DigUp for HR — Policies, Offer Letters & Training Media

Download DigUp for Mac EmbeddingGemma 2 All roles

On-device flow Schematic: DigUp uses EmbeddingGemma 2 for HR 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
Remote-work clause location	Policy PDF semantic search	Open at page
Onboarding video chapter	Video + audio embeddings	Jump near topic
Signed offer scan photo	Image + OCR (Mac)	Find by layout/text
Confidentiality	Local folders only	Exclude personal drives; read privacy FAQ

Numbered checklist

Coordinate with IT; DigUp indexes only selected folders after ~865 MB model install.
Keep PII folders tightly scoped.
Search policy language employees actually ask about.
Schedule indexing off peak.
Link employees to digup.run FAQ as community help — disclose not affiliated.

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.