Update README.md

README.md

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-title: VPTQ
-emoji: 🌖
+title: VPTQ Demo
+emoji: 🚀
 colorFrom: blue
 colorTo: green
 sdk: static
-pinned: false
+pinned: true
 license: mit
-short_description: Vector Post-Training Quantization (VPTQ) Inference Demo
+short_description: Vector Post Training Quantization Inference Demo
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Vector Post-Training Quantization (VPTQ) is a novel Post-Training Quantization method that leverages Vector Quantization to high accuracy on LLMs at an extremely low bit-width (<2-bit). VPTQ can compress 70B, even the 405B model, to 1-2 bits without retraining and maintain high accuracy.
+
+* Better Accuracy on 1-2 bits, (405B @ <2bit, 70B @ 2bit)
+* Lightweight Quantization Algorithm: only cost ~17 hours to quantize 405B Llama-3.1
+* Agile Quantization Inference: low decode overhead, best throughput, and TTFT
+
+[Github/Codes](https://github.com/microsoft/VPTQ)
+
+[Online Demo](https://huggingface.co/spaces/microsoft/VPTQ)
+
+[Paper](https://arxiv.org/abs/2409.17066)
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+