Update README.md

README.md

@@ -7,7 +7,7 @@ license: mit
 # Model Overview
 
 ## Description:
-The NVIDIA DeepSeek V3-0324 FP4 model is the quantized version of the DeepSeek AI's DeepSeek V3-0324 model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check [here](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324). The NVIDIA DeepSeek V3-0324 FP4 model is quantized with [TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer).
+The NVIDIA DeepSeek V3-0324 FP4 model is the quantized version of DeepSeek AI's DeepSeek V3-0324 model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check [here](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324). The NVIDIA DeepSeek V3-0324 FP4 model is quantized with [TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer).
 
 This model is ready for commercial/non-commercial use.  <br>
 
@@ -17,6 +17,14 @@ This model is not owned or developed by NVIDIA. This model has been developed an
 ### License/Terms of Use:
 [MIT](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md) 
 
+### Deployment Geography:
+Global <br>
+
+### Use Case: <br>
+Developers looking to take off the shelf pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications. <br>
+
+### Release Date:  <br>
+Huggingface 06/06/2025 via https://huggingface.co/nvidia/DeepSeek-R1-0528-FP4 <br> 
 
 ## Model Architecture:
 **Architecture Type:** Transformers  <br>
@@ -25,18 +33,18 @@ This model is not owned or developed by NVIDIA. This model has been developed an
 ## Input:
 **Input Type(s):** Text <br>
 **Input Format(s):** String <br>
-**Input Parameters:** 1D (One Dimensional): Sequences <br>
+**Input Parameters:** 1D (One-Dimensional): Sequences <br>
 **Other Properties Related to Input:** Context length up to 128K <br>
 
 ## Output:
 **Output Type(s):** Text <br>
 **Output Format:** String <br>
-**Output Parameters:** 1D (One Dimensional): Sequences <br>
+**Output Parameters:** 1D (One-Dimensional): Sequences <br>
 **Other Properties Related to Output:** N/A <br>
 
 ## Software Integration:
 **Supported Runtime Engine(s):** <br>
-* Tensor(RT)-LLM <br>
+* TensorRT-LLM <br>
 
 **Supported Hardware Microarchitecture Compatibility:** <br>
 * NVIDIA Blackwell <br>
@@ -47,21 +55,28 @@ This model is not owned or developed by NVIDIA. This model has been developed an
 ## Model Version(s):
 The model is quantized with nvidia-modelopt **v0.27.0**  <br>
 
-## Datasets:
-* Calibration Dataset: [cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail) <br>
+## Training, Testing, and Evaluation Datasets:
+Data Collection Method by Dataset: Undisclosed
+Labeling Method by Dataset: Undisclosed
+Properties: Undisclosed
+
+
+## Calibration Dataset: 
+** Link: [cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail) <br>
 ** Data collection method: Automated. <br>
-** Labeling method: Unknown. <br>
+** Labeling method: Undisclosed. <br>
+
 * Evaluation Dataset: [MMLU](https://github.com/hendrycks/test)  <br>
-** Data collection method: Unknown. <br>
+** Data collection method: Undisclosed. <br>
 ** Labeling method: N/A. <br>
 
 
 ## Inference:
-**Engine:** Tensor(RT)-LLM <br>
+**Engine:** TensorRT-LLM <br>
 **Test Hardware:** B200 <br>
 
 ## Post Training Quantization
-This model was obtained by quantizing the weights and activations of DeepSeek V3-0324 to FP4 data type, ready for inference with TensorRT-LLM. Only the weights and activations of the linear operators within transformers blocks are quantized. This optimization reduces the number of bits per parameter from 8 to 4, reducing the disk size and GPU memory requirements by approximately 1.6x.
+This model was obtained by quantizing the weights and activations of DeepSeek V3-0324 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 8 to 4, reducing the disk size and GPU memory requirements by approximately 1.6x.
 
 ## Usage
 
@@ -120,4 +135,4 @@ This section compares the accuracy of the original DeepSeek V3-0324 model with o
 
 NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
 
-Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).