CISO Assistant is a one-stop-shop GRC platform for Risk Management, AppSec, Compliance & Audit, TPRM, Privacy, and Reporting. It supports 100+ global frameworks with automatic control mapping, including ISO 27001, NIST CSF, SOC 2, CIS, PCI DSS, NIS2, DORA, GDPR, HIPAA, CMMC, and more.

Automate the measurement of organizational behavior

Run a cloud exit assessment on your infrastructure to gain insights into the challenges and constraints of a potential cloud exit.

Apache DevLake is an open-source dev data platform to ingest, analyze, and visualize the fragmented data from DevOps tools, extracting insights for engineering excellence, developer experience, and community growth.

EDoRA: Efficient Weight-Decomposed Low-Rank Adaptation via Singular Value Decomposition

Implementation of various Autoregressive models, Research papers and techniques. Main aim is to write clean, modular, wrapper-free implementations.

Analyze Git repositories for developer and project production insights

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Transform AI-generated text into natural, human-like writing using DoRA fine-tuned Qwen models. Features interactive training modes, Google Colab support, and comprehensive data processing pipeline for 32k context humanization.

PEFT for Code QA using DoRA

Skin cancer ranks among the most prevalent cancers globally, with early identification crucial for improving patient outcomes. This study presents a strategy for skin cancer detection using a fine-tuned BLIP-2 (Bootstrapped Language-Image Pre- training) model, optimized via Weight-Decomposed Low-Rank Adaptation (DoRA).

Distributed DoRA Training through periodic weight averaging for finetuning/continual pretraining

An interactive web app that helps you track project progress and forecast completion dates using charts and metrics.

Implementation and documentation of PEFT methods (LoRA & DoRA) in OpenVINO™ Training Extensions, including experimental results.

DORA Metrics Calculator

This repository contains a PyTorch implementation of a Convolutional Neural Network (CNN) for classifying the MNIST dataset. The project explores different fine-tuning techniques, including LoRA (Low-Rank Adaptation), DoRA (Dynamic Low-Rank Adaptation), and QLoRA (Quantized Low-Rank Adaptation), to improve model performance and efficiency.

Privacy-preserving AI personalization through encrypted DoRA/LoRA adapters. Cryptographic right-to-be-forgotten, ephemeral TEE inference, and consent-gated access. Your AI. Your Data. Your Control.

A short script to keep Devlake data up-to-date.