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README.md
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@@ -19,7 +19,8 @@ We introduce Llama3-ChatQA-2, which bridges the gap between open-source LLMs and
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[Llama3-ChatQA-2-70B](https://huggingface.co/nvidia/Llama3-ChatQA-2-70B)   [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)
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## Overview of Benchmark Results
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Results in [ChatRAG Bench](https://huggingface.co/datasets/nvidia/ChatRAG-Bench) are as follows:
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[Llama3-ChatQA-2-70B](https://huggingface.co/nvidia/Llama3-ChatQA-2-70B)   [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)
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## Overview of Benchmark Results
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<!-- Results in [ChatRAG Bench](https://huggingface.co/datasets/nvidia/ChatRAG-Bench) are as follows: -->
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We evaluate ChatQA 2 on short-context RAG benchmark (ChatRAG) (within 4K tokens), long context tasks from SCROLLS and LongBench (within 32K tokens), and ultra-long context tasks from In- finiteBench (beyond 100K tokens). Results are shown below.
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