matteogeniaccio/GLM-4-32B-0414-GGUF-fixed
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Attashe
attashe
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Neural Network, Object detection, Generative Art
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attashe's activity
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š Empowering Seamless AI App Development without GPU Cloud server: Introducing ZETIC.MLange!
š± We are thrilled to announce the launch of ZETIC.MLange, end-to-end development tool designed to simplify the creation and deployment of on-device AI solutions.
š” What is ZETIC.MLange?
ZETIC.MLange is a comprehensive development platform that enables developers to build, test, and deploy AI models directly on devices. By facilitating on-device AI development, it offers benefits such as enhanced privacy, reduced latency, and offline functionality.
š® Key Features:
- End-to-End Workflow: One-stop implementation from your AI model to Application project
- On-Device Optimization: Tailor models for efficient performance on various hardware platforms.
- User-Friendly Interface: Intuitive tools and documentation to assist both novice and experienced developers.
š Explore More!
- How-to Video: https://youtu.be/MKYi09eRNUE?si=dgNm6VQ-klMyek0q
- Product Page: https://mlange.zetic.ai
- Experience ZETIC.MLange on Mobile:
- Google Play Store: https://play.google.com/store/apps/details?id=com.zeticai.zeticapp
- Apple App Store: https://apps.apple.com/app/zeticapp/id6739862746
š« We believe ZETIC.MLange will be a valuable asset for developers aiming to harness the power of on-device AI. We invite you to explore its capabilities and share your feedback.
#OnDeviceAI #ZETICMLange #AIDevelopment #EdgeComputing
š± We are thrilled to announce the launch of ZETIC.MLange, end-to-end development tool designed to simplify the creation and deployment of on-device AI solutions.
š” What is ZETIC.MLange?
ZETIC.MLange is a comprehensive development platform that enables developers to build, test, and deploy AI models directly on devices. By facilitating on-device AI development, it offers benefits such as enhanced privacy, reduced latency, and offline functionality.
š® Key Features:
- End-to-End Workflow: One-stop implementation from your AI model to Application project
- On-Device Optimization: Tailor models for efficient performance on various hardware platforms.
- User-Friendly Interface: Intuitive tools and documentation to assist both novice and experienced developers.
š Explore More!
- How-to Video: https://youtu.be/MKYi09eRNUE?si=dgNm6VQ-klMyek0q
- Product Page: https://mlange.zetic.ai
- Experience ZETIC.MLange on Mobile:
- Google Play Store: https://play.google.com/store/apps/details?id=com.zeticai.zeticapp
- Apple App Store: https://apps.apple.com/app/zeticapp/id6739862746
š« We believe ZETIC.MLange will be a valuable asset for developers aiming to harness the power of on-device AI. We invite you to explore its capabilities and share your feedback.
#OnDeviceAI #ZETICMLange #AIDevelopment #EdgeComputing
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# Video Tokenization ā for efficient AI video processing
Meet šš¢šššØš¤, a new open-source video tokenization technique developed by Microsoft Research to address the computational challenges of processing large volumes of video data. The core problem VidTok tackles is the inefficiency caused by redundant information in raw video pixels.
VidTok converts complex video footage into compact, structured units called tokens, making it easier and more efficient for AI systems to analyze, understand, and generate video content.
Research Paper: https://arxiv.org/abs/2412.13061
VidTok Code: https://github.com/microsoft/VidTok
Meet šš¢šššØš¤, a new open-source video tokenization technique developed by Microsoft Research to address the computational challenges of processing large volumes of video data. The core problem VidTok tackles is the inefficiency caused by redundant information in raw video pixels.
VidTok converts complex video footage into compact, structured units called tokens, making it easier and more efficient for AI systems to analyze, understand, and generate video content.
Research Paper: https://arxiv.org/abs/2412.13061
VidTok Code: https://github.com/microsoft/VidTok
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Dino: The Minimalist Multipurpose Chat System š
Github: https://github.com/PRITHIVSAKTHIUR/Agent-Dino
By default, it performs the following tasks:
{Text-to-Text Generation}, {Image-Text-Text Generation}
Github: https://github.com/PRITHIVSAKTHIUR/Agent-Dino
By default, it performs the following tasks:
{Text-to-Text Generation}, {Image-Text-Text Generation}
@image: Generates an image using Stable Diffusion xL. @3d: Generates a 3D mesh. @web: Web search agents.@rAgent: Initiates a reasoning chain using Llama mode for coding explanations. @tts1-ā, @tts2-ā: Voice generation (Female and Male voices). @yolo : Object Detection
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