Interpretable ML (iML) is a set of data type objects, visualizations, and interfaces that can be used by any method designed to explain the predictions of machine learning models (or really the output of any function). It currently contains the interface and IO code from the Shap project, and it will potentially also do the same for the Lime project.

TensorFlow is a flexible platform for building and training machine learning models. This package provides the "lite" variant for mobile devices.

This package provides multiplicatively convolutional fast integral transforms.

ONNX is a format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.

Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions.

This package provides a port to Facebook's LLaMA collection of foundation language models. It requires models parameters to be downloaded independently to be able to run a LLaMA model.

fastText is a library for efficient learning of word representations and sentence classification.

Dlib is a modern C++ toolkit containing machine learning algorithms and tools. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments.

This is a Python library that aims at making tensor learning simple and accessible. It allows performing tensor decomposition, tensor learning and tensor algebra easily. Its backend system allows seamlessly perform computation with NumPy, PyTorch, JAX, MXNet, TensorFlow or CuPy and run methodxs at scale on CPU or GPU.

This package provides a C++ and Python library for performing arbitrary optimizations on ONNX models, as well as a growing list of prepackaged optimization passes.

Not all possible optimizations can be directly implemented on ONNX graphs--- some will need additional backend-specific information---but many can, and the aim is to provide all such passes along with ONNX so that they can be re-used with a single function call.

This package is a high-performance inference of OpenAI's Whisper automatic speech recognition (ASR) model, implemented in plain C/C++ without dependencies, with

• AVX intrinsics support for x86 architectures

• VSX intrinsics support for POWER architectures

• Mixed F16 / F32 precision

• 4-bit and 5-bit integer quantization support

• Zero memory allocations at runtime

• Support for CPU-only inference

• Efficient GPU support for NVIDIA

• OpenVINO Support

• C-style API

This package implements the Hopcroft-Karp algorithm, producing a maximum cardinality matching from a bipartite graph.

This package provides simple access speech to text for using in Linux without being tied to a desktop environment, using the vosk-api. The user configuration lets you manipulate text using Python string operations. It has zero overhead, as this relies on manual activation and there are no background processes. Dictation is accessed manually with nerd-dictation begin and nerd-dictation end commands.

This package provides simple access speech to text for using in Linux without being tied to a desktop environment, using the vosk-api. The user configuration lets you manipulate text using Python string operations. It has zero overhead, as this relies on manual activation and there are no background processes. Dictation is accessed manually with nerd-dictation begin and nerd-dictation end commands.

This package is a toolbox for optimization on Riemannian manifolds with support for automatic differentiation.

Gloo is a collective communications library. It comes with a number of collective algorithms useful for machine learning applications. These include a barrier, broadcast, and allreduce.

TensorFlow Lite for Microcontrollers is a port of TensorFlow Lite designed to run machine learning models on DSPs, microcontrollers and other devices with limited memory.

Scikit-learn provides simple and efficient tools for data mining and data analysis.

HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. HDBSCAN is ideal for exploratory data analysis; it's a fast and robust algorithm that you can trust to return meaningful clusters (if there are any).

This package provides a Python library for probabilistic modeling and inference.

A Python library for reading and writing GGUF & GGML format ML models.

LIBSVM is a machine learning library for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.

This package provides an implementation of today’s most used tokenizers, with a focus on performance and versatility.

DMLC-Core is the backbone library to support all DMLC projects, offers the bricks to build efficient and scalable distributed machine learning libraries.