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Fine-Tuning LLMs to 1.58bit
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As Large Language Models (LLMs) grow in size and complexity, finding ways to reduce their computational and energy costs has become a critical challenge. Smoll LLm fine-tuning experiment with & without warmup quantization BitNet is effective in delivering strong performance compared to baseline methods, especially at lower bit levels. We also extend our thanks to Omar Sanseviero and Pedro Cuenca for their contributions in refining this blog post, helping to communicate our findings clearly and effectively to the AI community.
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