https://github.com/furk4neg3/ibm_reward_modeling_with_transformers

This repository contains the implementation of reward modeling to fine-tune large language models (LLMs), focusing on improving the quality and alignment of generated responses with specific goals.
https://github.com/furk4neg3/ibm_reward_modeling_with_transformers

Last synced: 2 months ago
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This repository contains the implementation of reward modeling to fine-tune large language models (LLMs), focusing on improving the quality and alignment of generated responses with specific goals.

• Host: GitHub
• URL: https://github.com/furk4neg3/ibm_reward_modeling_with_transformers
• Owner: furk4neg3
• Created: 2024-11-24T19:25:25.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-11-24T19:28:05.000Z (11 months ago)
• Last Synced: 2025-02-02T01:34:06.690Z (8 months ago)
• Language: Jupyter Notebook
• Size: 75.2 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Reward Modeling for Enhancing Large Language Models  

This repository contains the implementation of reward modeling to fine-tune large language models (LLMs), focusing on improving the quality and alignment of generated responses with specific goals. The project utilizes the Hugging Face `trl` library to train a GPT-2 model for sequence classification and evaluate responses using reward-based techniques.  

## Objectives  

After completing this lab, you will gain the ability to:  

- Understand the fundamentals of reward modeling in machine learning.  

- Preprocess and prepare datasets for reward modeling tasks.  

- Configure and train a GPT-2 model for sequence classification.  

- Implement special tokens in tokenizers for customized training.  

- Evaluate model performance using pairwise comparisons of responses.  

- Align LLM outputs with desired objectives and behaviors.  

## Key Features  

- **Reward Modeling:** Training models to prioritize specific behaviors using defined rewards.  

- **GPT-2 Fine-Tuning:** Configuration and training of a GPT-2 model for sequence classification.  

- **Pairwise Evaluation:** Comparing model-generated responses to measure quality and alignment.  

- **Hugging Face Integration:** Leveraging the `trl` library for efficient reward model implementation.  

## Techniques Covered

- **Data Preprocessing:** Preparing datasets for reward-based training tasks.

- **Tokenization:** Adding special tokens to tailor the tokenizer for custom tasks.

- **Sequence Classification:** Using GPT-2 to classify and generate high-quality responses.

- **Reward Modeling Concepts:** Understanding and implementing reward-based learning for LLMs.

- **Tools and Libraries:** Python, Hugging Face Transformers, Hugging Face trl, PyTorch

  

## Results

The reward model successfully fine-tuned the GPT-2 LLM to generate responses aligned with predefined goals and quality standards. The approach ensures enhanced performance for tasks such as customer service and complex instruction following.

## Acknowledgements

This project was developed as part of a hands-on lab by IBM from Generative AI Advance Fine-Tuning for LLMs course.