changing SLM to LLM to be consistent with reasoning model description

README.md

@@ -32,7 +32,7 @@ library_name: transformers
 
 ## Model Overview
 
-The Nemotron 3 Content Safety model is a small language model (SLM) that uses Google’s Gemma-3-4B-it as the base and is fine-tuned by NVIDIA on multimodal and multilingual content-safety related datasets. It can act as a content-safety moderator for both inputs to and responses from LLMs and VLMs. It can be considered an extension of the popular English-only [Llama 3.1 Nemoguard 8b Content Safety](https://huggingface.co/nvidia/llama-3.1-nemoguard-8b-content-safety) and the multilingual [Llama 3.1 Nemotron Safety Guard 8B v3](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Safety-Guard-8B-v3), that evaluate the safety of prompts and responses only for LLMs. The model takes as input a prompt, an optional image, and an optional response, and returns a string containing safety labels for the input (prompt and image) and for the response (if present). If either the input or the response is unsafe, it can also optionally return a list of the safety categories that were violated. The model uses the same safety taxonomy as the [Nemotron 8B Content Safety Dataset v2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0). The model supports 12 languages - English, Arabic, German, Spanish, French, Hindi, Japanese, Thai, Dutch, Italian, Korean and Chinese. 
+The Nemotron 3 Content Safety model is a Large Language Model (LLM) classifier that uses Google’s Gemma-3-4B-it as the base and is fine-tuned by NVIDIA on multimodal and multilingual content-safety related datasets. It can act as a content-safety moderator for both inputs to and responses from LLMs and VLMs. It can be considered an extension of the popular English-only [Llama 3.1 Nemoguard 8b Content Safety](https://huggingface.co/nvidia/llama-3.1-nemoguard-8b-content-safety) and the multilingual [Llama 3.1 Nemotron Safety Guard 8B v3](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Safety-Guard-8B-v3), that evaluate the safety of prompts and responses only for LLMs. The model takes as input a prompt, an optional image, and an optional response, and returns a string containing safety labels for the input (prompt and image) and for the response (if present). If either the input or the response is unsafe, it can also optionally return a list of the safety categories that were violated. The model uses the same safety taxonomy as the [Nemotron 8B Content Safety Dataset v2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0). The model supports 12 languages - English, Arabic, German, Spanish, French, Hindi, Japanese, Thai, Dutch, Italian, Korean and Chinese. 
 
 The model was trained as a LoRA adapter and the weights were merged back into the parent [Gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it) model.