libfm-qt is the Qt port of libfm, a library providing components to build desktop file managers which belongs to LXDE.

This package comprises a number of graphic files and themes for LXQt.

lxqt-qtplugin is providing a library libqtlxqt to integrate Qt with LXQt.

lxqt-openssh-askpass is a GUI to query credentials on behalf of other programs.

libsysstat is a library to query system information like CPU and memory usage or network traffic.

This library provides a Qt implementation of the DBusMenu protocol, forked from libdbusmenu-qt. The DBusMenu protocol makes it possible for applications to export and import their menus over DBus.

ObConf-Qt is a Qt port of ObConf, a configuration editor for window manager OpenBox.

lxqt-powermanagement is providing tools to monitor power management events and optionally trigger actions like e. g. shut down a system when laptop batteries are low on power.

lxqt-sudo is a graphical front-end of commands sudo and su respectively. As such it enables regular users to launch applications with permissions of other users including root.

lxqt-config is providing several tools involved in the configuration of both LXQt and the underlying operating system.

GNU M4 is an implementation of the M4 macro language, which features some extensions over other implementations, some of which are required by GNU Autoconf. It is used as a macro processor, which means it processes text, expanding macros as it encounters them. It also has some built-in functions, for example to run shell commands or to do arithmetic.

This package provides a Python implementation of the CMA-ES algorithm and a few related numerical optimization tools.

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 Plain C/C++ implementation based on ggml, working in the same way as llama.cpp. It supports diffusion models such as SD, Flux, Wan, Qwen and Image,Z-Image.

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

Brian is a simulator for spiking neural networks written in Python. It is therefore designed to be easy to learn and use, highly flexible and easily extensible.

This package provides easy download of thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.

These models can be applied on:

• Text, for tasks like text classification, information extraction, question answering, summarization, translation, and text generation, in over 100 languages.

• Images, for tasks like image classification, object detection, and segmentation.

• Audio, for tasks like speech recognition and audio classification.

Transformer models can also perform tasks on several modalities combined, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.

This package provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community. At the same time, each Python module defining an architecture is fully standalone and can be modified to enable quick research experiments.

Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration between them.

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.

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.

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

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 stand-alone implementation of several NumPy dtype extensions used in machine learning libraries, including:

• bfloat16: an alternative to the standard float16 format

• float8_*: several experimental 8-bit floating point representations including:

  • float8_e4m3b11fnuz

  • float8_e4m3fn

  • float8_e4m3fnuz

  • float8_e5m2

  • float8_e5m2fnuz

• int4 and uint4: low precision integer types.

This package provides a speech recognition toolkit based on kaldi. It supports more than 20 languages and dialects - English, Indian English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish, Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi, Filipino, Ukrainian, Kazakh, Swedish, Japanese, Esperanto, Hindi, Czech, Polish. The program works offline, even on lightweight devices. Portable per-language models are about 50Mb each, and there are much bigger and precise models available.

Vosk API provides a streaming API allowing to use it on-the-fly and bindings for different programming languages. It allows quick reconfiguration of vocabulary for better accuracy, and supports speaker identification beside simple speech recognition.

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.