Update README.md

README.md

@@ -17,11 +17,11 @@ tags:
   <img src="banner.png" width="80%" alt="NexaQuant" />
 </div>
 
-## Background + Overview 
+## Introduction
 
 DeepSeek-R1 has been making headlines for rivaling OpenAI’s O1 reasoning model while remaining fully open-source. Many users want to run it locally to ensure data privacy, reduce latency, and maintain offline access. However, fitting such a large model onto personal devices typically requires quantization (e.g. Q4_K_M), which often sacrifices accuracy (up to ~22% accuracy loss) and undermines the benefits of the local reasoning model.
 
-We’ve solved the trade-off by quantizing the DeepSeek R1 Distilled model to one-fourth its original size—without losing any accuracy. This lets you run powerful on-device reasoning wherever you are, with no compromises. Tests on an **HP Omnibook AIPC** with an **AMD Ryzen™ AI 9 HX 370 processor** showed a decoding speed of **17.20 tokens per second** and a peak RAM usage of just **5017 MB** in NexaQuant version—compared to only **5.30 tokens** per second and **15564 MB RAM** in the unquantized version—while **maintaining full precision model accuracy.**
+We’ve solved the trade-off by quantizing the DeepSeek R1 Distilled model to one-fourth its original size—without losing any accuracy. Tests on an **HP Omnibook AIPC** with an **AMD Ryzen™ AI 9 HX 370 processor** showed a decoding speed of **17.20 tokens per second** and a peak RAM usage of just **5017 MB** in NexaQuant version—compared to only **5.30 tokens** per second and **15564 MB RAM** in the unquantized version—while NexaQuant **maintaining full precision model accuracy.**
 
 ## How to run locally