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README.md

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+**KVQuant** is a methodology for efficient KV cache quantization that incorporates several innovations to acheive accurate low-precision quantization, 
+thereby enabling efficient long context length inference.
+
+**TLDR:** KVQuant addresses the memory bottleneck with long context length inference by quantizing the KV cache to low precision.
+KVQuant achieves high accuracy with low-precision KV cache quantization by considering several consistent patterns observed in cached KV values across different LLMs, 
+and by developing methods to exploit these patterns, including:
+
+- **Per-channel, Pre-RoPE** Key quantization to better match the outlier channels in Keys
+- Non-Uniform Quantization (**NUQ**) to better represent the non-uniform activations
+- **Dense-and-Sparse Quantization** to mitigate the impacts of numerical outliers on quantization difficulty
+- **Q-Norm** to mitigate distribution shift at ultra low precisions (eg. 2-bit)
+- **Attention-Sink Aware Quantization** to avoid quantization error with the first token, which is disproportionately sensitive to quantization error
+
+For more details please check out our [paper](https://arxiv.org/abs/2401.18079.pdf).
+
+## Model description
+
+Quantizer file for running DBRX with 2-bit KV cache using KVQuant. 
+
+* **Base Model:** [DBRX-Instruct](https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm)
+* **Bitwidth:** 2-bit
+* **Sparsity Level:** 1%
+
+## Links
+
+* **Paper**: [https://arxiv.org/abs/2401.18079.pdf](https://arxiv.org/abs/2401.18079.pdf)
+* **Code**: [https://github.com/SqueezeAILab/KVQuant](https://github.com/SqueezeAILab/KVQuant) 
+
 ---
 license: mit
----
+---