Course info

In today's rapidly evolving enterprise landscape, mastering the art of LLM (Large Language Model) fine-tuning has become indispensable for engineers seeking to enhance the performance and specificity of AI models. This course is meticulously designed to provide you with the practical skills and theoretical knowledge required to excel in fine-tuning LLMs, ensuring that your models not only perform optimally but also maintain robustness and accuracy across diverse applications.

This 2-month intensive course is predominantly hands-on, featuring extensive lab exercises, projects, quizzes, and case studies. It focuses on contemporary fine-tuning techniques that go beyond simply feeding more data into an LLM. We delve into sophisticated methods that prevent catastrophic forgetting and reduce hallucinations while achieving measurable improvements in domain-specific tasks.

Course Overview

The course is structured to guide you through a comprehensive journey of LLM fine-tuning, starting from foundational concepts to advanced techniques. The key topics covered include:

• Overview of the Journey: Understand the evolution of fine-tuning practices and the importance of these techniques in modern AI. This module will set the stage by providing a historical context and highlighting the significance of fine-tuning in achieving high-performance AI models.
• Performance Evaluations: Learn how to assess the performance of fine-tuned models, ensuring they meet desired benchmarks. This includes understanding various evaluation metrics, tools, and best practices for validating model improvements.
• Dangers of Fine-tuning: Catastrophic Forgetting and Hallucinations: Explore the risks associated with fine-tuning and methods to mitigate these issues. This topic covers the challenges of maintaining model integrity and reliability during and after fine-tuning.
• Parameter-Efficient Fine Tuning: Discover techniques for efficient fine-tuning that optimize model performance without excessive computational costs. Learn about methods like AdapterFusion, Prompt Tuning, and other parameter-efficient strategies.
• LoRA Family of Methods: Gain insights into the Low-Rank Adaptation methods for fine-tuning LLMs. This section delves into the specifics of LoRA techniques, their advantages, and implementation strategies.
• Fine-tuning Encoder Models: Focus on strategies for fine-tuning encoder-based models for improved feature extraction. Learn how to adapt these models to various tasks and enhance their performance through targeted fine-tuning.
• Fine-tuning LLMs: Dive deep into advanced techniques specific to large language models. This includes understanding the nuances of tuning models like GPT, BERT, and their derivatives.
• Fine-tuning Vision Models: Learn how to adapt vision models to specific tasks through fine-tuning. This topic covers techniques for enhancing models like CNNs and Vision Transformers for improved image recognition and analysis.
• Fine-tuning Multimodal Models: Explore the challenges and solutions for fine-tuning models that integrate multiple data types. Understand how to handle and optimize models that process both text and image data simultaneously.
• Fine-tuning vs RAG: Understand the differences and complementarities between fine-tuning and Retrieval-Augmented Generation (RAG) techniques. Learn how to effectively combine these approaches for enhanced model performance.
• Cloud-based Fine Tuning (Modal/Runpod): Utilize cloud platforms for scalable fine-tuning solutions. This section provides insights into leveraging cloud resources to perform large-scale fine-tuning efficiently.
• Data Augmentation and Synthetic Data Creation: Enhance your datasets with synthetic data to improve model training and robustness. Explore various techniques for generating and using synthetic data to augment your training process.
• Fine-tuning Tools: Unsloth, Axolotl, and Others: Get hands-on experience with cutting-edge tools that facilitate efficient fine-tuning processes. Learn how to use these tools to streamline and optimize your fine-tuning workflows.

By the end of this course, you will have developed a robust understanding of fine-tuning techniques and their practical applications across various domains. The skills you acquire will empower you to refine AI models that deliver precise, reliable, and high-performance results in any enterprise setting. Join us on this journey to become proficient in one of the most critical aspects of modern AI development.

We look forward to welcoming you to this challenging and rewarding course, where you will engage with complex concepts, collaborate on innovative projects, and emerge as a skilled professional in the field of LLM fine-tuning.