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.

ggml is a ML library written in C and C++ with a focus on transformer inference, similar to ML libraries such as PyTorch and TensorFlow.

FANN is a neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks.

Captum is a model interpretability and understanding library for PyTorch. Captum contains general purpose implementations of integrated gradients, saliency maps, smoothgrad, vargrad and others for PyTorch models. It has quick integration for models built with domain-specific libraries such as torchvision, torchtext, and others.

This package provides logging utilities for the SpaCy natural language processing framework.

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.

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.

Argos Translate uses OpenNMT for translations and can be used as either a Python library, command-line, or GUI application. It supports installing language model packages which are ZIP archives with a .argosmodel extension. This package provides a Qt interface for Argos Translate.

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

NNPACK is an acceleration package for neural network computations. NNPACK aims to provide high-performance implementations of convnet layers for multi-core CPUs.

NNPACK is not intended to be directly used by machine learning researchers; instead it provides low-level performance primitives leveraged in leading deep learning frameworks, such as PyTorch, Caffe2, MXNet, tiny-dnn, Caffe, Torch, and Darknet.

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 small self-contained low-precision general matrix multiplication (GEMM) library. It is not a full linear algebra library. Low-precision means that the input and output matrix entries are integers on at most 8 bits. To avoid overflow, results are internally accumulated on more than 8 bits, and at the end only some significant 8 bits are kept.

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.

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

QNNPACK is a library for low-precision neural network inference. It contains the implementation of common neural network operators on quantized 8-bit tensors.

GPyTorch is a Gaussian process library implemented using PyTorch.

OpenFst is a library for constructing, combining, optimizing, and searching weighted finite-state transducers (FSTs).

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

LibreTranslate is a machine translation API and web interface, powered by the Argos Translate library.

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.

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.

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.

LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:

• Faster training speed and higher efficiency

• Lower memory usage

• Better accuracy

• Parallel and GPU learning supported (not enabled in this package)

• Capable of handling large-scale data

This package includes the Python scripts included in llama.cpp. These scripts are necessary for format conversion, shader generation, etc.