Reward modeling for generative AI with Hugging Face
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Learn the fundamentals of reward modeling for generative AI using Hugging Face. Discover how to train models that learn from user feedback to improve AI performance in real-world scenarios.
At a Glance
Train large language models (LLMs) for reward modeling. Imagine a machine learning engineer at a leading technology company, tasked with integrating advanced language models into AI-powered products. The objective is to evaluate and select LLMs capable of understanding and following complex instructions, improving automated customer service, and generating high-quality responses. This process involves fine-tuning models using domain-specific data sets and Low-Rank Adaptation (LoRA) techniques.
A look at the project ahead
- Learning Objective 1: Evaluate and select the best LLMs for specific tasks.
- Learning Objective 2: Fine-tune models using domain-specific data sets and Low-Rank Adaptation (LoRA).
- Learning Objective 3: Implement reward modeling and reinforcement learning with human feedback.
- Learning Objective 4: Gain proficiency in using the Hugging Face Transformers library to fine-tune pretrained models on domain-specific data sets. Implement LoRA techniques and deploy the fine-tuned models into production environments.
- Learning Objective 5: Develop and apply reward functions using Hugging Face tools to guide generative model behavior.
What you’ll need
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