Upload README.md with huggingface_hub

README.md

@@ -36,7 +36,7 @@ Global <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 [TBD] via https://huggingface.co/nvidia/GLM-4.7-NVFP4 <br>
+Huggingface 03/25/2026 via https://huggingface.co/nvidia/GLM-4.7-NVFP4 <br>
 
 ## Model Architecture:
 **Architecture Type:** Transformers  <br>
@@ -53,7 +53,7 @@ Huggingface [TBD] via https://huggingface.co/nvidia/GLM-4.7-NVFP4 <br>
 **Output Type(s):** Text <br>
 **Output Format:** String <br>
 **Output Parameters:** 1D (One-Dimensional): Sequences <br>
-**Other Properties Related to Output:** N/A <br>
+**Other Properties Related to Output:** None <br>
 
 Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br>
 
@@ -78,10 +78,11 @@ The model version is NVFP4 1.0 version and is quantized with nvidia-modelopt **v
 ** Link: [cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail), [Nemotron-Post-Training-Dataset-v2](https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v2) <br>
 ** Data Collection Method by dataset: Automated. <br>
 ** Labeling method: Automated. <br>
-** Properties: The cnn_dailymail dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. <br>
+** Properties: The cnn_dailymail dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail.<br> Nemotron-Post-Training-Dataset-v2 dataset is NVIDIA’s post-training dataset with an extension of SFT and RL data into five target languages: Spanish, French, German, Italian and Japanese. The data supports improvements of math, code, general reasoning, and instruction following capabilities. <br>
 
 ## Training Dataset:
-** Data Modality: Undisclosed <br>
+** Data Modality: Text <br>
+** Text Training Data Size: Undisclosed <br>
 ** Data Collection Method by dataset: Undisclosed <br>
 ** Labeling Method by dataset: Undisclosed<br>
 ** Properties: Undisclosed
@@ -129,10 +130,6 @@ The accuracy benchmark results are presented in the table below:
    </td>
    <td><strong>AIME 2025</strong>
    </td>
-   <td><strong>tau2_bench_telecom</strong>
-   </td>
-   <td><strong>ns_aa_lcr</strong>
-   </td>
   </tr>
   <tr>
    <td>FP8
@@ -147,10 +144,6 @@ The accuracy benchmark results are presented in the table below:
    </td>
    <td>0.960
    </td>
-   <td>0.982
-   </td>
-   <td>0.653
-   </td>
   </tr>
   <tr>
    <td>NVFP4
@@ -165,10 +158,6 @@ The accuracy benchmark results are presented in the table below:
    </td>
    <td>0.960
    </td>
-   <td>0.968
-   </td>
-   <td>0.640
-   </td>
   </tr>
 </table>