Offeline

Offeline is a privacy-first desktop & web app for running LLMs locally on your hardware using multiple backend options

Open source version

demo_vid.mp4

Production desktop demo

latest_offeline.mp4

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Table of Contents

• Features
• Supported Backends
• Installation
• Requirements
• Development
• Roadmap
• Credits

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Features

Core Capabilities

• Complete Privacy: All AI models run locally on your hardware. No data is sent to external servers. Process everything on your machine.
• Multi-Platform: Use via browser (web app) or native desktop application with Electron
• Offline-Capable: Download models once, use them offline indefinitely (WebGPU mode)
• Multiple AI Backends: Choose your preferred inference engine:
  • WebGPU - Run models directly in your browser using GPU acceleration
  • Ollama - Manage and run models with Ollama backend
  • llama.cpp - CPU/GPU optimized inference on desktop

Chat & Interaction

• Rich Chat Interface: Clean, intuitive conversation interface with real-time streaming responses
• File Embeddings: Load and ask questions about documents (PDF, MD, DOCX, TXT, CSV, RTF) - fully locally!
• Voice Support: Interact with the AI using voice messages
• Regenerate Responses: Quickly regenerate AI responses without retyping prompts
• Chat History: Persistent, organized conversation history across sessions
• Export Conversations: Save your chats as JSON or Markdown
• RAG capabilities: Use retrieval augmented generation to chat with your documents
• Knowledge base: A smarter way to manage your documents

AI Customization

• Custom Memory/Instructions: Add custom system prompts and memory to personalize AI behavior
• Web Search Integration: Optional real-time web search capabilities with Tavily or DuckDuckGo (when enabled)
• Light & Dark Mode: Toggle between themes for comfortable usage
• Markdown & Code Syntax Highlighting: Beautifully rendered markdown and syntax-highlighted code blocks
• Model Selection: Easily switch between different open-source models

Supported Models

• Llama 2 & 3 - Meta's popular language models
• Gemma - Google's efficient models
• Mistral - Mistral AI's powerful models
• And more - Support for any GGUF-compatible models

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Supported Backends

Backend	Platform	Type	Notes
WebGPU	Browser	GPU	Native browser acceleration, no installation needed
Ollama	Desktop (Windows/Mac/Linux)	CPU/GPU	Easy model management, requires Ollama installation
llama.cpp	Desktop (Windows/Mac/Linux)	CPU/GPU	Direct integration, optimized performance

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Installation

Web Application

Prerequisites

• Node.js 18+
• npm or pnpm package manager
• Modern browser with WebGPU support (Chrome/Edge/Firefox with flags)

Setup

1. Clone the repository:

   git clone https://github.com/iBz-04/offeline
   cd offeline

2. Install dependencies:

   npm install
   # or
   pnpm install

3. Run development server:

   npm run dev
   # or
   pnpm dev

4. Open in browser:

   Navigate to http://localhost:3000

Production Build

npm run build
npm run start

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Desktop Application

Prerequisites

• Node.js 18+
• pnpm (recommended)
• For Ollama backend: Ollama installed and running
• For llama.cpp backend: Models in GGUF format

Setup

1. Install desktop dependencies:

   cd desktop
   pnpm install

2. Development Mode:

   pnpm electron:dev

3. Production Build:

   pnpm electron:prod

Desktop Features

• Native application experience
• Seamless Ollama integration (auto-start/stop)
• Direct llama.cpp support
• System tray integration
• Model management UI

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Requirements

Browser (WebGPU)

• GPU: GPU with WebGPU support
  • 3B models: ~3GB VRAM
  • 7B models: ~6GB VRAM
  • Larger models: Proportionally more VRAM
• Browser: Chrome/Edge 113+, Firefox with WebGPU enabled

Desktop (Ollama/llama.cpp)

• RAM: 8GB+ recommended for 7B models
• CPU: Multi-core processor recommended
• GPU: Optional but recommended for faster inference
  • NVIDIA (CUDA support)
  • Apple Silicon (Metal support)
  • AMD (Vulkan support)

Tip: Smaller models (3B) are more efficient and suitable for file embeddings on resource-constrained systems.

