Update README.md

README.md

@@ -8,21 +8,20 @@ tags:
 - llama
 - llama-3
 - meta
+- GGUF
 ---
 
 # DeepSeek-R1-Distill-Llama-8B-NexaQuant
 
 ## Background + Overview 
+
 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 **66.40 tokens per second** and a peak RAM usage of just **1228 MB** in NexaQuant version—compared to only **25.28 tokens** per second and **3788 MB RAM** in the unquantized version—while **maintaining full precision model accuracy.**
 
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-
 ## How to run locally
-NexaQuant is compatible with **Nexa-SDK**, **Ollama**, **LM Studio**, **Llama.cpp**, and any llama.cpp based project. 
 
-Below, we outline multiple ways to run the model locally.
+NexaQuant is compatible with **Nexa-SDK**, **Ollama**, **LM Studio**, **Llama.cpp**, and any llama.cpp based project. Below, we outline multiple ways to run the model locally.
 
 #### Option 1: Using Nexa SDK