Offered By: IBMSkillsNetwork
Reward modeling for generative AI with Hugging Face
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.
Continue readingGuided Project
Artificial Intelligence
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:Â 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
Estimated Effort
2 Hours
Level
Intermediate
Skills You Will Learn
AI, Generative AI, HuggingFace, LLM, NLP, Python
Language
English
Course Code
GPXX0ANNEN