How to use from
llama.cpp
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Janeodum/tsaro-e4b-gguf:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf Janeodum/tsaro-e4b-gguf:Q3_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Janeodum/tsaro-e4b-gguf:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf Janeodum/tsaro-e4b-gguf:Q3_K_M
Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Janeodum/tsaro-e4b-gguf:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf Janeodum/tsaro-e4b-gguf:Q3_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Janeodum/tsaro-e4b-gguf:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Janeodum/tsaro-e4b-gguf:Q3_K_M
Use Docker
docker model run hf.co/Janeodum/tsaro-e4b-gguf:Q3_K_M
Quick Links

Tsaro E4B โ€” Multilingual Threat Extraction

Fine-tuned Gemma 4 E4B for structured threat extraction from community security reports in Hausa, Nigerian Pidgin, and English.

Part of Tsaro, a community early-warning system for northern Nigeria.

Available quantizations

File Size Recommended for
tsaro-e4b-q4_k_m.gguf ~5.0 GB Primary โ€” best quality
tsaro-e4b-q3_k_m.gguf ~4.85 GB Minor size savings

Both require ~6-7 GB phone RAM to run.

Use with Cactus (React Native)

import { CactusLM } from 'cactus-react-native';
const lm = await CactusLM.init({
  modelUrl: 'https://huggingface.co/Janeodum/tsaro-e4b-gguf/resolve/main/tsaro-e4b-q4_k_m.gguf',
  contextSize: 2048,
});

Use with llama.cpp

./llama-cli -m tsaro-e4b-q4_k_m.gguf -p "your report here"

System prompt

You are Tsaro, a community security report analyzer for northern Nigeria.
Extract threat entities from reports in Hausa, Nigerian Pidgin, or English.
Output ONLY valid JSON with relevant fields: threat_type, vehicle_type,
vehicle_count, person_count, cattle_count, direction, location,
forest_reference, time_reference. Omit fields that are not mentioned.

Training

  • Base: google/gemma-4-E4B-it
  • Framework: Unsloth
  • Data: 35,512 multilingual examples (Hausa, Pidgin, English)
  • LoRA r=16 alpha=16, 2 epochs, lr=2e-4 cosine

License

Inherits Gemma Terms of Use: https://ai.google.dev/gemma/terms

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