Exploring RAFT: The Future of AI with Retrieval-Augmented Fine-Tuning Posted by By admin 0 Min Read Less Error Rates Merging Augmenting fine-tuned development causes RAFT to remarkably improve the accuracy of specialized tasks. Instead, its performance in many benchmarks, such as TorchHub, earned gains of up to 76% against ordinary fine-tuning techniques. admin February 12, 2025 Previous Article Natural Language Processing (NLP) in AI Next Article Convergence Labs Introduces the Large Memory Model (LM2): A Memory-Augmented Transformer Architecture Designed to Address Long Context Reasoning Challenges