Popular GitHub Repositories for Learned Step Size

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

Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.

Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.

Kodu is an autonomous coding agent that lives in your IDE. It is a VSCode extension that can help you build your dream project step by step by leveraging the latest technologies in automated coding agents

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

This is a Phi Family of SLMs book for getting started with Phi Models. Phi a family of open sourced AI models developed by Microsoft. Phi models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks

Lightning-fast serving engine for any AI model of any size. Flexible. Easy. Enterprise-scale.

The leading workflow orchestration platform. Run stateful step functions and AI workflows on serverless, servers, or the edge.

校招、秋招、春招、实习好项目！带你从零实现一个高性能的深度学习推理库，支持大模型 llama2 、Unet、Yolov5、Resnet等模型的推理。Implement a high-performance deep learning inference library step by step

???YOLOv5-Lite: Evolved from yolov5 and the size of model is only 900+kb (int8) and 1.7M (fp16). Reach 15 FPS on the Raspberry Pi 4B~

One-step image-to-image with Stable Diffusion turbo: sketch2image, day2night, and more

Rainbow is all you need! A step-by-step tutorial from DQN to Rainbow

An introductory series to Reinforcement Learning (RL) with comprehensive step-by-step tutorials.

Official repository of my book: "Deep Learning with PyTorch Step-by-Step: A Beginner's Guide"

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

:oncoming_automobile: "MORE THAN VEHICLE COUNTING!" This project provides prediction for speed, color and size of the vehicles with TensorFlow Object Counting API.

Pytorch Implementation (unofficial) of the paper "Mean Flows for One-step Generative Modeling" by Geng et al.

Implementation of MEGABYTE, Predicting Million-byte Sequences with Multiscale Transformers, in Pytorch

The nnsight package enables interpreting and manipulating the internals of deep learned models.

Achieve the llama3 inference step-by-step, grasp the core concepts, master the process derivation, implement the code.