Files
landing_page/qdrant-landing/content/blog/introducing-the-quaterion-a-framework-for-fine-tuning-similarity-learning-models.md
T
treanandqdrant 0854e89482 Excluded blog from the sitemap (#106)
* excluded blog from the sitemap

* docs auto-sync

* docs auto-sync

Co-authored-by: qdrant <qdrant@users.noreply.github.com>
2023-01-11 13:36:30 +01:00

40 lines
2.0 KiB
Markdown
Raw Blame History

This file contains invisible Unicode characters
This file contains invisible Unicode characters that are indistinguishable to humans but may be processed differently by a computer. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.
---
draft: false
title: "Introducing the Quaterion: a framework for fine-tuning similarity
learning models"
slug: quaterion
short_description: Please meet Quaterion—a framework for training and
fine-tuning similarity learning models.
description: We're happy to share the result of the work we've been into during
the last months - [Quaterion](https://quaterion.qdrant.tech/). It is a
framework for fine-tuning similarity learning models that streamlines the
training process to make it significantly faster and cost-efficient.
preview_image: /blog_data/from_cms/new-cmp-demo.gif
date: 2022-06-28T12:48:36.622Z
author: Andrey Vasnetsov
author_link: https://www.linkedin.com/in/andrey-vasnetsov-75268897/
featured: true
categories:
- News
- Release
- Quaterion
tags:
- Corporate news
- Release
- Quaterion
- PyTorch
sitemapExclude: True
---
We're happy to share the result of the work we've been into during the last months - [Quaterion](https://quaterion.qdrant.tech/). It is a framework for fine-tuning similarity learning models that streamlines the training process to make it significantly faster and cost-efficient.
To develop Quaterion, we utilized PyTorch Lightning, leveraging a high-performing AI research approach to constructing training loops for ML models.
![quaterion](/blog_data/from_cms/new-cmp-demo.gif)
This framework empowers vector search [solutions](https://qdrant.tech/solutions/), such as semantic search, anomaly detection, and others, by advanced coaching mechanism, specially designed head layers for pre-trained models, and high flexibility in terms of customization according to large-scale training pipelines and other features.
Here you can read why similarity learning is preferable to the traditional machine learning approach and how Quaterion can help benefit <https://quaterion.qdrant.tech/getting_started/why_quaterion.html#why-quaterion>   
A quick start with Quaterion:<https://quaterion.qdrant.tech/getting_started/quick_start.html>\
\
And try it and give us a star on GitHub :) <https://github.com/qdrant/quaterion>