Tsaro Gemma 4 E2B
Fine-tuned Gemma 4 E2B threat extraction model for Tsaro, a shared safety
system for Northern Nigeria.
What this model does
Given an unstructured report in Hausa, Pidgin, or English, this model returns
a structured threat signal — threat type, location, perpetrator and vehicle
counts, direction of movement, time references, and a confidence score — and
judges whether the message is a genuine security report at all.
Model details
- Base model:
google/gemma-4-e2b-it
- Fine-tuning: LoRA adapter trained on Tsaro threat-report data, then merged
into the base weights
- Role in Tsaro: the E2B variant is the smaller of two on-device extraction
models, used as the fallback for older or low-RAM Android devices
Derived models
Janeodum/tsaro-e2b-gguf — GGUF quantization for
on-device inference via llama.cpp / llama.rn
Training data
Fine-tuned on threat-report examples spanning Hausa, Pidgin, and English,
including examples derived from the ACLED Nigeria conflict archive with
Hausa and Pidgin translations.
Intended use and limitations
Built for community safety reporting in a specific regional context. Not a
general-purpose model. Outputs are extraction assistance, not verified
intelligence.
