llSourcell/Doctor-Dignity
Doctor Dignity
Doctor-Dignity is a privacy-preserving medical AI framework designed to execute large language models and diagnostic reasoning tasks locally on edge hardware. It provides a local inference engine and retrieval augmented generation implementation that ensures sensitive health data remains offline by removing dependencies on external cloud servers and internet connectivity.
The project includes a medical fine-tuning framework for adapting base language models to specialized clinical domains using parameter-efficient methods. To enable execution on resource-constrained and mobile devices, it provides tools for quantized model deployment and weight compression.
The system covers clinical knowledge retrieval via local vector databases and private health data processing. It also includes capabilities for model serialization and the use of supervised fine-tuning and reinforcement learning to improve diagnostic accuracy.
Features
- On-Device Inference Engines - Implements an optimized runtime for executing large language models locally on edge hardware to ensure data privacy.
- Knowledge Retrieval Systems - Implements a knowledge retrieval system using local vector indices to access verified medical information offline.
- Local Inference Engines - Ships a local execution environment for running medical language models and diagnostic tasks without cloud dependencies.
- Local RAG Implementations - Provides a local implementation of retrieval augmented generation using private medical datasets and offline LLMs.
- Medical Diagnostic Reasoning - Executes diagnostic reasoning tasks on private hardware to keep sensitive health data offline.
- Model Quantization Frameworks - Provides a framework to reduce model size by converting weights into lower-precision formats for resource-constrained devices.
- Local LLM Execution - Enables the execution of large language models on local hardware to ensure data privacy and offline availability.
- Parameter Efficient Fine-Tuning - Utilizes parameter-efficient fine-tuning to adapt large language models to specialized clinical domains with minimal weight updates.
- Offline-First Persistence - Implements an offline-first architecture for processing clinical datasets without requiring external internet connectivity.
- On-Device Vector Databases - Ships an on-device vector database to perform private similarity searches for medical knowledge retrieval.
- Privacy-Preserving Clinical Data AI - Implements a framework for secure, privacy-preserving AI workflows that keep sensitive clinical data entirely offline.
- Private Data Processing Suites - Ensures patient confidentiality by performing all AI workflows and data processing within isolated local environments.
- Fine-Tuning Toolkits - Provides a toolkit of scripts and methods for adapting base language models to specialized medical domains.
- Quantized Model Deployments - Deploys models using low-precision weight formats to minimize memory requirements on edge GPUs and mobile devices.
- Quantized Medical Models - Compresses large neural networks to enable efficient execution of medical AI on mobile and edge hardware.
- Clinical Domain Adaptation - Supports training language models using supervised fine-tuning and reinforcement learning on medical datasets.
- Mobile Model Serialization - Provides binary serialization formats to export neural network models for efficient execution on mobile and edge hardware.
