Code and documentation to train Stanford's Alpaca models, and generate the data.
- deep-learning
- instruction-following
- language-model
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Code and documentation to train Stanford's Alpaca models, and generate the data.
The #1 AI Harness for Building Resumes, PDFs, Cover Letters & more, locally with 100+ LLMs support.
Lightning ⚡️ fast forecasting with statistical and econometric models.
Scalable and user friendly neural :brain: forecasting algorithms.
Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
Live Helper Chat - live support for your website. Featuring web and mobile apps, Voice & Video & ScreenShare. Supports Telegram, Twilio (whatsapp), Facebook messenger including building a bot.
:video_game: Unity SDK to use the IBM Watson services.
Large collection of number systems providing custom arithmetic for mixed-precision algorithm development and optimization for AI, Machine Learning, Computer Vision, Signal Processing, CAE, EDA, control, optimization, estimation, and approximation.
Self-hosted artifact registry and pull-through cache in one binary. 17 formats: Docker/OCI, Maven, npm, PyPI, Cargo, Go, Raw, RubyGems, Terraform, Ansible Galaxy, NuGet, Pub, Conan, CPAN, RPM, Debian/APT, Hugging Face. No database, about 15 MB RAM idle, supply-chain curation built in.
Imgcompress is a self-hosted image processing toolbox that handles compression, format conversion, and AI background removal in a single web interface. It supports over 70 input formats (including PSD, HEIC, and RAW) and can output common formats or generate PDFs, all without sending files to external services.
Library for using the IBM Watson Speech to Text and Text to Speech services in web browsers.
Ruby speech recognition with Pocketsphinx
Sparse matrix formats for linear algebra supporting scientific and machine learning applications
A Genetic Programming platform for Python with TensorFlow for wicked-fast CPU and GPU support.
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
MCP server for spatial transcriptomics analysis through natural language interfaces.
GoStats is a go library for math statistics mostly used in ML domains, it covers most of the statistical measures functions.
A dependency-free Linear Algebra, Statistics, and Machine Learning library written from scratch in V