Sign up to save your podcasts
Or
Share 【第185期】RAG Foundry：简化RAG的开源框架
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
【第185期】RAG Foundry：简化RAG的开源框架
Seventy3：借助NotebookLM的能力进行论文解读，专注人工智能、大模型、机器人算法方向，让大家跟着AI一起进步。
进群添加小助手微信：seventy3_podcast
备注：小宇宙
今天的主题是：RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented GenerationSummary
The provided document introduces RAG FOUNDRY, an open-source framework designed to streamline the development and evaluation of Retrieval-Augmented Generation (RAG) systems for large language models. This framework integrates data handling, model training, inference, and evaluation into a unified workflow, enabling efficient experimentation with various RAG techniques. The authors demonstrate RAG FOUNDRY's effectiveness by enhancing and fine-tuning models like Llama-3 and Phi-3 on knowledge-intensive tasks, showcasing consistent performance improvements. The paper also compares RAG FOUNDRY to existing tools and outlines its modular architecture, highlighting its flexibility and extensibility for researchers and practitioners working on RAG.
本文介绍了 RAG FOUNDRY，一个开源框架，旨在简化检索增强生成（RAG）系统的开发与评估，专为大型语言模型设计。该框架将数据处理、模型训练、推理和评估整合为一个统一的工作流程，使得在各种 RAG 技术上进行高效实验成为可能。作者通过在知识密集型任务上对 Llama-3 和 Phi-3 等模型进行增强与微调，展示了 RAG FOUNDRY 的有效性，体现了一致的性能提升。文章还将 RAG FOUNDRY 与现有工具进行了比较，并详细阐述了其模块化架构，强调其对从事 RAG 研究与应用的研究人员和实践者的灵活性和可扩展性。
原文链接：https://arxiv.org/abs/2408.02545
View all episodes
By 任雨山Seventy3：借助NotebookLM的能力进行论文解读，专注人工智能、大模型、机器人算法方向，让大家跟着AI一起进步。
进群添加小助手微信：seventy3_podcast
备注：小宇宙
今天的主题是：RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented GenerationSummary
The provided document introduces RAG FOUNDRY, an open-source framework designed to streamline the development and evaluation of Retrieval-Augmented Generation (RAG) systems for large language models. This framework integrates data handling, model training, inference, and evaluation into a unified workflow, enabling efficient experimentation with various RAG techniques. The authors demonstrate RAG FOUNDRY's effectiveness by enhancing and fine-tuning models like Llama-3 and Phi-3 on knowledge-intensive tasks, showcasing consistent performance improvements. The paper also compares RAG FOUNDRY to existing tools and outlines its modular architecture, highlighting its flexibility and extensibility for researchers and practitioners working on RAG.
本文介绍了 RAG FOUNDRY，一个开源框架，旨在简化检索增强生成（RAG）系统的开发与评估，专为大型语言模型设计。该框架将数据处理、模型训练、推理和评估整合为一个统一的工作流程，使得在各种 RAG 技术上进行高效实验成为可能。作者通过在知识密集型任务上对 Llama-3 和 Phi-3 等模型进行增强与微调，展示了 RAG FOUNDRY 的有效性，体现了一致的性能提升。文章还将 RAG FOUNDRY 与现有工具进行了比较，并详细阐述了其模块化架构，强调其对从事 RAG 研究与应用的研究人员和实践者的灵活性和可扩展性。
原文链接：https://arxiv.org/abs/2408.02545