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

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

This framework provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa and achieve state-of-the-art performance in various tasks. Text is embedded in vector space such that similar text are closer and can efficiently be found using cosine similarity.

This package provides easy access to pretrained models for more than 100 languages, fine-tuned for various use-cases.

Further, this framework allows an easy fine-tuning of custom embeddings models, to achieve maximal performance on your specific task.

Inquirer should ease the process of asking end user questions, parsing, validating answers, managing hierarchical prompts and providing error feedback.

This package provides provides an implementation of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for Python.

ML Collections is a library of Python collections designed for Machine Learning usecases.

Apache Burr makes it easy to develop applications that make decisions (chatbots, agents, simulations, etc...) from simple Python building blocks. Apache Burr works well for any application that uses LLMs, and can integrate with any of your favorite frameworks. Burr includes a UI that can track/monitor/trace your system in real time, along with pluggable persisters (e.g. for memory) to save and load application state.

Random Jungle is an implementation of Random Forests. It is supposed to analyse high dimensional data. In genetics, it can be used for analysing big Genome Wide Association (GWA) data. Random Forests is a powerful machine learning method. Most interesting features are variable selection, missing value imputation, classifier creation, generalization error estimation and sample proximities between pairs of cases.

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.

Uniform Manifold Approximation and Projection is a dimension reduction technique that can be used for visualization similarly to t-SNE, but also for general non-linear dimension reduction.

This package includes a variety of tools used to analyze persistence diagrams. It currently houses implementations of

• Persistence images

• Persistence landscapes

• Bottleneck distance

• Modified Gromov–Hausdorff distance

• Sliced Wasserstein kernel

• Heat kernel

• Diagram plotting

DeepXDE is a library for scientific machine learning and physics-informed learning. It includes implementations for the PINN (physics-informed neural networks), DeepONet (deep operator network) and MFNN (multifidelity neural network) algorithms.

LinearOperator is a PyTorch package for abstracting away the linear algebra routines needed for structured matrices (or operators).

Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text, Stanza divides it into sentences and words, and then can recognize parts of speech and entities, do syntactic analysis, and more.

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

GPy is a Gaussian Process (GP) framework written in Python, from the Sheffield machine learning group. GPy implements a range of machine learning algorithms based on GPs.

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.

Visualization and NeuroML import/export tools for the Brian 2 simulator.

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.

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 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.

SpaCy is a library for advanced Natural Language Processing in Python and Cython. It comes with pretrained pipelines and currently supports tokenization and training for 70+ languages. It features state-of-the-art speed and neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT, as well as a production-ready training system and easy model packaging, deployment and workflow management.

ONNX Interface for Framework Integration is a cross-platform API for loading and executing ONNX graphs on optimized backends. This package contains facebook extensions and is used by PyTorch.

This package provides a collection of ordinal regression models for machine learning in Python. They are intended to be used with scikit-learn and are compatible with its API.