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| <title>My static Space</title> | |
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| <div class="description"> | |
| <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> | |
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| <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> | |
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