- Berlin, Germany
Lists (7)
Sort Name ascending (A-Z)
Stars
Official inference repo for FLUX.1 models
Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
Programmable Engine for Drone Reinforcement Learning Applications
The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use th…
ImageBind One Embedding Space to Bind Them All
From the Transistor to the Web Browser, a rough outline for a 12 week course
You like pytorch? You like micrograd? You love tinygrad! ❤️
Recipes for shrinking, optimizing, customizing cutting edge vision models. 💜
Toolkit for creating, sharing and using natural language prompts.
Implementation of Alphafold 3 in Pytorch
Pytorch implementation of the xLSTM model by Beck et al. (2024)
A recurrent (LSTM) neural network in C
An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).
Official implementation of Transformer Neural Processes
Implementation of the Convolutional Conditional Neural Process
[GPT beats diffusion🔥] [scaling laws in visual generation📈] Official impl. of "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction". An *ultra-simple, user-friendly …
A multi-asset matching engine for market places of all sizes
Finetune Llama 3.1, Mistral, Phi & Gemma LLMs 2-5x faster with 80% less memory
open-source library for quantum algorithms (simulation, optimization, machine learning)
A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum compu…
Ship detection from satellite imagery using Deep Learning model (YOLOv5). Model trained with custom Sentinel-1 dataset provided by Earth Engine API.
Code repository for the paper - "Matryoshka Representation Learning"