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As large language models (LLMs) grow, reducing their computational and energy costs via quantization becomes crucial. BitNet, a new transformer architecture from Microsoft Research, drastically cuts computational costs by representing parameters with ternary values (-1, 0, 1) at 1.58 bits per parameter. The post details how existing models, like Llama3, can be fine-tuned using BitNet, achieving efficient performance while maintaining accuracy. The article also covers the implementation, optimization, and benchmarking of custom inference kernels, making LLMs more scalable and practical.

Fine-tuning LLMs to 1.58bit: extreme quantization made easy

<p>So for more efficient LLM:</p>
<ol>
<li>They make matrix multiplication and addition on GPU.</li>
<li>We load LLM to new hardware and use INT8 addition somehow.</li>
</ol>
<p><img src="https://media.daily.dev/image/upload/s--wnphDObF--/f_auto/v1732709150/ugc/content_cd96517b-b412-4876-b941-71f404db1c05" alt="chrome_dSLCS9ozU7"></p>
