Update README.md

README.md

@@ -16,7 +16,7 @@ tags:
 # Model Overview
 
 ## Description:
-The NVIDIA Qwen3-Coder-480B-A35B-Instruct NVFP4 model is the quantized version of Alibaba's Qwen3-Coder-480B-A35B-Instruct model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check [here](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct). The NVIDIA Qwen3-Coder-480B-A35B-Instruct FP4 model is quantized with [TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer).
+The NVIDIA Qwen3-Coder-480B-A35B-Instruct NVFP4 model is the quantized version of Alibaba's Qwen3-Coder-480B-A35B-Instruct model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check [here](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct). The NVIDIA Qwen3-Coder-480B-A35B-Instruct FP4 model is quantized with [Model Optimizer](https://github.com/NVIDIA/Model-Optimizer).
 
 This model is ready for commercial/non-commercial use.  <br>
 
@@ -98,7 +98,7 @@ The model is quantized with nvidia-modelopt **v0.41.0**  <br>
 **Test Hardware:** B200 <br>
 
 ## Post Training Quantization
-This model was obtained by quantizing the weights and activations of Qwen3-Coder-480B-A35B-Instruct to FP4 data type, ready for inference with TensorRT-LLM. Only the weights and activations of the linear operators within transformer blocks are quantized. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 3.3x.
+This model was obtained by quantizing the weights and activations of Qwen3-Coder-480B-A35B-Instruct to FP4 data type, ready for inference with TensorRT-LLM. Only the weights and activations of the linear operators within transformer blocks are quantized. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 3.5x.
 
 ## Usage
 
@@ -150,7 +150,7 @@ The accuracy benchmark results are presented in the table below:
    </td>
   </tr>
   <tr>
-   <td>BF16 (AA Ref)
+   <td>BF16
    </td>
    <td>0.486
    </td>
@@ -168,6 +168,8 @@ The accuracy benchmark results are presented in the table below:
   <tr>
 </table>
 
+> Baseline: [Qwen/Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct).
+> Benchmarked with temperature=0.0, top_p=1.0e-05, max num tokens 16384
 
 
 ## Ethical Considerations