Apache Cassandra Lunch 140: LLM Fine Tuning with QLoRA vs RAG : An Evaluation

We continue our investigation into the latest advancements in language model technology with our upcoming talk, "QLoRA vs RAG: an Evaluation." In this session, we compare outcomes from two cutting-edge techniques used in enhancing the capabilities of the Llama 2 model we fine-tuned in the last few talks and Retrieval Augmented Generation (RAG).

Obioma Anomnachi will continue to explore how each method connects to the Llama 2 model, emphasizing their respective impacts on performance through the lens of industry-standard statistical measures. Attendees will gain an understanding of:

• The fundamentals of QLoRA and RAG: Learn how each technique functions, their applications, and their theoretical foundations. Check out our earlier talks in this series for a more in-depth description of this talk.
• Performance Evaluation: We will dissect the performance of our fine-tuned Llama 2 model with QLoRA against its performance with RAG, utilizing robust statistical methods to assess accuracy, relevance, and efficiency.
• Practical Implications: Discover the real-world implications of each method in various applications, from academic research to industry deployments.
• Future Directions: Discuss potential advancements and how these technologies could shape the future of natural language processing and AI.

This talk is designed for data scientists, AI researchers, and anyone interested in the technical aspects of language model development and performance enhancement. Whether you're looking to implement these techniques or simply curious about the state of AI technology, this session will provide valuable insights into two important and common deployments in the field.