💡Fine-tuning LLN models is a crucial skill for generative AI roles in companies.
🔑Quantization, Laura CLA, and PFT are important techniques used in fine-tuning.
💻Implementing projects with hugging face open source models enhances understanding of fine-tuning.
📊Fine-tuning with custom datasets using Google Gamma model helps in solving different use cases.
🎯This crash course provides step-by-step understanding, theoretical intuition, and practical application of fine-tuning.
Why is fine-tuning LLN models important for generative AI roles?
—Many companies hiring for generative AI roles expect candidates to have fine-tuning skills with custom datasets.
What are the key techniques used in fine-tuning LLN models?
—Quantization, Laura CLA, and PFT are important techniques used in fine-tuning.
How can I enhance my understanding of fine-tuning LLN models?
—Implementing projects with hugging face open source models can enhance your understanding of fine-tuning.
What are the benefits of fine-tuning with custom datasets using Google Gamma model?
—Fine-tuning with custom datasets using Google Gamma model helps in solving different use cases and provides valuable practical experience.
What can I expect to learn in this crash course?
—This crash course provides step-by-step understanding, theoretical intuition, and practical application of fine-tuning LLN models.
00:00Welcome to the crash course on performing fine-tuning with LLM models.
01:18In this crash course, you will learn the theoretical intuition and practical application of fine-tuning LLN models.
02:10You will learn about techniques like quantization, Laura CLA, and PFT, which are important for fine-tuning.
03:40You will also implement multiple projects using hugging face open source models to enhance your understanding of fine-tuning.
05:36Furthermore, you will learn how to perform fine-tuning with your own custom datasets using the Google Gamma model.
07:34This crash course provides a step-by-step process, theoretical intuition, and practical application of fine-tuning LLN models.
