Lumina LLM | Offline & Online Custom Models
⚡ THE BEST AI EXPORT

Custom LLMs
Offline + Online Access

Unified intelligence: run powerful private models on-device or switch to cloud-scale reasoning. Beautiful fixed viewport — everything stays crisp as you scroll.
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Offline Mode

Fully local inference — zero internet, total privacy. Run fine-tuned LLMs directly on your device with sub-second latency. Perfect for secure deployments & air-gapped environments.

Local weights · Always available
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Online Access

Cloud-synced frontier models with live tool use, RAG, and massive context windows. Automatic fallback, intelligent routing — get the best of both worlds.

Global endpoints · Realtime knowledge
🧬 Bespoke LLM Ecosystem — Tailored for You

📦 Offline-First Engine

Quantized models, WebGPU acceleration, and on-device vector stores. Your data never leaves.

☁️ Hybrid Orchestration

Smart router: offline for simple tasks, online for deep reasoning + live web data.

🧠 Fine-tuning Hub

Bring custom datasets, train LoRA adapters, export for offline or deploy as cloud endpoint.

🔐 Privacy+ Mode

End-to-end encrypted sync when online — offline retains full sovereignty.

🤖 AI Studio · Custom Model Playground
Online mode (active)

Select your LLM and experience offline vs online behavior — responses adapt intelligently.

🌐 Online model — live knowledge & cloud scale
🤖 System
Welcome to the unified LLM interface. Switch between offline & online models. Try a prompt: “Explain offline vs online LLM tradeoffs” or “What’s the latest in AI?”
🧑‍💻 You
How do custom LLMs keep performance both offline & online?
✨ AI
Our platform uses adaptive quantization for offline models and on-demand cloud scaling for online. You get seamless context sharing, and intelligent fallback ensures zero disruption.
* Simulated AI responses reflect real-world offline/online capabilities. Offline = local privacy, online = enriched context.
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Offline Ready
GGUF, ONNX, local transformers
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Online Augmented
Live APIs, web search, RAG
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Full Export Control
Bring your own fine-tuned LLMs

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