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Breaking the cost-accuracy Pareto frontier, BDH-CQ achieves state-of-the-art efficiency in reasoning tasks with minimal computational expense.
Polish language models get a major efficiency boost: Bielik v3 slashes tokenization overhead with a custom vocabulary, outperforming generic tokenizers.
Achieve a 50% inference speedup on a large language model for European languages by compressing it to 7.35B parameters, while retaining 90% of the original 11B parameter model's performance.
Can a dedicated research program keep a smaller, local LLM competitive against global giants in the rapidly evolving AI landscape?
