Issue #112: Salesforce releases new model for RAG usecases
Issue #112: Salesforce releases new model for RAG usecases
Introducing SFR-RAG
Welcome to Issue #112 of One Minute AI, your daily AI news companion. This issue discusses a recent announcement from Salesforce.
Salesforce AI Research announces SFR-RAG
Salesforce AI Research introduced SFR-RAG, a 9-billion parameter language model designed for Retrieval Augmented Generation (RAG) tasks. It excels in generating contextually faithful answers, effectively handling multi-hop reasoning, and minimizing hallucinations. SFR-RAG also achieves state-of-the-art results in three out of seven benchmarks in the ContextualBench suite. It uses a refined chat template that adds two new roles, Thought and Observation, enhancing the reliability and customization of RAG applications by separating internal reasoning from external contextual information.
SFR-RAG outperforms larger models like GPT-4o and Command-R+, demonstrating superior resilience in handling conflicting or fabricated context documents. Its architecture allows developers to fine-tune the model more efficiently for complex RAG tasks without cumbersome data parsing or token masking. The model is particularly well-suited for scenarios requiring accurate, context-aware responses, ensuring minimal privacy concerns and more user-friendly outputs. SFR-RAG will be accessible via API in the future.
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