Repository History
6 repositories tagged with PyTorch
Ludwig: Low-Code Declarative Deep Learning for LLMs and AI Models
Ludwig is a powerful, low-code declarative deep learning framework designed for building custom LLMs, neural networks, and other AI models. It simplifies the process of training, fine-tuning, and deploying models, from LLM fine-tuning to tabular classification, using a simple YAML configuration without boilerplate Python code. This makes advanced AI development accessible and efficient for a wide range of applications.
torchtune: PyTorch Native Library for LLM Post-Training and Experimentation
torchtune is a PyTorch native library designed for authoring, post-training, and experimenting with Large Language Models (LLMs). It offers hackable training recipes, simple PyTorch implementations of popular LLMs, and best-in-class memory efficiency. Please note: torchtune is no longer actively maintained as of 2025.
TensorRT-LLM: Optimizing Large Language Model Inference on NVIDIA GPUs
TensorRT-LLM is an open-source library by NVIDIA designed to optimize inference for Large Language Models (LLMs) and Visual Generation models. It offers a user-friendly Python API, state-of-the-art optimizations, and specialized kernels to ensure efficient performance on NVIDIA GPUs. This powerful tool enables developers to deploy LLMs with high throughput and low latency, from single-GPU setups to multi-node deployments.
PyTorch Image Models (timm): The Ultimate Collection of Image Encoders
PyTorch Image Models (timm) is an extensive library offering the largest collection of PyTorch image encoders and backbones. It provides a wide array of state-of-the-art models, complete with pretrained weights, training, evaluation, and inference scripts. This makes it an invaluable resource for researchers and developers working with computer vision tasks in PyTorch.
FlashAttention: Fast and Memory-Efficient Exact Attention
FlashAttention is a cutting-edge library from Dao-AILab, designed to provide fast and memory-efficient exact attention for deep learning models. It significantly accelerates transformer training and inference by optimizing memory usage and computational speed. This makes it an essential tool for researchers and developers working with large-scale AI models.
multiresolution-time-series-transformer: Long-term Forecasting with MTST
This repository provides a PyTorch implementation of the Multi-Resolution Time-Series Transformer (MTST) for long-term forecasting. Based on the Zhang et al. (2024) paper, MTST processes temporal data at different resolutions to effectively capture both short-term and long-term patterns. It offers a flexible and robust solution for advanced time series prediction tasks.