Add link to tech report. Fix typo in usage example #2

README.md

@@ -10,7 +10,7 @@ license_link: >-
 
 Llama-3.1-Minitron-4B-Width-Base is a base text-to-text model that can be adopted for a variety of natural language generation tasks.
 It is obtained by pruning Llama-3.1-8B; specifically, we prune model embedding size and MLP intermediate dimension.
-Following pruning, we perform continued training with distillation using 94 billion tokens to arrive at the final model; we use the continuous pre-training data corpus used in Nemotron-4 15B for this purpose. 
+Following pruning, we perform continued training with distillation using 94 billion tokens to arrive at the final model; we use the continuous pre-training data corpus used in Nemotron-4 15B for this purpose. Please refer to our [technical report](https://arxiv.org/abs/2408.11796) for more details.
 
 This model is ready for commercial use.
 
@@ -59,7 +59,7 @@ import torch
 from transformers import AutoTokenizer, LlamaForCausalLM
 
 # Load the tokenizer and model
-model_path = "nvidia/Llama3.1-Minitron-4B-Width-Base"
+model_path = "nvidia/Llama-3.1-Minitron-4B-Width-Base"
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 
 device = 'cuda'
@@ -143,4 +143,6 @@ NVIDIA believes Trustworthy AI is a shared responsibility and we have establishe
 Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). 
 
 ## References
-* [Compact Language Models via Pruning and Knowledge Distillation](https://arxiv.org/abs/2407.14679)
+
+* [Compact Language Models via Pruning and Knowledge Distillation](https://arxiv.org/abs/2407.14679)
+* [LLM Pruning and Distillation in Practice: The Minitron Approach](https://arxiv.org/abs/2408.11796)