Update README.md

README.md

@@ -169,7 +169,7 @@ A large variety of training data was used for the knowledge distillation phase b
 
 The data for the multi-stage post-training phases for improvements in Code, Math, and Reasoning is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model. 
 
-In conjunction with this model release, NVIDIA has released 30M samples of post-training data, as public and permissive. [Llama-Nemotron-Postraining Dataset](https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset-v1)
+In conjunction with this model release, NVIDIA has released 30M samples of post-training data, as public and permissive. Please see [Llama-Nemotron-Postraining-Dataset-v1](https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset-v1).
 
 Distribution of the domains is as follows:
 
@@ -184,17 +184,6 @@ Distribution of the domains is as follows:
 
 Prompts have been sourced from either public and open corpus or synthetically generated. Responses were synthetically generated by a variety of models, with some prompts containing responses for both reasoning on and off modes, to train the model to distinguish between two modes. 
 
-Models that were used in the creation of this dataset:
-
-* Llama-3.3-70B-Instruct  
-* Llama-3.1-Nemotron-70B-Instruct  
-* Llama-3.3-Nemotron-70B-Feedback/Edit/Select  
-* Mixtral-8x22B-Instruct-v0.1  
-* DeepSeek-R1  
-* Qwen-2.5-Math-7B-Instruct  
-* Qwen-2.5-Coder-32B-Instruct  
-* Qwen-2.5-72B-Instruct  
-* Qwen-2.5-32B-Instruct
 
 **Data Collection for Training Datasets:**