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Description
MonoGame is an open-source framework that empowers developers to build cross-platform games utilizing C# and various .NET languages. It is compatible with an array of platforms, such as Windows, macOS, Linux, Android, iOS, PlayStation 4, PlayStation 5, Xbox One, and Nintendo Switch. This framework boasts an extensive range of features, including capabilities for 2D and 3D rendering, sound management, input processing, and content organization, which facilitate the creation of high-quality games in different genres. Serving as a re-imagining of Microsoft's XNA 4 API, MonoGame offers a familiar environment for those who have previously worked with XNA. Noteworthy titles crafted with MonoGame include "Streets of Rage 4," "Carrion," "Celeste," and "Stardew Valley," showcasing the framework's versatility and effectiveness. The MonoGame Foundation, along with a dedicated community, actively oversees the ongoing development and enhancement of the framework, ensuring it remains a valuable tool for game developers. With continuous updates, MonoGame strives to meet the evolving needs of the gaming industry.
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
Integrations
.NET
Android
Apple iOS
Apple iPadOS
C#
Rider
Ubuntu
Visual Studio
Visual Studio Code
Windows 11
Integrations
.NET
Android
Apple iOS
Apple iPadOS
C#
Rider
Ubuntu
Visual Studio
Visual Studio Code
Windows 11
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
MonoGame
Founded
2009
Country
United States
Website
monogame.net
Vendor Details
Company Name
LightOn
Founded
2016
Country
France
Website
www.lighton.ai/lighton-blogs/monoqwen-vision