avnlp/rag-model-training
Training code for advanced RAG techniques - Adaptive-RAG, Corrective RAG, RQ-RAG, Self-RAG, Agentic RAG, and ReZero. Reproduces paper methodologies to fine-tune LLMs via SFT and GRPO for adaptive retrieval, corrective evaluation, query refinement, self-reflection, and agentic search behaviors.
Implements modular training pipelines for six distinct RAG architectures using SFT and reinforcement learning (GRPO), with specialized components like query complexity classifiers, document relevance evaluators, and multi-phase reflection critics. Built on Llama and T5 model families with domain-specific datasets (financial QA, multi-hop reasoning, web search augmentation), enabling fine-grained control over retrieval depth, document filtering, and agentic decision-making. Provides reproducible implementations of peer-reviewed methodologies with configurable training workflows for different RAG pipeline stages.
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6
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2
Language
Python
License
MIT
Category
Last pushed
Mar 05, 2026
Commits (30d)
0
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