XEverentX

XEverentX's Stars

• FloridSleeves/LLMDebugger

  LDB: A Large Language Model Debugger via Verifying Runtime Execution Step by Step

  Language:Python43641

• OpenBMB/XAgent

  An Autonomous LLM Agent for Complex Task Solving

  Language:Python8.1k840

• openai/grade-school-math

  Language:Python1.1k149

• hendrycks/math

  The MATH Dataset (NeurIPS 2021)

  Language:Python89086

• Lightning-AI/lit-llama

  Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.

  Language:Python6k520

• togethercomputer/RedPajama-Data

  The RedPajama-Data repository contains code for preparing large datasets for training large language models.

  Language:Python4.6k350

• MadryLab/implementation-matters

  Language:Python11812

• ai-forever/ru-gpts

  Russian GPT3 models.

  Language:Python2.1k441

• microsoft/LoRA

  Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"

  Language:Python10.7k685

• ruohoruotsi/Butterworth-Filter-Design

  C++ classes for designing high-order Butterworth IIR & equalization filters

  Language:C++17344

• rlabbe/Python-for-Signal-Processing

  Notebooks for "Python for Signal Processing" book

  Language:Python9754

• rlabbe/Kalman-and-Bayesian-Filters-in-Python

  Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

  Language:Jupyter Notebook16.7k4.2k

• google-research/morph-net

  Fast & Simple Resource-Constrained Learning of Deep Network Structure

  Language:Python1k149

• eml-eda/q-ppg

  Language:Python192

• neuropsychology/NeuroKit

  NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing

  Language:Python1.6k419

• AlessandraGalli/PPG

  Code to estimate HR from PPG signals using Subspace Decomposition and Kalman filter for the dataset of 22 PPG recordings provided for the 2015 IEEE Signal Processing Cup (SP Cup) competition. The traces are stored in folder 'DATABASE'. Please cite this publication when referencing this material: "Measuring Heart Rate During Physical Exercise by Subspace Decomposition and Kalman Smoothing", IEEE Transaction on Instrumentation & Measurement.

  Language:Matlab7128

• andtem2000/PPG

  Language:Matlab2716

• ValveSoftware/Proton

  Compatibility tool for Steam Play based on Wine and additional components

  Language:C++24.5k1.1k

• intel/qpl

  Intel® Query Processing Library (Intel® QPL)

  Language:C9819

• eguiraud/cppyy_cmake_bindings

  Testing automatic generation of python -> c++ bindings with cppyy

  Language:CMake5

• mf1024/ImageNet-Datasets-Downloader

  ImageNet dataset downloader. Creates a custom dataset by specifying the required number of classes and images in a class.

  Language:Python502132

• morenfang/Pytorch-ImageNet

  Pytorch-ImageNet baseline

  Language:Python31

• google/svcca

  Language:Jupyter Notebook625139

• openvinotoolkit/nncf

  Neural Network Compression Framework for enhanced OpenVINO™ inference

  Language:Python942234

• Yorko/mlcourse.ai

  Open Machine Learning Course

  Language:Python9.8k5.7k

• glorotxa/SME

  Language:Python251108

• Accenture/AmpliGraph

  Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org

  Language:Python2.2k250