SGLang is a fast serving framework for large language models and vision language models.

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

A high-throughput and memory-efficient inference and serving engine for LLMs

Text utilities, including beam search decoding, tokenizing, and more, built for use in Flashlight.

Sequence algorithms for use in Flashlight.

A C++ standalone library for machine learning

KenLM: Faster and Smaller Language Model Queries

C++ Library Manager for Windows, Linux, and MacOS

Self-Supervised Speech/Sound Pre-training and Representation Learning Toolkit

oneAPI Deep Neural Network Library (oneDNN)

Transformer related optimization, including BERT, GPT

Fast lattice reduction

Optimizing the Lifetime and Location of Arrays to Reduce the Memory Usage of Neural Networks

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

UniSpeech - Large Scale Self-Supervised Learning for Speech

Fast ML in JS with Bun + Flashlight

Pretraining, fine-tuning and evaluation scripts for Indic-Wav2Vec2

flashlight tap

Repository containing experimentation platform on how to train, infer on wav2vec2 models.

ArrayFire: a general purpose GPU library.

Facebook AI Research's Automatic Speech Recognition Toolkit

A suite of tools for managing crowdsourcing tasks from the inception through to data packaging for research use

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

Automatic differentiation with weighted finite-state transducers.

Example pybind11 module built with a CMake-based build system

Googletest - Google Testing and Mocking Framework

dataset for lightly supervised training using the librivox audio book recordings. https://librivox.org/.