This repository provides a comprehensive overview of the training methodologies behind large language models like ChatGPT, focusing on the stages of pre-training, supervised fine-tuning (SFT), reward modeling, and reinforcement learning (RLHF). It serves as an educational resource for understanding the technical underpinnings of AI alignment and model training, targeting engineers and researchers interested in replicating or extending these techniques.

How It Works

The project details the progression from unsupervised pre-training on vast text corpora to supervised fine-tuning using prompt-response pairs. It then elaborates on reward modeling, where human preferences are used to train a model that scores responses, and finally, reinforcement learning (specifically Proximal Policy Optimization - PPO) to further refine the model based on these rewards. This multi-stage approach is key to aligning LLM behavior with human intent and safety guidelines.

Highlighted Details

• Explains the training pipeline: Pre-training, Supervised Fine-Tuning (SFT), Reward Modeling, and Reinforcement Learning (RLHF).
• Covers core LLM architectural components like RMSNorm, SwiGLU, and Rotary Positional Embeddings (ROPE).
• Details techniques for efficient fine-tuning, including LoRA (Low-Rank Adaptation) and the PEFT library.
• Discusses the TRL (Transformer Reinforcement Learning) library for implementing RLHF, including RewardTrainer and PPOTrainer.

Maintenance & Community

This repository appears to be a personal project focused on educational content rather than a continuously maintained software library. There are no explicit mentions of contributors, sponsorships, or community channels.

Licensing & Compatibility

The repository does not specify a license. Users should assume all rights are reserved by the author unless otherwise stated.

Limitations & Caveats

This repository is an educational resource and does not provide runnable code or pre-trained models for direct use. It focuses on explaining concepts and methodologies rather than offering a deployable solution.