kithara 0.0.10

LLM post-training library

Kithara - Easy Finetuning on TPUs

👋 Overview

Kithara is a lightweight library offering building blocks and recipes for tuning popular open source LLMs including Gemma2 and Llama3 on Google TPUs.

It provides:

• Frictionless scaling: Distributed training abstractions intentionally built with simplicity in mind.
• Multihost training support: Integration with Ray, GCE and GKE.
• Async, distributed checkpointing: Multi-host & Multi-device checkpointing via Orbax.
• Distributed, streamed dataloading: Per-process, streamed data loading via Ray.data.
• GPU/TPU fungibility: Same code works for both GPU and TPU out of the box.
• Native integration with HuggingFace: Tune and save models in HuggingFace format.

New to TPUs?

Using TPUs provides significant advantages in terms of performance, cost-effectiveness, and scalability, enabling faster training times and the ability to work with larger models and datasets. Check out our onboarding guide to getting TPUs.

🔗 Key links and resources

📚 Documentation	Read Our Docs
💾 Installation	Quick Pip Install
✏️ Get Started	Intro to Kithara
🌟 Supported Models	List of Models
🌐 Supported Datasets	List of Data Formats
⌛️ Performance Optimizations	Our Memory and Throughput Optimizations
📈 Scaling up	Guide for Tuning Large Models

🌵 Examples

• Quick Start Colab Notebook: SFT + LoRA with Gemma2-2b

• SFT + LoRA: Step by Step Example

• Continued Pretraining: Step by Step Example