RAG-FiT

RAG-FiT is a library designed to enhance LLMs' capability to utilize external information by fine-tuning models with specifically created RAG-enhanced datasets.

CommonProductProgramming\Artificial Intelligence\\Natural Language Processing\

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RAG-FiT is a powerful tool aimed at enhancing large language models (LLMs) through Retrieval-Augmented Generation (RAG) techniques. It assists models in better utilizing external information by creating specialized RAG-enhanced datasets. This library supports the complete workflow from data preparation to model training, inference, and evaluation. Key advantages include modular design, customizable workflows, and support for various RAG configurations. RAG-FiT is open-source and suitable for rapid prototyping and experimentation by researchers and developers.

\Supports data augmentation: Creates RAG-enhanced datasets
including data loading
normalization
aggregation
and retrieval.\
\Efficient training: Trains models using Parameter-Efficient Fine-Tuning (PEFT) techniques.\
\Flexible inference: Supports predictions using trained or untrained LLMs.\
\Diverse evaluation: Provides various RAG-specific evaluation metrics
such as EM
F1

\This product is primarily aimed at researchers and developers
especially those seeking to enhance the performance of LLMs in specific tasks. It is suitable for scenarios that require augmenting model capabilities with external information
such as question-answering systems and text generation.\

\Fine-tuning the PubMedQA dataset with RAG-FiT to enhance model performance in medical question-answering tasks.\
\Models enhanced by RAG-FiT improve accuracy and relevance in news summarization tasks.\
\Using RAG-FiT to fine-tune legal documents
aiding models in better understanding and generating legal content.\

\1. Clone the RAG-FiT repository and install dependencies: run `pip install -e .`.\
\2. Create a RAG-enhanced dataset: Use the `processing.py` script to configure data loading
retrieval
and preprocessing steps.\
\3. Train the model: Use the `training.py` script to select appropriate PEFT techniques for model training.\

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