| **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 65B model using SqueezeLLM. More details can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf). | |
| * **Base Model:** [LLaMA 65B](https://arxiv.org/abs/2302.13971) | |
| * **Bitwidth:** 4-bit | |
| * **Sparsity Level:** 0.05% | |
| ## 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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