Update README.md

README.md

@@ -42,7 +42,7 @@ The pretraining data has a cutoff date of September 2024.
 
 ## Model Overview
 
-NVIDIA-Nemotron-Nano-12B-v2 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so, albeit with a slight decrease in accuracy for harder prompts that require reasoning. Conversely, allowing the model to generate reasoning traces first generally results in higher-quality final solutions to queries and tasks. The model was compressed further into [nvidia/NVIDIA-Nemotron-Nano-9B-v2](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2).
+NVIDIA-Nemotron-Nano-12B-v2 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so, albeit with a slight decrease in accuracy for harder prompts that require reasoning. Conversely, allowing the model to generate reasoning traces first generally results in higher-quality final solutions to queries and tasks. The model was fine-tuned from [NVIDIA-Nemotron-Nano-12B-v2-Base](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-12B-v2-Base) was further compressed into [NVIDIA-Nemotron-Nano-9B-v2](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2).
 
 The model uses a hybrid architecture consisting primarily of Mamba-2 and MLP layers combined with just four Attention layers. For the architecture, please refer to the [Nemotron-H tech report](https://arxiv.org/abs/2504.03624).
 The model was trained using [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) and [NeMo-RL](https://github.com/NVIDIA-NeMo/RL).