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huggingface/alignment-handbook

Provides robust training recipes and scripts to align large language models with human and AI preferences, enhancing helpfulness and safety.

Core Features

Robust training recipes for LLM alignment (SFT, DPO, ORPO, RLAIF, Constitutional AI).

Supports continued pretraining and fine-tuning (full weights, LoRA/QLoRA).

Includes scripts for training and evaluation, with distributed training via DeepSpeed.

Offers a collection of pre-trained models and datasets (e.g., Zephyr, No Robots).

Provides guidance on preference alignment methods and data collection.

Detailed Introduction

The Alignment Handbook by Hugging Face offers a comprehensive collection of robust training recipes and scripts designed to align large language models (LLMs) with human and AI preferences. Addressing the gap in public resources for preference alignment, this project provides practical methodologies for techniques like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning from Human Feedback (RLHF). It enables developers to enhance LLM helpfulness and safety, offering tools for continued pretraining, parameter-efficient fine-tuning (LoRA/QLoRA), and distributed training with DeepSpeed, thereby empowering the creation of more capable and aligned chatbots.