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About

ModelSync is a distributed version control tool inspired by Git, but entirely focused on the Artificial Intelligence ecosystem.

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🧠 ModelSync

A modern versioning system for Artificial Intelligence projects.


🚀 Overview

ModelSync is a distributed version control tool inspired by Git, but entirely focused on the Artificial Intelligence ecosystem.

While Git is great for versioning code, it fails when dealing with:

  • 🗂️ Large datasets
  • 🧠 Trained models (binaries)
  • 🧪 Experiments with multiple parameters
  • 📈 Performance metrics and comparisons

The goal of ModelSync is to fill this gap, offering a data and experiment-oriented versioning environment.


🧰 Technologies Used

Component Technology
CLI Typer
API FastAPI
Server Uvicorn
Git GitPython
Data Pandas
Container Docker

⚙️ MVP Features

  • modelsync init → Initializes a .modelsync/ repository
  • modelsync commit -m "message" → Saves a snapshot with metadata
  • Simple versioning with file hashing
  • Basic API for queries and external interactions

📦 How to Use

✅ Prerequisites

  • Python 3.10+
  • Linux, macOS or Windows (WSL recommended)

🔧 Installation

git clone https://github.com/your-username/modelsync.git
cd modelsync
pip install -r requirements.txt

🧪 CLI Usage

# Initialize ModelSync repository
python modelsync/cli/main.py init

# Create a commit with message
python modelsync/cli/main.py commit -m "Model with 92% accuracy"

📋 Basic Commands Reference

Core Commands

Command Description
modelsync init Creates a new ModelSync repository
modelsync status Shows the status of tracked files and changes
modelsync add [file] Adds a file to the staging area
modelsync add . Adds all files to the staging area
modelsync commit -m "[message]" Saves changes with a descriptive message
modelsync log Shows commit history
modelsync log --oneline Shows commit history in one line per commit
modelsync diff Shows differences between working directory and staging area

AI-Specific Commands

Command Description
modelsync dataset add ./data.csv --name "Training Data" Add dataset to version control
modelsync model add model.pkl --framework sklearn --metrics '{"accuracy": 0.95}' Add model to version control
modelsync experiment create feature_engineering Create experiment branch
modelsync llm start Start vLLM API server
modelsync llm generate --prompt "Hello world" Generate text with vLLM
modelsync llm_status Check vLLM service status
modelsync web Start web interface

Advanced Commands

Command Description
modelsync restore [file] Discards changes in a file
modelsync reset [file] Unstages a file from the staging area
modelsync branch [name] Creates a new branch for experiments
modelsync checkout [branch] Switches to another branch
modelsync merge [branch] Merges changes from a branch
modelsync remote add [name] [url] Adds a remote repository
modelsync push Uploads changes to remote repository
modelsync pull Downloads updates from remote repository
modelsync clone [url] Clones a remote ModelSync repository

About

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