Popular GitHub Repositories for Transformer Encoder

? Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

??? 60+ Implementations/tutorials of deep learning papers with side-by-side notes ?; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), ? reinforcement learning (ppo, dqn), capsnet, distillation, ... ?

Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

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

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

?? - a deep learning toolkit for Text-to-Speech, battle-tested in research and production

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

OpenMMLab Detection Toolbox and Benchmark

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

SOTA Open Source TTS

? A ranked list of awesome machine learning Python libraries. Updated weekly.

CVPR 2025 论文和开源项目合集

[NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer

A neural network that transforms a design mock-up into a static website.

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

pix2tex: Using a ViT to convert images of equations into LaTeX code.

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

Natural Language Processing Tutorial for Deep Learning Researchers

RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.

Ongoing research training transformer models at scale