From 6ad67332c6f3592e8c7123de700fed95a96dc912 Mon Sep 17 00:00:00 2001 From: Peter MIKOLA Date: Sat, 17 Aug 2024 13:13:53 +0200 Subject: [PATCH] :art: format ACCELERATE page and update project index --- content/pages/accelerate.md | 47 +++++++++++++++++++++++++++----- content/pages/projects/data.toml | 4 +-- 2 files changed, 42 insertions(+), 9 deletions(-) diff --git a/content/pages/accelerate.md b/content/pages/accelerate.md index a06fe94..ce50512 100644 --- a/content/pages/accelerate.md +++ b/content/pages/accelerate.md @@ -3,16 +3,49 @@ title = "ACCELERATE - Modular Abstraction Layer for Simplified Integration of Ma path = "accelerate" +++ -This project aims to develop a modular machine learning model abstraction layer for the novel open source browser “Verso” to seamlessly load and utilize various machine learning models. The proposed solution involves using the Rust programming language and will provide bindings to existing machine learning libraries, use common model formats such as ONNX, and implement a basic API for interaction with different model architectures and types. This approach aims to make it easy for everyday users to load and run AI models without worrying about specifics, and further also provides web developers with simpler access through a minimal API for integration in web content. +This project aims to develop a modular machine-learning model abstraction layer for the novel open-source +browser `Verso` to load and utilize various machine-learning models seamlessly. +The proposed solution involves using the Rust programming language and will provide bindings to existing +machine learning libraries, use common model formats such as `ONNX`, and implement a basic API for +interaction with different model architectures and types. +This approach aims to make it easy for everyday users to load and run AI models without worrying about +specifics, and further also provides web developers with simpler access through a minimal API for +integration in web content. +The intended abstraction layer will be implemented in the `Servo` webview, and the expected deliverable is +a source code library that runs cross-platform within the Verso browser. With this approach, we aim to +provide the first stepping stone towards a coherent user experience for using machine learning models +in a browser such as a11y, DID service brokerage, predictive page loading, etc. -The intended abstraction layer will be implemented in the Servo webview, and the expected deliverable is a source code library that runs cross-platform within the Verso browser. With this approach, we aim to provide the first stepping stone towards a coherent user experience for using machine learning models in a browser such as a11y, DID service brokerage, predictive page loading, etc. - -By addressing several key challenges, such as defining a common API for different open source machine learning models, ensuring data privacy and security, striking a balance between smaller and high-performance models, and considering various hardware configurations, this project aims to significantly enhance user experiences. +By addressing several key challenges, such as defining a common API for different open-source machine +learning models, ensuring data privacy and security, striking a balance between smaller and +high-performance models, and considering various hardware configurations, this project aims to +significantly enhance user experiences. ## Innovation -With the Verso browser, we aim to revolutionize web browsing through comprehensive integration of on-device AI. Unlike Firefox Nightly's experimental AI features, Verso offers a robust, fully integrated experience. Our key innovation is a highly adaptable abstraction layer that seamlessly incorporates standardized AI models, including transformers and computer vision models like YOLO V9 for object detection. The abstraction layer supports multiple model formats, adapts to various hardware configurations, and provides a common API for diverse machine learning tasks, aiding Verso developers. It enables advanced features like intelligent content summarization and image description, offering a more cohesive and user-friendly experience than Firefox. Verso-Accelerate focuses on balancing high-performance models with hardware compatibility, ensuring efficient machine learning enhanced browsing across devices. By leveraging and expanding upon open-source machine learning models, the abstraction layer for Verso will set a new standard for intelligent, privacy-preserving web interaction, enhancing user productivity and accessibility. +With the Verso browser, we aim to revolutionize web browsing through comprehensive integration of on-device +AI. Unlike Firefox Nightly's experimental AI features, `Verso` offers a robust, fully integrated experience. +Our key innovation is a highly adaptable abstraction layer that seamlessly incorporates standardized AI +models, including transformers and computer vision models like `YOLO V9` for object detection. +The abstraction layer supports multiple model formats, adapts to various hardware configurations, and +provides a common API for diverse machine learning tasks, aiding `Verso` developers. +It enables advanced features like intelligent content summarization and image description, offering a +more cohesive and user-friendly experience than Firefox. The [Verso Accelerator] focuses on balancing +high-performance models with hardware compatibility, ensuring efficient machine learning enhanced +browsing across devices. By leveraging and expanding upon open-source machine learning models, the +abstraction layer for `Verso` will set a new standard for intelligent, privacy-preserving web interaction, +enhancing user productivity and accessibility. + +## Modularity and Envisioned Use Cases -# Modularity and Envisioned Use Cases +We're developing `ACCELERATE` as a seamless module, easily loadable within `Verso` as an opt-in feature. +Our goal is to empower users with the flexibility to enhance their browsing experience. +With our model loader, you can experience a more natural reading experience through text-to-speech models, +supporting individuals with vision impairments. The addition of live captions to videos further expands +accessibility features, ensuring that everyone can engage with multimedia content. The model loader also +enables the summary feature for website content, allowing users to quickly grasp essential information at +a glance. This is especially useful in today's fast-paced digital environment where information density +can be overwhelming. By integrating these features into your browsing experience, we're committed to +making your online interactions more intuitive and enjoyable. -We're developing ACCELERATE as a seamless module, easily loadable within Verso as an opt-in feature. Our goal is to empower users with the flexibility to enhance their browsing experience. With our model loader, you can experience a more natural reading experience through text-to-speech models, supporting individuals with vision impairments. The addition of live captions to videos further expands accessibility features, ensuring that everyone can engage with multimedia content. The model loader also enables the summary feature for website content, allowing users to quickly grasp essential information at a glance. This is especially useful in today's fast-paced digital environment where information density can be overwhelming. By integrating these features into your browsing experience, we're committed to making your online interactions more intuitive and enjoyable. +[Verso Accelerator]: https://github.com/versotile-org/verso-accelerator diff --git a/content/pages/projects/data.toml b/content/pages/projects/data.toml index f491d07..6581a06 100644 --- a/content/pages/projects/data.toml +++ b/content/pages/projects/data.toml @@ -19,9 +19,9 @@ links = [ ] [[project]] -name = "Accelerate" +name = "ACCELERATE" desc = "Simplified Machine Learning Model Abstraction Layer" -tags = ["machine-learning", "llm", "transformer", "model", "loading"] +tags = ["machine-learning", "llm", "transformer", "model-loading"] links = [ { name = "homepage", url = "/accelerate" }, { name = "github", url = "https://github.com/versotile-org/verso-accelerator" },