The GeomLoss library provides efficient GPU implementations for:

• Kernel norms (also known as Maximum Mean Discrepancies).

• Hausdorff divergences, which are positive definite generalizations of the Chamfer-ICP loss and are analogous to log-likelihoods of Gaussian Mixture Models.

• Debiased Sinkhorn divergences, which are affordable yet positive and definite approximations of Optimal Transport (Wasserstein) distances.

MNN is a deep learning framework. It supports inference and training of deep learning models for inference and training on-device.

KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models, builds off llama.cpp and adds many additional features:

• Runs on CPU or GPU, supports full or partial offloaded

• LLM text generation (Supports all GGML and GGUF models, backwards compatibility with ALL past models)

• Image Generation (Stable Diffusion 1.5, SDXL, SD3, Flux)

• Speech-To-Text (Voice Recognition) via Whisper

• Text-To-Speech (Voice Generation) via OuteTTS, Kokoro, Parler and Dia

• Provides many compatible APIs endpoints for many popular webservices (KoboldCppApi OpenAiApi OllamaApi A1111ForgeApi ComfyUiApi WhisperTranscribeApi XttsApi OpenAiSpeechApi)

• Bundled KoboldAI Lite UI with editing tools, save formats, memory, world info, author's note, characters, scenarios

• Includes multiple modes (chat, adventure, instruct, storywriter) and UI Themes (aesthetic roleplay, classic writer, corporate assistant messsenger)

• Supports loading Tavern Character Cards, importing many different data formats from various sites, reading or exporting JSON savefiles and persistent stories

• Many other features including new samplers, regex support websearch, RAG via TextDB, image recognition/vision and more

All up-to-date GGUF models are supported, and KoboldCpp also includes backward compatibility for older versions/legacy GGML models.

ONNX Runtime is a performance-focused complete scoring engine for Open Neural Network Exchange (ONNX) models, with an open extensible architecture to continually address the latest developments in AI and Deep Learning. ONNX Runtime stays up to date with the ONNX standard with complete implementation of all ONNX operators, and supports all ONNX releases (1.2+) with both future and backwards compatibility.

This package provides common Python utilities and GitHub Actions for the Lightning suite of libraries.

This package provides fast machine learning algorithms including matrix factorization and divisive clustering for large sparse and dense matrices.

paramz is a lightweight parameterization framework for parameterized model creation and handling. Its features include:

• Easy model creation with parameters.

• Fast optimized access of parameters for optimization routines.

• Memory efficient storage of parameters (only one copy in memory).

• Renaming of parameters.

• Intuitive printing of models and parameters.

• Gradient saving directly inside parameters.

• Gradient checking of parameters.

• Optimization of parameters.

• Jupyter notebook integration.

• Efficient storage of models, for reloading.

• Efficient caching.

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

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 a Python library for translating plain- and rich-text documents with Argos Translate.

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.

This package provides a standard API for reinforcement learning and a diverse set of reference environments (formerly Gym).

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 a functional take on deep learning, compatible with your favorite libraries.

The MCL algorithm is short for the Markov Cluster Algorithm, a fast and scalable unsupervised cluster algorithm for graphs (also known as networks) based on simulation of (stochastic) flow in graphs.

CTranslate2 is a C++ and Python library for efficient inference with Transformer models.

The project implements a custom runtime that applies many performance optimization techniques such as weights quantization, layers fusion, batch reordering, etc., to accelerate and reduce the memory usage of Transformer models on CPU and GPU.

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

Pomegranate is a graphical models library for Python, implemented in Cython for speed.

This tool provides ordinary differential equation solvers implemented in PyTorch. Backpropagation through ODE solutions is supported using the adjoint method for constant memory cost.

This package provides a reimplementation of OpenAI's Whisper model using CTranslate2, which is a inference engine for transformer models.

GPyTorch is a Gaussian process library implemented using PyTorch.

TorchMetrics is a collection of 100+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. It offers:

• A standardized interface to increase reproducibility

• Reduces boilerplate

• Automatic accumulation over batches

• Metrics optimized for distributed-training

• Automatic synchronization between multiple devices

jaxtyping provides type annotations and runtime checking for shape and dtype of JAX arrays, PyTorch, NumPy, TensorFlow, and PyTrees.

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