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Description

Mono is an open-source implementation of the Microsoft .NET Framework, backed by Microsoft and part of the .NET Foundation, adhering to ECMA standards for C# and the common language runtime. It has become a growing ecosystem supported by an enthusiastic community of contributors, positioning itself as a top choice for creating applications that operate across multiple platforms. The latest version of Mono is now available, providing comprehensive guidance on setup and internal workings. Our documentation is also open-source, inviting collaboration from anyone interested in enhancing it. We encourage community involvement; whether you want to report bugs, contribute code, or engage directly with developers, your input is valued. In essence, Mono serves as a robust platform for developers aiming to build versatile applications that function seamlessly on various systems. The collaborative spirit of the Mono project fosters innovation and continuous improvement in cross-platform development.

Description

MonoQwen2-VL-v0.1 represents the inaugural visual document reranker aimed at improving the quality of visual documents retrieved within Retrieval-Augmented Generation (RAG) systems. Conventional RAG methodologies typically involve transforming documents into text through Optical Character Recognition (OCR), a process that can be labor-intensive and often leads to the omission of critical information, particularly for non-text elements such as graphs and tables. To combat these challenges, MonoQwen2-VL-v0.1 utilizes Visual Language Models (VLMs) that can directly interpret images, thus bypassing the need for OCR and maintaining the fidelity of visual information. The reranking process unfolds in two stages: it first employs distinct encoding to create a selection of potential documents, and subsequently applies a cross-encoding model to reorder these options based on their relevance to the given query. By implementing Low-Rank Adaptation (LoRA) atop the Qwen2-VL-2B-Instruct model, MonoQwen2-VL-v0.1 not only achieves impressive results but does so while keeping memory usage to a minimum. This innovative approach signifies a substantial advancement in the handling of visual data within RAG frameworks, paving the way for more effective information retrieval strategies.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

.NET
Babel Obfuscator
C#
Docker
Nancy
Visual Studio
Yuno

Integrations

.NET
Babel Obfuscator
C#
Docker
Nancy
Visual Studio
Yuno

Pricing Details

Free
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Mono

Website

www.mono-project.com

Vendor Details

Company Name

LightOn

Founded

2016

Country

France

Website

www.lighton.ai/lighton-blogs/monoqwen-vision

Product Features

Product Features

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