• Docs >
  • Welcome to Quaterion’s documentation!

Welcome to Quaterion’s documentation!

Quaterion is a framework for fine-tuning similarity learning models. The framework closes the “last mile” problem in training models for semantic search, recommendations, anomaly detection, extreme classification, matching engines, e.t.c.

It is designed to combine the performance of pre-trained models with specialization for the custom task while avoiding slow and costly training.


  • 🌀 Warp-speed fast: With the built-in caching mechanism, Quaterion enables you to train thousands of epochs with huge batch sizes even on laptop GPU.

  • 🐈‍ Small data compatible: Pre-trained models with specially designed head layers allow you to benefit even from a dataset you can label in one day.

  • 🏗️ Customizable: Quaterion allows you to re-define any part of the framework, making it flexible even for large-scale and sophisticated training pipelines.

  • 🌌 Scalable: Quaterion is built on top of PyTorch Lightning and inherits all its scalability, cost-efficiency, and reliability perks.



For training:

pip install quaterion

For inference service:

pip install quaterion-models

Quaterion framework consists of two packages - quaterion and quaterion-models.

Since it is not always possible or convenient to represent a model in ONNX format (also, it is supported), the Quaterion keeps a very minimal collection of model classes, which might be required for model inference, in a separate package.

It allows avoiding installing heavy training dependencies into inference infrastructure: pip install quaterion-models

At the same time, once you need to have a full arsenal of tools for training and debugging models, it is available in one package: pip install quaterion

Next Steps

Indices and tables


Learn more about Qdrant vector search project and ecosystem

Discover Qdrant

Similarity Learning

Explore practical problem solving with Similarity Learning

Learn Similarity Learning


Find people dealing with similar problems and get answers to your questions

Join Community