Generative AI NLP Specialization | Level 3

Dive Deeper into LLMs and it’s fine tuning techniques

Duration

3 Days

Level

Intermediate Level

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Welcome to “Generative AI NLP Specialization | Level 3”, an advanced course that will immerse you in the fascinating world of fine-tuning Large Language Models (LLMs) and harnessing them for real-world applications. This comprehensive journey will equip you with the knowledge and skills needed to fine-tune LLMs effectively and align them with human values. Through a structured exploration of topics and practical exercises, you’ll delve into the nuances of reinforcement learning, parameter-efficient fine-tuning, and the evaluation of fine-tuned models.

By the end of this course, you will be able to gain a deep understanding of the intricacies of fine-tuning Large Language Models, including instruction fine-tuning, parameter-efficient fine-tuning, and techniques like LoRA and Soft Prompts.

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Developers and software engineers interested in learning GenerativeAI
AI enthusiasts and professionals aiming to build intelligent and innovative solutions
Even Data scientists and machine learning practitioners seeking to enhance their skills in working with GenAI models

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Deepdive into Fine-tuning LLMs

Introduction to Large Language Models
Pre-training Large Language Models
Computational Challenges in Training LLMs
Scaling Laws and Compute-Optimal Models
Fine-tuning Techniques
- Instruction Fine-tuning
- Fine-tuning on a Single Task
- Multi-task Instruction Fine-tuning
- Parameter Efficient Fine-tuning (PEFT)
- PEFT Techniques: LoRA and Soft Prompts

Model Evaluation and Benchmarks
- Evaluating Fine-tuned Models
- Introduction to Benchmarks

Reinforcement Learning and LLM-powered Applications

Introduction
Overview of Fine-tuning Large Language Models
Importance of Aligning Models with Human Values
Reinforcement Learning from Human Feedback (RLHF)
- Introduction to RLHF
- Obtaining Feedback from Humans
- Developing a Reward Model for RLHF

Fine-tuning with Reinforcement Learning
- Fine-tuning Process using RLHF
- Techniques for Optimizing RLHF Performance
- Optional Video: Proximal Policy Optimization
- Addressing Reward Hacking
Scaling Human Feedback
- Challenges and Considerations
- Strategies for Collecting and Incorporating Large-scale Feedback
- Evaluation and Assessment
- Methods for Evaluating Fine-tuned Language Models
- Assessing Model Performance in Alignment with Human Values
Lab: Transforming Human Interactions with AI ( RLHF)

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Participants should have attended Generative AI NLP Specialisation | Level 2 course or have an equivalent knowledge.

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