update README

README.md

@@ -13,7 +13,7 @@ tags:
 
 
 ## Model Details
-We introduce Llama3-ChatQA-2, which bridges the gap between open-source LLMs and leading proprietary models (e.g., GPT-4-Turbo) in long-context understanding and retrieval-augmented generation (RAG) capabilities. Llama3-ChatQA-2 is developed using an improved training recipe from [ChatQA-1.5 paper](https://arxiv.org/pdf/2401.10225), and it is built on top of [Llama-3 base model](https://huggingface.co/meta-llama/Meta-Llama-3-70B). Specifically, we continued training of Llama-3 base models to extend the context window from 8K to 128K tokens, along with a three-stage instruction tuning process to enhance the model’s instruction-following, RAG performance, and long-context understanding capabilities. Llama3-ChatQA-2 has two variants: Llama3-ChatQA-2-8B and Llama3-ChatQA-2-70B. Both models were originally trained using [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), we converted the checkpoints to Hugging Face format. **For more information about ChatQA, check the [website](https://chatqa-project.github.io/)!**
+We introduce Llama3-ChatQA-2, which bridges the gap between open-source LLMs and leading proprietary models (e.g., GPT-4-Turbo) in long-context understanding and retrieval-augmented generation (RAG) capabilities. Llama3-ChatQA-2 is developed using an improved training recipe from [ChatQA-1.5 paper](https://arxiv.org/pdf/2401.10225), and it is built on top of [Llama-3 base model](https://huggingface.co/meta-llama/Meta-Llama-3-70B). Specifically, we continued training of Llama-3 base models to extend the context window from 8K to 128K tokens, along with a three-stage instruction tuning process to enhance the model’s instruction-following, RAG performance, and long-context understanding capabilities. Llama3-ChatQA-2 has two variants: Llama3-ChatQA-2-8B and Llama3-ChatQA-2-70B. Both models were originally trained using [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), we converted the checkpoints to Hugging Face format. **For more information about ChatQA 2, check the [website](https://chatqa2-project.github.io/)!**
 
 ## Other Resources
 [Llama3-ChatQA-2-70B](https://huggingface.co/nvidia/Llama3-ChatQA-2-70B)   [Evaluation Data](https://huggingface.co/datasets/nvidia/ChatRAG-Bench)   [Training Data](https://huggingface.co/datasets/nvidia/ChatQA2-Training-Data)   [Retriever](https://huggingface.co/intfloat/e5-mistral-7b-instruct)   [Website](https://chatqa2-project.github.io/)   [Paper](https://arxiv.org/abs/2407.14482)
@@ -22,7 +22,7 @@ We introduce Llama3-ChatQA-2, which bridges the gap between open-source LLMs and
 Results in [ChatRAG Bench](https://huggingface.co/datasets/nvidia/ChatRAG-Bench) are as follows:
 
 
-![Example Image](example.png)
+![Example Image](overview.png)
 | | ChatQA-2-70B | GPT-4-Turbo-2024-04-09 | Qwen2-72B-Instruct | Llama3.1-70B-Instruct |
 | -- |:--:|:--:|:--:|:--:|
 | Ultra-long (4k) | 41.04 | 33.16 | 39.77 | 39.81 |
@@ -136,3 +136,4 @@ Peng Xu (pengx@nvidia.com), Wei Ping (wping@nvidia.com)
 
 ## License
 The use of this model is governed by the [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](https://llama.meta.com/llama3/license/)
+Also it is Non-Commercial License