ABrain One - community-driven AI development, providing open-source projects and research tools. Our mission is to accelerate innovation in neural networks, large language models, and AI performance analysis, making advanced technology accessible. We specialize in creating high-quality, reproducible datasets and frameworks designed for both researchers and practitioners.
AI Linux
AI Linux is a production-ready, containerized environment specifically engineered for AI development. It provides a seamless, reproducible platform for deploying and running a wide range of AI projects, from cutting-edge Large Language Models (LLMs) to complex computer vision tasks. This powerful, preconfigured setup ensures compatibility and effortless integration of the latest tools and dependencies.
- Streamlined AI Development: A preconfigured environment with all the tools needed for model training and evaluation.
- Universal Compatibility: Supports all major neural network architectures used in our research labs.
- Designed for All Users: An essential toolkit for both academic researchers and professional AI practitioners.
LLM‑Based Neural Network
Generator (NNGPT)
Unleash the power of Large Language Models (LLMs) to autonomously create and refine neural network architectures. The NNGPT project uses generative AI to explore and propose optimal model designs. By leveraging the comprehensive NN Dataset, it fine-tunes models, automatically suggests high-quality architectures, and balances performance with resource demands.
- Automatic Architecture Generation: Use generative workflows to propose novel and efficient neural network designs.
- Integrated Evaluation: Built-in pipelines for benchmarking and comparing candidate models.
- Iterative Optimization: Continuously improve models through fine-tuning and feedback loops.
Neural
Network Dataset (LEMUR)
The LEMUR Neural Network Dataset is a dynamic, modular collection of neural network architectures, datasets, and evaluation metrics. It empowers users to combine various architectures and hyperparameters, providing a standardized and reproducible way to benchmark model performance. Released under a permissive open-source license, this project is continuously updated to support cutting-edge AI research.
- Comprehensive Library: Includes classic models like AlexNet and ResNet, alongside newer designs.
- Seamless Workflow: Features integrated preprocessing, data loaders, and metric evaluation.
- Data-Driven Insights: Easily export performance statistics to Excel and generate visualizations for deep analysis.
NN-RAG
NN-RAG is a robust framework for neural network-based Retrieval-Augmented Generation (RAG). It enhances LLMs by providing them with real-time context from external sources, whether local repositories or the internet. This project is essential for building models that require access to up-to-date, external knowledge, leading to more accurate and reliable generative AI systems.
- Knowledge Integration: Seamlessly combines retrieval-based components with generative models.
- Enhanced Model Performance: Improves accuracy for tasks that require external knowledge.
- Open-Source Framework: A flexible, open-source solution for research and practical AI applications.
NN-LITE
NN-LITE is the ultimate solution for optimizing and deploying efficient neural networks in constrained environments. This fully automated benchmarking suite generates a TFLite file from your model, runs it on an Android emulator, and provides a detailed JSON report with key performance metrics. It is the perfect tool for ensuring high accuracy and low latency in edge computing and real-time applications.
- Automated Benchmarking: A one-command solution for testing model performance on edge devices.
- Optimized for Efficiency: Designed to deploy lightweight models in resource-limited environments.
- Real-Time Performance: Ideal for low-latency applications on a wide range of neural network architectures.
Neural Network Performance Analysis (NN Stat)
NN Stat automates the statistical analysis of model training results, transforming raw data into clear, actionable insights. This tool generates compelling visualizations (PNG/SVG plots), exportable Excel tables, and comprehensive summary metrics. It is designed for seamless integration with the NN Dataset and NNGPT projects, providing a complete workflow for model evaluation and comparison.
- Effortless Reporting: Batch export statistics for multiple training trials.
- Powerful Visualizations: Generate plots for metrics like accuracy, loss curves, and more.
- Integrated Workflow: Fully compatible with the AI Linux environment for a consistent experience.
Why Use the AI Projects?
These projects are designed to streamline AI research and development with an emphasis on reproducibility and transparency. Each framework is carefully crafted to ensure high accuracy, performance, and community-driven improvements. By using standardized datasets and evaluation pipelines, we reduce experimental biases and increase the reliability of your results.
- Reproduce Model Training: Use fixed hyperparameter pipelines and standard datasets for consistent results.
- Streamline Evaluation: Compare alternative architectures with statistical summaries and visualizations.
- Ensure Consistency: Utilize containerization via AI Linux for a uniform environment across all projects.