Teach a small language model your voice from your notes, a WhatsApp export, an FAQ or answers you speak out loud. It scrubs secrets, fine-tunes a LoRA adapter on your own computer or phone, and compares it with the base model. Your data never leaves the device.

Markdown notes, WhatsApp exports, chat JSON, Q&A CSVs, or questions answered out loud and transcribed by Whisper on the device.
Emails, phone numbers, wallet addresses, private keys, tokens, IBANs, cards and seed phrases are replaced before anything is trained.
QVAC fine-tunes a LoRA adapter with a memory check first, a live loss curve, and pause and resume.
Publish the adapter (never the data) as a pear:// link with qvac-seed; a friend's Pocket Tune downloads it, checks its SHA-256 and compares it.



# the web UI (Node.js 22+) git clone https://github.com/lopeselio/qvac-pocket-tune cd qvac-pocket-tune && npm install && npm run build:ui npm start # opens http://127.0.0.1:4322 # or the CLI, from the same clone node bin/pocket-tune.js prep examples/pirate-voice/faq.csv --out runs/pirate node bin/pocket-tune.js train runs/pirate
The phone app is in mobile/ (Expo; build it with Xcode or Android Studio).