DigUp for Students

Download DigUp for Mac EmbeddingGemma 2 All roles

On-device flow Schematic: DigUp uses EmbeddingGemma 2 for Students local search

Original schematic — DigUp indexes local files with EmbeddingGemma 2 (llama.cpp).

Sourced snapshot (2026-10-15)

Fact	Value	Source
Model	EmbeddingGemma 2 · ~740M params · ~865 MB Q8 via llama.cpp	Google AI · DigUp README
Vector space	768-d shared multimodal space	Google AI · DigUp README
Mac / Win·Linux	0.6.1 · 0.7.0-beta.1	GitHub Releases
Stars / forks	~3,768 / ~254	GitHub API

Pain → DigUp workflow → tip

Pain	DigUp + EmbeddingGemma 2 workflow	Tip
Which PDF defined “entropy”?	Index course PDFs; query the concept	Opens the page — not just filename
Photo of whiteboard after class	Index camera folder; visual search	Find by what’s written/drawn
Where in the lecture they explained X?	Audio/video ~30s windows	Jump near the moment
Code homework snippet	Opt-in code: search	Open at the line in your editor

Numbered checklist

Install DigUp on Apple Silicon Mac or Win/Linux beta PC.
Add Courses / Notes folders; download iCloud class files locally first.
Let EmbeddingGemma 2 finish (~865 MB) before heavy indexing overnight.
Search concepts in your own words; add course codes as keywords.
Pause indexing on battery during exams week.

Why EmbeddingGemma 2 matters here

Google’s EmbeddingGemma 2 (~740M, unified 768-d multimodal space) is what DigUp runs via llama.cpp (~865 MB Q8 package). One query can rank images, PDF pages, audio windows, video frames, and optional code — see the local multimodal guide and model page. digup.run is not Google-official.