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The Alignment Handbook is an open-source resource created to provide practical guidance for aligning large language models with human preferences and safety requirements. The project focuses on the post-training stage of model development, where models are refined after pre-training to behave more helpfully, safely, and reliably in real-world applications. It provides detailed training recipes that explain how to perform tasks such as supervised fine-tuning, preference modeling, and reinforcement learning from human feedback. The handbook also includes reproducible workflows for training instruction-following models and evaluating alignment quality across different datasets and benchmarks. One of its goals is to bridge the gap between academic research on alignment methods and practical engineering implementation.
Features
- Detailed training recipes for aligning large language models
- Guides for supervised fine-tuning and preference learning workflows
- Support for techniques such as RLHF and Direct Preference Optimization
- Evaluation methods for measuring alignment quality and model behavior
- Open datasets and training scripts for reproducing alignment experiments
- Documentation bridging research methods and practical engineering workflows
Project Samples
