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DS SERVE: A Framework for Efficient and Scalable Neural Retrieval
arXiv – CS AI|Jinjian Liu, Yichuan Wang, Xinxi Lyu, Rulin Shao, Joseph E. Gonzalez, Matei Zaharia, Sewon Min||6 views
🤖AI Summary
DS-Serve is a new framework that converts massive text datasets (up to half a trillion tokens) into efficient neural retrieval systems. The framework provides web interfaces and APIs with low latency and supports applications like retrieval-augmented generation (RAG) and training data attribution.
Key Takeaways
- →DS-Serve can process half a trillion tokens into high-performance neural retrieval systems.
- →The framework operates with low latency and modest memory overhead on a single node.
- →It offers inference-time trade-offs between latency, accuracy, and result diversity.
- →Primary applications include large-scale RAG, training data attribution, and search agent training.
- →The system provides both web interface and API endpoints for accessibility.
#neural-retrieval#ds-serve#rag#machine-learning#data-processing#ai-framework#scalability#text-datasets#api#inference
Read Original →via arXiv – CS AI
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