Popular GitHub Repositories for Transformer Js

? 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++

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

?Vite+Vue3+Gin拥有AI辅助的基础开发平台，支持TS和JS混用。它集成了JWT鉴权、权限管理、动态路由、显隐可控组件、分页封装、多点登录拦截、资源权限、上传下载、代码生成器、表单生成器和可配置的导入导出等开发必备功能。

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

JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js

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

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