v0.1.1 (llama3 patch)

Overview

This patch includes support for fine-tuning Llama3 with torchtune as well as various improvements to the library.

New Features & Improvements

Recipes

• Added configuration for Llama2 13B QLoRA (#779)
• Added support for Llama2 70B LoRA (#788)

Models

• Added support for Llama3 (#793)

Utils

• Improvements to Weights & Biases logger (#772, #777)

Documentation

• Added Llama3 tutorial (#793)
• Updated E2E tutorial with instructions for uploading to the Hugging Face Hub (#773)
• Updates to the README (#775, #778, #786)
• Added instructions for installing torchtune nightly (#792)

torchtune v0.1.0 (first release)

Overview

We are excited to announce the release of torchtune v0.1.0! torchtune is a PyTorch library for easily authoring, fine-tuning and experimenting with LLMs. The library emphasizes 4 key aspects:

• Simplicity and Extensibility. Native-PyTorch, componentized design and easy-to-reuse abstractions
• Correctness. High bar on proving the correctness of components and recipes
• Stability. PyTorch just works. So should torchtune
• Democratizing LLM fine-tuning. Works out-of-the-box on both consumer and professional hardware setups

torchtune is tested with the latest stable PyTorch release (2.2.2) as well as the preview nightly version.

New Features

Here are a few highlights of new features from this release.

Recipes

• Added support for running a LoRA finetune using a single GPU (#454)
• Added support for running a QLoRA finetune using a single GPU (#478)
• Added support for running a LoRA finetune using multiple GPUs with FSDP (#454, #266)
• Added support for running a full finetune using a single GPU (#482)
• Added support for running a full finetune using multiple GPUs with FSDP (#251, #482)
• Added WIP support for DPO (#645)
• Integrated with EleutherAI Eval Harness for an evaluation recipe (#549)
• Added support for quantization through integration with torchao (#632)
• Added support for single-GPU inference (#619)
• Created a config parsing system to interact with recipes through YAML and the command line (#406, #456, #468)

Models

• Added support for Llama2 7B (#70, #137) and 13B (#571)
• Added support for Mistral 7B (#571)
• Added support for Gemma [WIP] (#630, #668)

Datasets

• Added support for instruction and chat-style datasets (#752, #624)
• Included example implementations of datasets (#303, #116, #407, #541, #576, #645)
• Integrated with Hugging Face Datasets (#70)

Utils

• Integrated with Weights & Biases for metric logging (#162, #660)
• Created a checkpointer to handle model files from HF and Meta (#442)
• Added a tune CLI tool (#396)

Documentation

In addition to documenting torchtune’s public facing APIs, we include several new tutorials and “deep-dives” in our documentation.

• Added LoRA tutorial (#368)
• Added “End-to-End Workflow with torchtune” tutorial (#690)
• Added datasets tutorial (#735)
• Added QLoRA tutorial (#693)
• Added deep-dive on the checkpointer (#674)
• Added deep-dive on configs (#311)
• Added deep-dive on recipes (#316)
• Added deep-dive on Weights & Biases integration (#660)

Community Contributions

This release of torchtune features some amazing work from the community:

• Gemma 2B model from @solitude-alive (#630)
• DPO finetuning recipe from @yechenzhi (#645)
• Weights & Biases updates from @tcapelle (#660)