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Reward modeling for generative AI with Hugging Face

IntermediateGuided Project

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.

4.8 (11 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Enrollment Count

  • 73

Skills You Will Learn

  • Generative AI, LLM, NLP, AI, Python, HuggingFace

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 2 hours

Platform

  • SkillsNetwork

Last Update

  • June 9, 2025
About this Guided Project
Learn how to train large language models (LLM) for reward modeling, a cutting-edge area in AI that enhances the capability of models to generate high-quality, contextually appropriate responses. As a machine learning engineer at a large technology company, you'll explore how to integrate these advanced models into AI-powered products, improving automated customer service and handling complex instructions. By the end of this project, you have valuable skills in model fine-tuning, reinforcement learning, and human feedback integration, making you proficient in deploying sophisticated AI solutions in real-world applications.

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

Before you begin this guided project, it's recommended that you have a basic understanding of Python programming and some familiarity with deep learning concepts. Experience with natural language processing (NLP) would be advantageous but is not mandatory.
You'll be working in an environment powered by IBM Skills Network Labs, which comes pre-installed with essential tools like Python, Hugging Face libraries, and Faiss, so you can focus on learning without worrying about setting up your environment. This project is best accessed using the latest versions of Chrome, Edge, Firefox, Internet Explorer, or Safari to ensure optimal performance.

Instructors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Ashutosh Sagar

Data Scientist

I am currently a Data Scientist at IBM with a Master’s degree in Computer Science from Dalhousie University. I specialize in natural language processing, particularly in semantic similarity search, and have a strong background in working with advanced AI models and technologies.

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Fateme Akbari

Data Scientist @IBM

I'm a data-driven Ph.D. Candidate at McMaster University and a data scientist at IBM, specializing in machine learning (ML) and natural language processing (NLP). My research focuses on the application of ML in healthcare, and I have a strong record of publications that reflect my commitment to advancing this field. I thrive on tackling complex challenges and developing innovative, ML-based solutions that can make a meaningful impact—not only for humans but for all living beings. Outside of my research, I enjoy exploring nature through trekking and biking, and I love catching ball games.

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Contributors

Victoria Nadar

Growth Marketer @IBM

Here to tell you what I learnt from my experience in the Marketing Technology Industry

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