Quaterion is a Python framework designed to fine-tune similarity learning models for tasks like semantic search, recommendations, and anomaly detection. It targets users needing to adapt pre-trained models to custom tasks efficiently, especially with limited data or computational resources, by avoiding slow, costly full model retraining.

How It Works

Quaterion leverages a caching mechanism to enable training with large batch sizes on modest hardware, significantly speeding up the fine-tuning process. It incorporates specially designed head layers that allow pre-trained models to specialize effectively even with small, custom datasets, mitigating the risk of catastrophic forgetting. The framework is built on PyTorch Lightning, inheriting its scalability, cost-efficiency, and reliability for complex training pipelines.

Quick Start & Requirements

• Training: pip install quaterion
• Inference Service: pip install quaterion-models
• Prerequisites: PyTorch, PyTorch Lightning. GPU recommended for training.
• Docs: https://github.com/qdrant-tech/quaterion
• Tutorials: Fine-tuning NLP models, Fine-tuning CV models.

Highlighted Details

• Enables training thousands of epochs with large batch sizes on laptop GPUs via built-in caching.
• Compatible with small datasets, allowing effective fine-tuning with custom head layers.
• Highly customizable architecture for sophisticated training pipelines.
• Built on PyTorch Lightning for scalability and reliability.

Maintenance & Community

• Community support via Discord.
• Active development indicated by recent commits and releases.
• Contact: info@qdrant.tech

Licensing & Compatibility

• Licensed under Apache License, Version 2.0.
• Permissive license suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

The quaterion-models package is separate to minimize inference dependencies, requiring users to install the appropriate package based on their needs. Specific hardware requirements beyond a GPU for optimal training performance are not detailed.