

Ragfoundry
Overview :
RAGFoundry is a library designed to enhance the ability of large language models (LLMs) to utilize external information by fine-tuning models on specially created RAG-augmented datasets. The library facilitates efficient model training using Parameter-Efficient Fine-Tuning (PEFT), allowing users to easily measure performance improvements with RAG-specific metrics. It features a modular design, enabling workflow customization through configuration files.
Target Users :
RAGFoundry is aimed at researchers and developers, particularly those working in the field of natural language processing who require the use of large language models for complex tasks. It is ideal for researchers who need to rapidly prototype and experiment with different RAG setups and configurations.
Use Cases
Researchers fine-tune language models on specific datasets using RAGFoundry to improve the performance of their question-answering systems.
Developers leverage the modularity of RAGFoundry to rapidly integrate it into existing NLP projects.
Educational institutions use RAGFoundry as a teaching tool to help students understand how fine-tuning improves model performance.
Features
Dataset creation: The processing module creates datasets and saves RAG interactions for RAG training and inference.
Training: Efficient training using PEFT, allowing users to train any model on the augmented datasets.
Inference: Generate predictions using either trained or untrained LLMs.
Evaluation: Run evaluations on outputs generated by the inference module, supporting custom metrics.
Modular design: Customize workflows through configuration files for easy expansion and modification.
HF Hub support: Trained models can be pushed to the HF Hub for easy sharing and utilization.
How to Use
1. Clone the RAGFoundry repository to your local environment.
2. Modify the configuration files as needed to customize the processes for dataset creation, training, inference, and evaluation.
3. Run the data processing script `processing.py` to create datasets for training and inference.
4. Use the `training.py` script to train the model.
5. Utilize the `inference.py` script for model inference to generate predictions.
6. Finally, evaluate the generated outputs using the `evaluation.py` script.
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