<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> This repository offers a practical, step-by-step implementation of Reinforcement Learning from Human Feedback (RLHF) for aligning Large Language Models (LLMs). Targeting engineers and researchers, it demystifies RLHF through three focused Jupyter notebooks, enabling hands-on understanding and application of LLM alignment techniques.

How It Works

The project implements RLHF in three stages using Jupyter notebooks: Supervised Fine-Tuning (SFT) adapts a pre-trained GPT-2 model to a target domain (movie review sentiment using SST2). A Reward Model (RM) is then trained on the same dataset to predict sentiment scores, mimicking human preferences. Finally, Proximal Policy Optimization (PPO) fine-tunes the SFT model, using the RM to guide generation towards desired outputs (positive sentiment sentences). This modular, notebook-driven approach simplifies the complex RLHF pipeline.

Quick Start & Requirements

• Prerequisites: A Hugging Face Access Token is required to download pre-trained models.
• Setup: Set the token as an environment variable: export HF_TOKEN='your_huggingface_token_here' (local) or within a notebook cell (import os; os.environ['HF_TOKEN'] = 'your_huggingface_token_here').
• Running: Execute the provided Jupyter notebooks sequentially: 1-SFT.ipynb, 2-RM Training.ipynb, and 3-RLHF.ipynb.
• Resources: Requires Python environment, Hugging Face libraries. GPU recommended for practical training times.
• Links: HuggingFace Quickstart Guide for token acquisition.

Highlighted Details

• Adapts the general RLHF framework to specifically fine-tune GPT-2 for generating positive sentiment sentences, using the Stanford Sentiment Treebank (SST2) dataset.
• Provides a clear, code-centric walkthrough of each RLHF component (SFT, RM, PPO) within separate, executable notebooks.

Maintenance & Community

No specific details regarding maintainers, community channels (e.g., Discord, Slack), or project roadmap are provided in the README.

Licensing & Compatibility

The repository README does not specify a software license. This lack of explicit licensing information may pose compatibility issues for commercial use or integration into closed-source projects.

Limitations & Caveats

The implementation is presented as a reference for understanding RLHF components and is tailored to a specific task (sentiment generation) using GPT-2 and the SST2 dataset. It may require significant adaptation for other LLMs, tasks, or datasets. No information on alpha status, known bugs, or performance benchmarks is provided.