recipes_overview.rst

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: :ref:`recipe_deepdive`.

Finetuning

Our recipes include:

• :ref:`Single-device LoRA fine-tuning <lora_finetune_recipe_label>`.
• Single-device full fine-tuning
• Distributed full fine-tuning
• Distributed LoRA fine-tuning
• :ref:`Direct Preference Optimization (DPO) <dpo_recipe_label>`
• Proximal Policy Optimization (PPO)
• :ref:`Distributed Quantization-Aware Training (QAT)<qat_distributed_recipe_label>`.

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.