What is RAG-FiT?
RAG-FiT is a powerful library designed to improve the performance of Large Language Models (LLMs) by fine-tuning them on Retrieval-Augmented Generation (RAG) datasets. If you're working with LLMs and want them to better leverage external information, RAG-FiT provides a streamlined way to create, train, evaluate, and optimize your models. Whether you're prototyping, experimenting, or deploying, this modular framework simplifies the process, making RAG techniques more accessible and effective.
Key Features
🔧 Create Custom Datasets: Generate RAG-augmented datasets tailored to your needs, including data retrieval, prompt creation, and preprocessing. These datasets are saved in a consistent, model-independent format, ready for training or inference.
🎯 Efficient Training with PEFT: Use Parameter-Efficient Fine-Tuning (PEFT) to train your models faster and with fewer resources. Easily push trained models to the Hugging Face Hub for sharing or deployment.
🤖 Flexible Inference: Generate predictions using your augmented datasets, whether you're using fine-tuned or untrained models. Test multiple configurations to find the best fit for your use case.
📊 Comprehensive Evaluation: Measure model performance with metrics like EM, F1, ROUGE, BERTScore, and more. Customizable metrics allow you to evaluate retrieval results, reasoning, citations, and other features beyond simple text outputs.
Use Cases
Academic Research: Quickly prototype experiments with different RAG configurations to study how LLMs integrate external information.
Enterprise Solutions: Optimize customer support chatbots by fine-tuning models to retrieve and generate responses based on your company’s knowledge base.
Content Creation: Enhance content generation tools by integrating RAG techniques, ensuring outputs are both accurate and contextually relevant.
Why Choose RAG-FiT?
RAG-FiT stands out for its modularity and ease of use. With configurable workflows and support for customization, it’s designed to fit seamlessly into your existing pipeline. Its integration with tools like Hugging Face and Hydra makes it a versatile choice for researchers and developers alike.
FAQ
Q: Do I need to be an expert in RAG techniques to use RAG-FiT?
A: Not at all! RAG-FiT simplifies the process with default configurations and modular workflows, making it accessible even if you're new to RAG.
Q: Can I use RAG-FiT with any LLM?
A: Yes, RAG-FiT is model-agnostic, so you can fine-tune and evaluate any LLM of your choice.
Q: How do I get started?
A: Clone the repository, install the library, and follow the PubmedQA Tutorial for a quick, end-to-end example.
Conclusion
RAG-FiT is your go-to framework for enhancing LLMs with RAG techniques. From dataset creation to evaluation, it provides the tools you need to build more accurate, context-aware models. Ready to improve your LLMs? Dive into RAG-FiT today and see the difference for yourself.
More information on RAG-FiT
Launched
Pricing Model
Free
Starting Price
Global Rank
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<5k
Tech used
Fastly,Google Fonts,Jekyll,GitHub Pages,Gzip,JSON Schema,OpenGraph,Varnish
RAG-FiT was manually vetted by our editorial team and was first featured on 2025-02-07.
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