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Private RAG with Open Source and Custom LLMs 🚀 | BentoML | OpenLLM
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In this session, Chaoyu Yang, Founder and CEO at BentoML, talked about the practical considerations of building private Retrieval-Augmented Generation (RAG) applications, utilizing a mix of open source and custom LLMs.
Topics that were covered:
✅ The benefits of self-hosting open source LLMs or embedding models for RAG.
✅ Common best practices in optimizing inference performance for RAG.
✅ BentoML for building RAG as a service, seamlessly chaining language models with various components, including text and multi-modal embedding, OCR pipelines, semantic chunking, classification models, and reranking models.
About LLMOps Space -
LLMOps.Space is a global community for LLM practitioners. 💡📚
The community focuses on content, discussions, and events around topics related to deploying LLMs into production. 🚀
Topics that were covered:
✅ The benefits of self-hosting open source LLMs or embedding models for RAG.
✅ Common best practices in optimizing inference performance for RAG.
✅ BentoML for building RAG as a service, seamlessly chaining language models with various components, including text and multi-modal embedding, OCR pipelines, semantic chunking, classification models, and reranking models.
About LLMOps Space -
LLMOps.Space is a global community for LLM practitioners. 💡📚
The community focuses on content, discussions, and events around topics related to deploying LLMs into production. 🚀
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