Update README.md

README.md

@@ -18,15 +18,13 @@ Please refer to our [arXiv paper](https://arxiv.org/abs/2410.14815) for more det
 
 Try this model on [build.nvidia.com](https://build.nvidia.com/nvidia/nemotron-4-mini-hindi-4b-instruct).
 
-For more details about how this model is used for [NVIDIA ACE](https://developer.nvidia.com/ace), please refer to [this blog post](https://developer.nvidia.com/blog/deploy-the-first-on-device-small-language-model-for-improved-game-character-roleplay/) and [this demo video](https://www.youtube.com/watch?v=d5z7oIXhVqg), which showcases how the model can be integrated into a video game. You can download the model checkpoint for NVIDIA AI Inference Manager (AIM) SDK from [here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ucs-ms/resources/nemotron-mini-4b-instruct).
-
 **Model Developer:** NVIDIA 
 
 **Model Dates:** Nemotron-4-Mini-Hindi-4B-Instruct was trained between June 2024 and Oct 2024.
 
 ## License
 
-[NVIDIA Community Model License](https://huggingface.co/nvidia/Nemotron-4-Mini-Hindi-4B-Instruct/blob/main/nvidia-community-model-license-aug2024.pdf)
+Nemotron-4-Mini-Hindi-4B-Instruct is released under the [NVIDIA Open Model License Agreement](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf).
 
 ## Model Architecture
 
@@ -106,7 +104,7 @@ tokenizer  = AutoTokenizer.from_pretrained("nvidia/Nemotron-4-Mini-Hindi-4B-Inst
 messages = [
     {"role": "user", "content": "भारत की संस्कृति के बारे में बताएं।"},
 ]
-pipe = pipeline("text-generation", model="nvidia/Nemotron-4-Mini-Hindi-4B-Instruct")
+pipe = pipeline("text-generation", model="nvidia/Nemotron-4-Mini-Hindi-4B-Instruct", max_new_tokens=128)
 pipe.tokenizer = tokenizer  # You need to assign tokenizer manually
 pipe(messages)
 ```
@@ -151,7 +149,7 @@ NVIDIA believes Trustworthy AI is a shared responsibility and we have establishe
 
 If you find our work helpful, please consider citing our paper:
 ```
-@article{hindiminitron2024,
+@article{hindinemotron2024,
       title={Adapting Multilingual LLMs to Low-Resource Languages using Continued Pre-training and Synthetic Corpus}, 
       author={Raviraj Joshi and Kanishk Singla and Anusha Kamath and Raunak Kalani and Rakesh Paul and Utkarsh Vaidya and Sanjay Singh Chauhan and Niranjan Wartikar and Eileen Long},
       journal={arXiv preprint arXiv:2410.14815},