Update README.md

README.md

@@ -23,7 +23,7 @@ DeepSeek-R1 has been making headlines for rivaling OpenAI’s O1 reasoning model
 
 We’ve solved the trade-off by quantizing the DeepSeek R1 Distilled model to one-fourth its original file 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.**
 
-## NexaQuant Use Case Example
+## NexaQuant Use Case Demo
 
 Here’s a comparison of how a standard Q4_K_M and NexaQuant-4Bit handle a common investment banking brain teaser question. NexaQuant excels in accuracy while shrinking the model file size by 4 times.
 
@@ -42,11 +42,11 @@ Right Answer: 1/4
 
 ## Benchmarks
 
-The benchmarks show that NexaQuant’s 4-bit model preserves the reasoning capacity of the original 16-bit model, delivering uncompromised performance in a significantly smaller memory & storage footprint. Model's general capacity is also greatly improved by NexaQuant.
+The benchmarks show that NexaQuant’s 4-bit model preserves the reasoning capacity of the original 16-bit model, delivering uncompromised performance in a significantly smaller memory & storage footprint.
 
 **Reasoning Capacity:**
 <div align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/66abfd6f65beb23afa427d8a/4SSDIqxikgaDbx8V-Q6bf.png" width="80%" alt="Example" />
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/6618e0424dbef6bd3c72f89a/pJzYVGTdWWvLn2MJtsD_d.png" width="80%" alt="Example" />
 </div>
 
 **General Capacity:**
@@ -66,7 +66,7 @@ The benchmarks show that NexaQuant’s 4-bit model preserves the reasoning capac
 | **IFEval - Prom - Loose**  | 30.31      | 25.74             | 28.47             |
 | **IFEval - Prom - Strict** | 27.91      | 25.74             | 25.51             |
 
-## How to run locally
+## 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.
 
@@ -83,6 +83,7 @@ Execute the following command in your terminal:
 nexa run DeepSeek-R1-Distill-Llama-8B-NexaQuant:q4_0
 ```
 
+
 #### Option 2: Using llama.cpp
 
 **Step 1: Build llama.cpp on Your Device**
@@ -98,6 +99,7 @@ Once built, run `llama-cli` under `<build_dir>/bin/`:
     --prompt 'Provide step-by-step reasoning enclosed in <think> </think> tags, followed by the final answer enclosed in \boxed{} tags.' \
 ```
 
+
 #### Option 3: Using LM Studio
 
 **Step 1: Download and Install LM Studio**
@@ -111,9 +113,10 @@ Get the latest version from the [official website](https://lmstudio.ai/).
 3. Once loaded, go to the chat window and start a conversation.
 ---
 
+
 ## What's next
 
-1. This model is built for complex problem-solving, which is why it sometimes takes a long thinking process even for simple questions. We recognize this and are working on improving it in the next update.
+1. This model is built for complex problem-solving, which is why it sometimes takes a long thinking process even for simple questions. We recognized this and are working on improving it in the next update.
   
 2. Inference Nexa Quantized Deepseek-R1 distilled model on NPU
    
@@ -124,3 +127,5 @@ If you liked our work, feel free to ⭐Star [Nexa's GitHub Repo](https://github.
 Interested in running DeepSeek R1 on your own devices with optimized CPU, GPU, and NPU acceleration or compressing your finetuned DeepSeek-Distill-R1? [Let’s chat!](https://nexa.ai/book-a-call)
 
 [Blogs](https://nexa.ai/blogs/deepseek-r1-nexaquant) | [Discord](https://discord.gg/nexa-ai) | [X(Twitter)](https://x.com/nexa_ai)
+
+Join Discord server for help and discussion.