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+**SqueezeLLM** is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving.
+
+**TLDR:** Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization. 
+But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method. 
+Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance, 
+as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach, 
+we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality. 
+For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf).
+
+
+## Model description
+
+4-bit quantized LLaMA 30B model using SqueezeLLM. More details can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf). 
+
+* **Base Model:** [LLaMA 30B](https://arxiv.org/abs/2302.13971)
+* **Bitwidth:** 4-bit
+* **Sparsity Level:** 0% (dense-only)
+
+## Links
+
+* **Paper**: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf)
+* **Code**: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM) 
+
+
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 license: other
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