Transformer GitHub Repositories (2026) · Open Source Tools

FreeToken brings datacenter-scale model serving to your desktop. Run massive models locally, fast and efficiently.

🤗 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

Port of OpenAI's Whisper model in C/C++

A framework for efficient model inference with omni-modality models

A vLLM-inspired inference engine for looped transformers, with loop-level scheduling and execution across composable stages

Formula Recognition & Office Editing | Handwriting & PDF to LaTeX/Markdown | Secure API Integrations.

SGLang is a high-performance serving framework for large language models and multimodal models.

An open, programmable decision layer for models and compute.

Fully automatic censorship removal for language models

Scalable and user friendly neural :brain: forecasting algorithms.

Community maintained hardware plugin for vLLM on Huawei Ascend

Official PyTorch implementation of Superpoint Transformer [ICCV'23], SuperCluster [3DV'24 Oral], and EZ-SP [ICRA'26]

cuDNN Frontend is NVIDIA's modern, open-source entry point to the cuDNN library and a growing collection of high-performance open-source kernels.

A personal research and development (R&D) lab that facilitates the sharing of knowledge.

A framework for state-of-the-art pre-trained bio foundation models on genomics and transcriptomics modalities.

:sunrise: A comprehensive, cutting-edge open-source framework for next-generation Transformers, with compute-matched benchmarking and a platform for autonomous architecture research.

GPT-2 transformer for controllable, multitrack symbolic music generation

Official PyTorch implementation of HGPTrans: hierarchical graph-pooling Transolver for vehicle aerodynamic drag prediction (Cd regression) from STL meshes.

Physics-guided augmentation and multi-scale Transformer learning for low-resource spectroscopy; CNMM-MSST XRF implementation optimized based on the JAAS paper.