Recipes Overview

Recipes are the primary entry points for torchtune users. These can be thought of as hackable, singularly-focused scripts for interacting with LLMs including fine-tuning, inference, evaluation, and quantization.

Each recipe consists of three components:

• Configurable parameters, specified through yaml configs and command-line overrides

• Recipe script, entry-point which puts everything together including parsing and validating configs, setting up the environment, and correctly using the recipe class

• Recipe class, core logic needed for fine-tuning, exposed through a set of APIs

Note

To learn more about the concept of “recipes”, check out our technical deep-dive: What Are Recipes?.

Finetuning

Our recipes include:

• Single-device LoRA fine-tuning.

• Single-device full fine-tuning

• Distributed full fine-tuning

• Distributed LoRA fine-tuning

• Direct Preference Optimization (DPO)

• Proximal Policy Optimization (PPO)

• Distributed Quantization-Aware Training (QAT).

For a full list, please run:

tune ls

Note

Our recipe documentation is currently in construction. Please feel free to follow the progress in our tracker issue here.