BRAG: High-Performance SLMs for RAG Tasks
Cost-Effective and Efficient AI Solutions
Maximalists AI Researcher has developed the BRAG series of small language models (SLMs) to offer high-performance, cost-effective alternatives in AI-driven language processing. These models have been trained at a remarkably low cost, positioning them as efficient and economical solutions in artificial intelligence.
The BRAG models were created to match or exceed the performance of leading models while keeping the training costs minimal. The series includes four models chosen based on their performance in open benchmarks and ability to balance efficiency and capability.
The BRAG models are particularly noteworthy for their performance relative to their size. The strategic selection of models and training methodology allowed Maximalists to optimize performance while managing costs effectively.
The models were evaluated using the ChatRAG-Bench, a benchmark designed to assess conversational QA and RAG capabilities across various document types and question formats. The evaluation metrics included F1-Score and Exact Match Accuracy, which provided insights into the models’ ability to generate precise and contextually relevant responses.
Maximalists plan to enhance BRAG models by improving RAG performance, tabular data handling, and introducing citation generation for better interpretability. They also aim to refine query rewriting techniques to improve search accuracy and relevance.
If you want to evolve your company with AI, stay competitive, and use BRAG for your advantage in redefining your sales processes and customer engagement.
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