fed-rag and RAGLAB

fed-rag
65
Established
RAGLAB
43
Emerging
Maintenance 10/25
Adoption 10/25
Maturity 25/25
Community 20/25
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 17/25
Stars: 141
Forks: 28
Downloads: —
Commits (30d): 0
Language: Python
License: Apache-2.0
Stars: 310
Forks: 35
Downloads: —
Commits (30d): 0
Language: Python
License: MIT
No risk flags
Stale 6m No Package No Dependents

About fed-rag

VectorInstitute/fed-rag

A framework for fine-tuning retrieval-augmented generation (RAG) systems.

This is a framework for developers and machine learning engineers to improve the accuracy and relevance of their Retrieval-Augmented Generation (RAG) systems. It helps fine-tune these systems to produce better responses by integrating external data sources, whether that data is stored centrally or distributed across different locations. Users provide their RAG models and data, and the framework outputs an enhanced RAG system.

Machine Learning Engineering Generative AI Federated Learning Natural Language Processing AI System Optimization

About RAGLAB

fate-ubw/RAGLAB

[EMNLP 2024: Demo Oral] RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation

This project helps researchers and developers evaluate and compare different Retrieval-Augmented Generation (RAG) algorithms for large language models. It takes in various RAG algorithms and benchmark datasets, then outputs comprehensive evaluation results. It is ideal for AI researchers, NLP scientists, and machine learning engineers who need to understand, reproduce, and extend state-of-the-art RAG techniques.

AI research NLP development Generative AI Language model evaluation Information retrieval

Related comparisons

fed-rag and RAG_Techniques fed-rag and FlexRAG fed-rag and RAGHub fed-rag and RAG_Techniques fed-rag and FlexRAG fed-rag and RAGHub

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