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alpaca-lora

Instruct-tune LLaMA on consumer hardware

ar2l_bpp

The repository is created for AR2L algorithm used for solving online 3D-BPP.

dense-deep-reinforcement-learning

This repo contains the code for paper "Dense reinforcement learning for safety validation of autonomous vehicles"

distribution_grid_analysis

dolly

dtqn

Deep Transformer Q-Networks for Partially Observable Reinforcement Learning

gym-acnportal

OpenAI Gym environments and utilities for reinforcement learning with ACN-Portal

h-transformer

mapdn

This repository is for an open-source environment for multi-agent active voltage control on power distribution networks (MAPDN).

patchtst-1

An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730

phire

probabilistic-energy-forecasting

Code for my master thesis on hierarchical probabilistic forecasting of smart meter time series using weather input.

prof

pytorch-forecasting

Time series forecasting with PyTorch

pytorch-wgan-gp

Improved Training of Wasserstein GANs

qlora

QLoRA: Efficient Finetuning of Quantized LLMs

relu_ann_milp

With this package, you can generate mixed-integer linear programming (MIP) models of trained artificial neural networks (ANNs) using the rectified linear unit (ReLU) activation function. At the moment, only TensorFlow sequential models are supported. Interfaces to either the Pyomo or Gurobi modeling environments are offered.

renewables_scenario_gen_gan

The implementation of scenario generation for renewables production process

stable-diffusion

A latent text-to-image diffusion model

styleswin

[CVPR 2022] StyleSwin: Transformer-based GAN for High-resolution Image Generation

trademaster

TradeMaster is an open-source platform for quantitative trading empowered by reinforcement learning :fire: :zap: :rainbow:

wassersteingan_gp-pytorch

Improved training of Wasserstein GANs

wgan-gp

A pytorch implementation of Paper "Improved Training of Wasserstein GANs"