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Development

Project Structure

offeline/
├── src/                    # Next.js frontend
│   ├── app/               # Next.js app directory
│   ├── components/        # React components
│   ├── hooks/             # Custom React hooks
│   ├── lib/               # Utilities (search, embedding, tools)
│   ├── providers/         # Context providers
│   └── types/             # TypeScript definitions
├── desktop/               # Electron desktop app
│   ├── main/              # Main process
│   └── preload/           # Preload scripts
└── package.json          # Root dependencies

Tech Stack

Frontend:

• Next.js 14 - React framework
• TypeScript - Type safety
• Tailwind CSS - Styling
• Radix UI - Component library
• Framer Motion - Animations

Backends:

• @mlc-ai/web-llm - WebGPU inference
• Ollama - Model server
• node-llama-cpp - Direct llama.cpp binding

Utilities:

• LangChain - AI/LLM utilities
• Transformers.js - ONNX model support
• react-markdown - Markdown rendering
• Zustand - State management

Building

# Web development
npm run dev

# Desktop development
cd desktop && pnpm electron:dev

# Full build
npm run build

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Roadmap

Completed

• Web application with WebGPU support
• Desktop application (Electron)
• Ollama integration
• llama.cpp integration
• File embeddings (PDF, DOCX, TXT, etc.)
• Web search integration (Tavily, DuckDuckGo)
• Chat history & export
• Custom memory/instructions
• Voice message support

In Progress

• Enhanced model management
• Performance optimizations
• Additional search backends

Future

• Advanced RAG (Retrieval-Augmented Generation)
• Plugin system
• Cloud sync (optional, privacy-preserving)
• API for third-party integrations

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Browser Support

Browser	WebGPU Support	Status
Chrome/Edge	113+	Full support
Firefox	120+	Full support (enable dom.webgpu.enabled)
Safari	17+ (macOS)	Full support

Check WebGPU browser compatibility for detailed information.

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Credits

Offeline is built with:

• HuggingFace - Model hub
• LangChain - LLM framework
• Next.js - React framework
• Electron - Desktop framework
• Ollama - Model server
• node-llama-cpp - llama.cpp Node.js binding
• Open-source LLM community

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Contributing

Contributions are welcome! Feel free to open issues and pull requests.

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Quick Start (Windows)

Below are common commands for Windows PowerShell; pnpm is recommended (pnpm-lock.yaml is included).

# 1) Clone and enter the project
git clone https://github.com/iBz-04/offeline; cd offeline

# 2) Install dependencies (root web app)
pnpm install

# 3) Start the web app (Next.js)
pnpm dev

# 4) IN ANOTHER terminal Start the second web instance
pnpm dev -- -p 3001

# 4) In another terminal: run the desktop app 
cd desktop; pnpm install; pnpm build; pnpm electron:dev

Notes:

• WebGPU works best on recent Chrome/Edge on Windows 10/11. If disabled, ensure your GPU drivers are up to date and that Chrome/Edge are current (113+).
• Ollama and llama.cpp backends are available in the desktop app. Install Ollama if you want to use Ollama models.

Configuration & Tips

• Web search backends: You can choose between Tavily (requires API key) and DuckDuckGo (no key). In the UI, open Search settings and paste your Tavily key. Alternatively, you can set the environment variable NEXT_PUBLIC_TAVILY_API_KEY before starting the web app.
• File embeddings: For best performance on low-spec machines, prefer smaller models (e.g., 3B) for embedding and chat.
• Desktop llama.cpp: The Electron app uses node-llama-cpp under the hood. Use GGUF models. GPU acceleration depends on your platform and build.

Troubleshooting

• WebGPU not available

  • Update Chrome/Edge to the latest version
  • Update graphics drivers
  • Check chrome://gpu to confirm WebGPU status

• Tavily key errors

  • Make sure you’ve saved the key in the Search settings UI
  • Or set NEXT_PUBLIC_TAVILY_API_KEY in your environment
  • The app will fall back to DuckDuckGo if Tavily isn’t configured

• Desktop app doesn’t start

  • Run from the desktop folder: pnpm install then pnpm electron:dev
  • Ensure Node.js 18+ is installed
  • On first run, electron-builder may install native deps (let it finish)