Mastering Generative AI: Fine-Tuning Transformers

The demand for technical gen AI skills is exploding. AI engineers who know how to fine-tune transformers for gen AI applications are in hot demand. This Generative AI Engineering Fine-Tuning with Transformers course is designed for AI engineers and other AI specialists who are looking to add highly sought-after skills to their resume. In this course, you’ll explore the differences between PyTorch and Hugging Face. You’ll use pre-trained transformers for language tasks and fine-tune them for special tasks. Plus, you’ll fine-tune generative AI models using PyTorch and Hugging Face. You’ll also explore concepts like parameter-efficient fine-tuning (PEFT), low-rank adaptation (LoRA), quantized low-rank adaptation (QloRA), model quantization with natural language processing (NLP) and prompting. Plus, through valuable hands-on labs, you’ll build your experience loading models and inference, training models with Hugging Face, pre-training LLMs, fine-tuning models, and PyTorch adaptors. If you’re looking to gain the job-ready skills employers need for fine-tuning transformers for gen AI, ENROLL TODAY and power up your resume for career success! Prerequisites: This course requires basic knowledge of Python, PyTorch, and transformer architecture. You should also be familiar with machine learning and neural network concepts. 3b:T4ec, <

• Job-ready skills working with transformer-based LLMs for generative AI engineering employers need in just 2 weeks!
• A good understanding of parameter-efficient fine-tuning (PEFT) using LoRA and QLoRA
• How to use pretrained transformers for language tasks and fine-tune them for specific tasks.
• How to load models and their inferences and train models with Hugging Face.

• Basic knowledge of Python, PyTorch, and transformer architecture. You should also be familiar with machine learning and neural network concepts.