Master Fine Tuning with Llama, HuggingFace Transformers 2024

What you’ll learn:
Fine tune a Llama Transformer model using HuggingFace
Learn how to use the SFT Trainer from HuggingFace for training
Learn how to use an instruction tuning dataset for language modeling alignment
Learn the basics of supervised fine tuning using HuggingFace

Requirements:
A Google Colab account is necessary. Preferably with compute units for higher memory GPUs. A basic knowledge of PyTorch, Python and machine learning is expected also.

Description:
Dive into the world of AI with our comprehensive Udemy course, designed for enthusiasts eager to master instruction tuning, or supervised fine-tuning (SFT), using the incredibly powerful Tiny Llama 1.1B model. This course is perfect for data scientists, ML engineers, and anyone interested in the practical applications of fine-tuning large language models.Our curriculum is specially crafted to provide you with hands-on experience using the Tiny Llama 1.1B model, trained on a vast 3 trillion tokens and paired with the Databricks-crafted Dolly 15k record dataset. Through this course, you’ll not only learn the basics of SFT but also how to effectively use the HuggingFace Transformers library to prepare your dataset, set up training, and compute accuracy—all within the accessible environment of Google Colab.What You Will LearnIntroduction to Supervised Fine-Tuning (SFT): Gain a solid understanding of the principles behind SFT and its importance in tailoring state-of-the-art language models to specific tasks.Exploring Tiny Llama 1.1B: Delve into the architecture and features of the Tiny Llama 1.1B model and discover its role in the AI ecosystem.Dataset Preparation with Dolly 15k: Learn how to prepare and preprocess the Dolly 15k record dataset to ensure your fine-tuning process is seamless and efficient.HuggingFace Transformers Library: Get hands-on experience with the HuggingFace library, learning how to load models onto a GPU and prepare your training environment.Training Setup and Execution: Walk through the steps to set up and execute the fine-tuning process using Google Colab with a focus on practical implementation.Performance Evaluation: Learn how to compute accuracy and evaluate your model’s performance, using metrics to ensure your SFT efforts are effective.Real-World Application: Translate your new skills to real-world problems, understanding how to adapt your fine-tuned model to various domains.Model Used: TinyLlama-1.1B-intermediate-step-1431k-3TDataset Used: databricks-dolly-15kWho This Course is ForData Scientists and Machine Learning Engineers seeking to specialize in NLP and instruction tuning.AI Practitioners looking to implement and scale fine-tuned language models for industry applications.Software Developers eager to enhance applications with sophisticated language understanding capabilities.Students and Academics desiring hands-on experience with state-of-the-art AI fine-tuning techniques.PrerequisitesProficiency in Python and familiarity with machine learning and NLP concepts.Experience with neural network frameworks, preferably PyTorch, as used by the HuggingFace Transformers library.A Google account to access Google Colab for hands-on exercises.

Who this course is for:
This course a basic level of machine learning knowledge. The user should understand the basics of forward and backward passes. Basic to intermediate Python level is also assumed. The user is also assumed to have a Google Colab account.