional training or complex modifications
how to implement and experiment with an open-source model that matches or exceeds proprietary solutions, with complete training possible in just 26 minutes on 16 H100 GPUs
publisher: @sahin.samia
PythonPyTorchHugging FaceCUDAs1-32B
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