What you'll learn
Fine tuning a Llama model with LoRA
Learn the principles and science behind low rank adaption
Fine tune models with LoRA on small consumer GPUs
Use HuggingFace PEFT, TRL and Trainer libraries for training

Requirements

Basic PyThon Knowledge
Basic Machine Learning Knowledge
A Google Colab Account

Description
Mastering LoRA Fine-Tuning on Llama 1.1B with the Guanaco Chat Dataset: Training on Consumer GPUs

Unleash the potential of Low-Rank Adaptation (LoRA) for efficient AI model fine-tuning with our groundbreaking Udemy course. Designed for forward-thinking data scientists, machine learning engineers, and software engineers, this course guides you through the process of LoRA fine-tuning applied to the cutting-edge Llama 1.1B model, utilizing the diverse Guanaco chat dataset. LoRA’s revolutionary approach enables the customization of large language models on consumer-grade GPUs, democratizing access to advanced AI technology by optimizing memory usage and computational efficiency.

Dive deep into the practical application of LoRA fine-tuning within the HuggingFace Transformers framework, leveraging its Parameter-Efficient Fine-Tuning Library alongside the intuitive HuggingFace Trainer. This combination not only streamlines the fine-tuning process, but also significantly enhances learning efficiency and model performance on datasets.

What You Will Learn

Introduction to LoRA Fine-Tuning: Grasp the fundamentals of Low-Rank Adaptation and its pivotal role in advancing AI model personalization and efficiency.

Hands-On with Llama 1.1B and Guanaco Chat Dataset: Experience direct interaction with the Llama 1.1B model and Guanaco chat dataset, preparing you for real-world application of LoRA fine-tuning.

Efficient Training on Consumer GPUs: Explore the transformational capability of LoRA to fine-tune large language models on consumer hardware, emphasizing its low memory footprint and computational advantages.

Integration with HuggingFace Transformers: Master the use of the HuggingFace Parameter-Efficient Fine-Tuning Library and the HuggingFace Trainer for streamlined and effective model adaptation.

Insightful Analysis of the LoRA Paper: Delve into the original LoRA research, dissecting its methodologies, findings, and impact on the field of NLP and beyond.

Model Evaluation and Optimization Techniques: Evaluate and optimize your fine-tuned model’s performance, employing metrics to gauge success and strategies for further improvement. Prompt the model before and after training to see the impact of LoRA training on real output.

Model Used: TinyLlama-1.1B-intermediate-step-1431k-3T

Dataset Used: guanaco-llama2-1k

Who This Course is For

AI and Machine Learning Practitioners: Innovators seeking advanced skills in model fine-tuning for specialized NLP tasks.

Data Scientists: Professionals aiming to harness LoRA for effective model training on unique datasets.

Tech Enthusiasts: Individuals eager to explore the implementation of state-of-the-art AI techniques on accessible platforms.

Academic Researchers and Students: Scholars and learners aspiring to deepen their knowledge of novel fine-tuning methods in AI research.

Prerequisites

Proficiency in Python: A solid foundation in Python programming is essential for engaging with the course material effectively.

Familiarity with Machine Learning and NLP Concepts: A basic understanding of machine learning principles and natural language processing is recommended to maximize learning outcomes.

Experience with Neural Network Frameworks: Prior exposure to frameworks like PyTorch, as utilized by the HuggingFace Transformers library, will facilitate a smoother learning experience.

Embrace the future of AI model tuning with our expertly designed course, and embark on a journey to mastering LoRA fine-tuning on Llama 1.1B using the Guanaco chat dataset, all while leveraging the power of consumer GPUs and the efficiency of HuggingFace Transformers.

Who this course is for
This course is for anyone looking to learn to fine tune large language models with LoRA on HuggingFace. Basic Python skills, machine learning knowledge and a Google Colab account is needed.