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NVIDIA has introduced ChatQA 2, a Llama3-70B-based model designed to bridge the gap between open-access large language models (LLMs) and leading proprietary models such as GPT-4-Turbo. ChatQA 2 features a 128K context window, enhancing its long-context understanding and Retrieval-Augmented Generation (RAG) capabilities. The model aims to achieve comparable performance to proprietary models in various tasks. NVIDIA's initiative includes a training recipe to effectively extend the context window, highlighting a significant advancement in the field of open-access LLMs, potentially challenging the dominance of proprietary models.
Nvidia presents ChatQA 2 Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities In this work, we introduce ChatQA 2, a Llama3-based model designed to bridge the gap between open-access LLMs and leading proprietary models (e.g., GPT-4-Turbo) in long- https://t.co/JRksZycSBe
NVIDIA proposes ChatQA 2, a Llama3-based model for enhanced long-context understanding and RAG capabilities. The idea is to bridge the gap between open-access and leading proprietary models like GPT-4-Turbo. Presents a training recipe to effectively extend the context window… https://t.co/5WYaYgtq4o
Introducing ChatQA 2, a Llama3-based model with a 128K context window, designed to close the gap between open LLMs and leading proprietary models like GPT-4-Turbo in both long-context and RAG capabilities. The long-context capability of LLMs is sometimes viewed as a rival to… https://t.co/N8reJv9E7u