torchtune: Easily fine-tune LLMs using PyTorch

🌈 Abstract

The article announces the alpha release of torchtune, a PyTorch-native library for easily fine-tuning large language models (LLMs). It highlights torchtune's key features, design principles, and integrations with popular tools in the open-source LLM ecosystem.

🙋 Q&A

[01] Overview of torchtune

1. What is torchtune?

• torchtune is a PyTorch-native library for easily fine-tuning large language models (LLMs).
• It provides composable and modular building blocks along with easy-to-extend training recipes to fine-tune popular LLMs on a variety of consumer-grade and professional GPUs.

2. What are the key features of torchtune?

• Supports the full fine-tuning workflow from start to finish, including:
  • Downloading and preparing datasets and model checkpoints
  • Customizing the training with composable building blocks
  • Logging progress and metrics
  • Quantizing the model post-tuning
  • Evaluating the fine-tuned model on popular benchmarks
  • Running local inference for testing fine-tuned models
  • Checkpoint compatibility with popular production inference systems

3. What are the design principles behind torchtune?

• Easy extensibility: Designed around easily composable components and hackable training loops, with minimal abstraction
• Democratize fine-tuning: Users of all expertise levels can use torchtune, even on consumer-grade GPUs
• Interoperability with the open-source LLM ecosystem: Integrates with popular tools like Hugging Face Hub, PyTorch FSDP, Weights & Biases, EleutherAI's LM Evaluation Harness, ExecuTorch, and torchao

[02] Motivation and Future Plans

1. Why was torchtune developed?

• There has been an explosion of interest in open LLMs, and fine-tuning these models has become a critical technique for adapting them to specific use cases.
• Existing solutions make it hard to add customizations or optimizations, as the necessary pieces are hidden behind layers of abstractions.
• torchtune aims to empower developers to adapt LLMs to their specific needs and constraints with full control and visibility.

2. What are the future plans for torchtune?

• The team plans to continue augmenting the library with more models, features, and fine-tuning techniques.
• They are open to feedback, comments, and feature requests from the community, and welcome contributions.