Molly OS — start here

On-device-first inference router: one 8B base + fifteen LoRA specialists, routed across local / LAN / cloud per request. Paper, adapters, and a live demo.

Author: Daniele Trovato · Core Labs R&D


What's here

Link
📄 Whitepaper (CC-BY-4.0, EN/IT/ES, figures included) molly-os-whitepaper
🎮 Live demo (chat · image gen · live-data docs) molly-os-demo
🧩 Fifteen specialist LoRA adapters see table below

Suggested path: read the whitepaper → try the demo → load any adapter locally → reach out for hosted orchestration.


The fifteen specialists

PEFT LoRA adapters, rank 32, trained on a popular open 8B instruction base. Released CC-BY-NC-4.0.

Computer Science

Engineering & Security

Finance & Business

Science & Medicine


How it works

Molly OS is a model-agnostic inference orchestration layer. Each request is routed across on-device / LAN / cloud / external targets based on task profile and locality. A shared quantized 8B base hosts many domain LoRA adapters concurrently, so specialization costs little memory. Data stays on-device by default; cloud is opt-in per request. Specialists are kept current via continuous distillation.

The open artifacts here (paper, adapters, demo) document the approach. The hosted orchestration service is the commercial offering.


Quickstart

Each adapter card includes a copy-paste PEFT + gated-base auth snippet — load the 8B base, attach the LoRA, generate. Start with any specialist repo above (e.g. molly-cs-nlp) and follow its Usage section.

For multi-adapter routing in production, use the hosted service (below) rather than rolling your own — the local snippet is single-adapter only.


License

  • Whitepaper: CC-BY-4.0
  • Adapters (×15): CC-BY-NC-4.0 (non-commercial; contact us for commercial terms)
  • Base model: governed by its own upstream license — review before redistribution

Product & contact

For commercial licensing, evaluation access, or research collaboration, get in touch.

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