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Machine Learning Optimization Lab
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Multilingual data quality classifiers can outperform monolingual ones, but only with careful tuning of the decision boundary, challenging the assumption that scale alone guarantees improved filtering.
Training a 70B parameter open-source LLM on a supercomputer reveals the hidden engineering hurdles and infrastructure adaptations needed to democratize large-scale AI development beyond the private sector.
