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<title>My static Space</title>
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<h2>VPTQ Online Demo</h2>
<p>
<b>VPTQ (Vector Post-Training Quantization)</b> is an advanced compression technique that dramatically reduces the size of large language models such as the 70B and 405B Llama models. VPTQ efficiently compresses these models to 1-2 bits within just a few hours, enabling them to run effectively on GPUs with limited memory.
For more information, visit the following links:
<ul>
<li>
<a href="https://arxiv.org/abs/2409.17066" target="_blank" class="link-styled">
<img src="icons/arxiv-logo.png" alt="arXiv" width="20" height="20" /> <b>Paper on arXiv</b>
</a>
</li>
<li>
<a href="https://github.com/microsoft/VPTQ" target="_blank" class="link-styled">
<img src="icons/github-mark.png" alt="GitHub" width="20" height="20" /> <b>GitHub Repository</b>
</a>
</li>
<li>
<a href="https://huggingface.co/VPTQ-community" target="_blank" class="link-styled">
<img src="icons/hf-logo.png" alt="Hugging Face" width="20" height="20" /> <b>Hugging Face Community</b>
</a>
</li>
</ul>
</p>
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