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?.
Supervised Finetuning¶
torchtune provides built-in recipes for finetuning on single device, on multiple devices with FSDP, using a variety of memory optimization features. Our fine-tuning recipes support all of our models and all our dataset types. This includes continued pre-training, and various supervised funetuning paradigms, which can be customized through our datasets. Check out our dataset tutorial for more information.
Our supervised fine-tuning recipes include:
Single-device LoRA fine-tuning.
Note
Want to learn more about a certain recipe, but can’t find the documentation here? Not to worry! Our recipe documentation is currently in construction - come back soon to see documentation of your favourite fine-tuning techniques. We’d love to support your contributions if you’re interested in helping out here. Check out our tracker issue here.
