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README.md

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 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-2-8B](https://huggingface.co/nvidia/Llama3-ChatQA-2-8B)   [Evaluation Data](https://huggingface.co/nvidia/Llama3-ChatQA-2-70B/tree/main/data)   [Training Data](https://huggingface.co/datasets/nvidia/ChatQA2-Long-SFT-data)   [Retriever](https://huggingface.co/intfloat/e5-mistral-7b-instruct)   [Website](https://chatqa2-project.github.io/)   [Paper](https://arxiv.org/abs/2407.14482)
+[Llama3-ChatQA-2-8B](https://huggingface.co/nvidia/Llama3-ChatQA-2-8B)   [Evaluation Data](https://huggingface.co/nvidia/Llama3-ChatQA-2-70B/tree/main/data)   [Training Data](https://huggingface.co/datasets/nvidia/ChatQA2-Long-SFT-data)   [Website](https://chatqa2-project.github.io/)   [Paper](https://arxiv.org/abs/2407.14482)
 
 ## Overview of Benchmark Results
 <!-- Results in [ChatRAG Bench](https://huggingface.co/datasets/nvidia/ChatRAG-Bench) are as follows: -->