Popular GitHub Repositories for Grain Size

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.

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.

???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~

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

⚡ boost inference speed of T5 models by 5x & reduce the model size by 3x.

A deep learning-powered visual navigation engine to enables autonomous navigation of pocket-size quadrotor - running on PULP

Dockershrink is an AI Assistant that reduces the size of your application's Docker Image

Compute CNN receptive field size in pytorch in one line

Optimized (for size and speed) Caffe lib for iOS and Android with out-of-the-box demo APP.

This project showcases an LLMOps pipeline that fine-tunes a small-size LLM model to prepare for the outage of the service LLM.

:microscope: Nano size Theano LSTM module

A set of simple tools for splitting, merging, OP deletion, size compression, rewriting attributes and constants, OP generation, change opset, change to the specified input order, addition of OP, RGB to BGR conversion, change batch size, batch rename of OP, and JSON convertion for ONNX models.

Sharp Image Deblurring

The official repo for [ECCV'22] "VSA: Learning Varied-Size Window Attention in Vision Transformers"

An expanded version of the previously released Kazakh text-to-speech (KazakhTTS) synthesis corpus. In KazakhTTS2, the overall size has increased from 93 hours to 271 hours, the number of speakers has risen from two to five (three females and two males), and the topic coverage has been diversified.

Algorithm for prioritized multi-agent path finding (MAPF) in grid-worlds. Moves into arbitrary directions are allowed (each agent is allowed to follow any-angle path on the grid). Timeline is continuous, i.e. action durations are not explicitly discretized into timesteps. Different agents' size and moving speed are supported. Planning is carried out in (x, y, \theta) configuration space, i.e. agents' orientation are taken into account.

Reducing the size of convolutional neural networks

This project is an implementation of the Generative Adversarial Network proposed in our CVPR 2017 paper - DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data. DeLiGAN is a simple but effective modification of the GAN framework and aims to improve performance on datasets which are diverse yet small in size.