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README.md

 # AutoAWQ
 
-AutoAWQ is a package that implements the Activation-aware Weight Quantization (AWQ) algorithm for quantizing LLMs. AutoAWQ will speed up your LLM by at least 2x compared to FP16. AutoAWQ was created and improved upon from the [original work](https://github.com/mit-han-lab/llm-awq) from MIT.
+<p align="center">
+| <a href="https://github.com/casper-hansen/AutoAWQ/issues/32"><b>Roadmap</b></a> | <a href="https://github.com/casper-hansen/AutoAWQ/tree/main/examples"><b>Examples</b></a> | <a href="https://github.com/casper-hansen/AutoAWQ/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22"><b>Issues: Help Wanted</b></a> |
+</p>
 
-Roadmap:
+AutoAWQ is an easy-to-use package for 4-bit quantized models. AutoAWQ speeds up models by 2x while reducing memory requirements by 3x compared to FP16. AutoAWQ implements the Activation-aware Weight Quantization (AWQ) algorithm for quantizing LLMs.  AutoAWQ was created and improved upon from the [original work](https://github.com/mit-han-lab/llm-awq) from MIT.
 
-- [x] Publish pip package
-- [ ] Refactor quantization code
-- [ ] Support more models
-- [ ] Optimize the speed of models
+*Latest News* 🔥
+- [2023/09] Multi-GPU support, bug fixes, and better benchmark scripts available
+- [2023/08] PyPi package released and AutoModel class available
 
 ## Install
 
@@ -62,12 +63,11 @@ The detailed support list:
 | Falcon   | 7B/40B                      |
 | OPT      | 125m/1.3B/2.7B/6.7B/13B/30B |
 | Bloom    | 560m/3B/7B/                 |
-| LLaVA-v0 | 13B                         |
 | GPTJ     | 6.7B                        |
 
 ## Usage
 
-Below, you will find examples for how to easily quantize a model and run inference.
+Below, you will find examples of how to easily quantize a model and run inference.
 
 ### Quantization