In this module, you’ll begin by defining instruction-tuning and its process. You’ll also gain insights into loading a dataset, generating text pipelines, and training arguments. Further, you’ll delve into reward modeling, where you’ll preprocess the dataset and apply low-rank adaptation (LoRA) configuration. You’ll also learn to quantify quality responses, guide model optimization, and incorporate reward preferences. You’ll also describe reward trainer, an advanced training technique to train a model, and reward model loss using Hugging Face. The labs, in this module will allow practice on instruction-tuning and reward models.

Learning Objectives

• Explain reward model loss and train a model to generate responses
• Set up and configure the GPT-2 model, tokenize and prepare text data, and evaluate the model’s performance using the reward modeling process
• Define the concepts of instruction-tuning
• Demonstrate loading and formatting data set, creating a model, and training arguments
• Define reward modeling and response evaluation for a model of human preferences
• Explain reward model loss and train a model to predict responses
• Describe reward modeling with Hugging Face to preprocess the data set and train a model

Outline

Module 1: Instruction-tuning and Reward modeling

In this module, you’ll begin by defining instruction-tuning and its process. You’ll also gain insights into loading a dataset, generating text pipelines, and training arguments. Further, you’ll delve into reward modeling, where you’ll preprocess the dataset and apply low-rank adaptation (LoRA) configuration. You’ll also learn to quantify quality responses, guide model optimization, and incorporate reward preferences. You’ll also describe reward trainer, an advanced training technique to train a model, and reward model loss using Hugging Face. The labs, in this module will allow practice on instruction-tuning and reward models.

Learning Objectives

• Explain reward model loss and train a model to generate responses
• Set up and configure the GPT-2 model, tokenize and prepare text data, and evaluate the model’s performance using the reward modeling process
• Define the concepts of instruction-tuning
• Demonstrate loading and formatting data set, creating a model, and training arguments
• Define reward modeling and response evaluation for a model of human preferences
• Explain reward model loss and train a model to predict responses