Tinker Cookbook is an open-source framework for fine-tuning large language models, supporting supervised learning, reinforcement learning, and parameter-efficient techniques like LoRA adapters. It provides a complete pipeline for aligning models with human preferences through multi-stage RLHF workflows, from supervised fine-tuning through preference optimization to reinforcement learning.
The framework distinguishes itself through recipe-based training orchestration, where fine-tuning workflows are defined as composable recipe files that chain data loading, model configuration, and training loops into repeatable pipelines. It includes an async concurrent sampling engine that maximizes throughput during training rollouts and evaluation, and supports multi-agent reinforcement learning with self-play or competitive environments. The system manages model checkpoints through hub-centric weight management, enabling saving, loading, downloading, and publishing to remote hubs for sharing and deployment.
Beyond core training, the framework covers hyperparameter sweeping across learning rates, LoRA ranks, and RL parameters to find optimal configurations. It handles vision-language model fine-tuning, prompt distillation into model weights, and multi-turn conversation training. The system includes tools for building, merging, and exporting LoRA adapters for efficient serving and HuggingFace compatibility, along with evaluation capabilities for measuring model performance on standard benchmarks.
The documentation provides guidance on configuring training runs, building custom reinforcement learning environments, and diagnosing training issues through AI assistant skills.