Update README.md

README.md

@@ -10,12 +10,12 @@ base_model:
   <img src="preview-banner.png" alt="Nemotron Slide" width="100%">
 </p>
 
-# Nemotron Slide, a Nemotron Specialized in Slide Generation
+# NemoSlides, a Nemotron Specialized in Slide Generation
 
 
-**Nemotron Slide** is a post-trained hybrid architecture language model built on [NVIDIA-Nemotron-3-Nano-30B-A3B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16) by NVIDIA Corporation. It underwent supervised fine-tuning (SFT) using [Nemo Automodel](https://github.com/NVIDIA-NeMo/Automodel)
+**NemoSlides** is a post-trained hybrid architecture language model built on [NVIDIA-Nemotron-3-Nano-30B-A3B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16) by NVIDIA Corporation. It underwent supervised fine-tuning (SFT) using [Nemo Automodel](https://github.com/NVIDIA-NeMo/Automodel)
 
-**Nemotron Slide** is purpose-built to generate high-quality, aesthetic slides from a single instruction.
+**NemoSlides** is purpose-built to generate high-quality, aesthetic slides from a single instruction.
 
 ---
 ## Model Summary
@@ -54,7 +54,7 @@ pip install transformers torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
-model_name = "trillionlabs/nemotron-slide"
+model_name = "trillionlabs/NemoSlides"
 
 tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(
@@ -82,9 +82,9 @@ print(tokenizer.decode(output[0][input_ids.shape[-1]:], skip_special_tokens=True
 We recommend deploying the model with the lastest version of [vLLM](https://github.com/vllm-project/vllm).
 
 ```bash
-wget https://huggingface.co/trillionlabs/nemotron-slide/blob/main/nano_v3_reasoning_parser.py
+wget https://huggingface.co/trillionlabs/NemoSlides/blob/main/nano_v3_reasoning_parser.py
 
-vllm serve trillionlabs/nemotron-slide \
+vllm serve trillionlabs/NemoSlides \
   --tensor-parallel-size 1 \
   --port 8000 \
   --trust-remote-code \