2025 Fine Tuning LLM with Hugging Face Transformers for NLP
2025 Fine Tuning LLM with Hugging Face Transformers for NLP
Master Transformer models like Phi2, LLAMA; BERT variants, and distillation for advanced NLP applications on custom data
Product Brand: Udemy
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Udemy Coupon Code for 2025 Fine Tuning LLM with Hugging Face Transformers for NLP Course. Master Transformer models like Phi2, LLAMA; BERT variants, and distillation for advanced NLP applications on custom data
Created by Laxmi Kant | KGP Talkie | 16.5 hours on-demand video course | 1 downloadable resource
Fine Tuning LLM Course Overview
2025 Fine Tuning LLM with Hugging Face Transformers for NLP
Do not take this course if you are an ML beginner. This course is designed for those who are interested in pure coding and want to fine-tune LLMs instead of focusing on prompt engineering. Otherwise, you may find it difficult to understand.
Welcome to “Mastering Transformer Models and LLM Fine Tuning”, a comprehensive and practical course designed for all levels, from beginners to advanced practitioners in Natural Language Processing (NLP). This course delves deep into the world of Transformer models, fine-tuning techniques, and knowledge distillation, with a special focus on popular BERT variants like Phi2, LLAMA, T5, BERT, DistilBERT, MobileBERT, and TinyBERT.
What you’ll learn
- Understand transformers and their role in NLP.
- Gain hands-on experience with Hugging Face Transformers.
- Learn about relevant datasets and evaluation metrics.
- Fine-tune transformers for text classification, question answering, natural language inference, text summarization, and machine translation.
- Understand the principles of transformer fine-tuning.
- Apply transformer fine-tuning to real-world NLP problems.
- Learn about different types of transformers, such as BERT, GPT-2, and T5.
- Hands-on experience with the Hugging Face Transformers library
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Who this course is for:
- NLP practitioners: This course is designed for NLP practitioners who want to learn how to fine-tune pre-trained transformer models to achieve state-of-the-art results on a variety of NLP tasks.
- Researchers: This course is also designed for researchers who are interested in exploring the potential of transformer fine-tuning for new NLP applications.
- Students: This course is suitable for students who have taken an introductory NLP course and want to deepen their understanding of transformer models and their application to real-world NLP problems.
- Developers: This course is beneficial for developers who want to incorporate transformer fine-tuning into their NLP applications.
- Hobbyists: This course is accessible to hobbyists who are interested in learning about transformer fine-tuning and applying it to personal projects.
