gemma-4-e2b-spatial-lora

gemma-4-e2b-spatial-lora

Gemma 4 E2B fine-tuned for humanitarian spatial tool calling
Part of GemmaTerrain — Multimodal GeoAI for the Unconnected

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Overview

This is a LoRA adapter for google/gemma-4-e2b-it, fine-tuned to output structured JSON tool calls for humanitarian spatial queries. It runs offline on ARM edge hardware (Raspberry Pi 5, Steam Deck) as part of the GemmaTerrain system — no internet, no cloud, no GPU required.

Given a natural language query in English, Bangla, Spanish, or Indonesian, the model selects exactly one spatial tool and outputs its arguments as JSON.

Example:

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Spatial Tools

The model is trained to call exactly one of these 6 tools:

Supported POI types: hospital, clinic, doctors, pharmacy, police, fire_station, shelter, school, university, bank, atm, supermarket, marketplace, drinking_water, water_point, fuel, bus_station, place_of_worship

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Training Details

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Dataset

28 hand-crafted humanitarian spatial query → tool call pairs across 3 real-world locations and 4 languages:

Languages: English · Bangla (transliterated) · Spanish · Indonesian

Query types: nearest POI, radius search, route calculation, isochrone, along-route search

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System Prompt

You are GemmaTerrain, a humanitarian spatial assistant running offline on edge hardware.
Select exactly ONE tool. Output only the tool call JSON — no explanation.

Valid poi_type values: hospital, clinic, doctors, pharmacy, police, fire_station,
shelter, school, university, bank, atm, supermarket, marketplace, drinking_water,
water_point, fuel, bus_station, place_of_worship

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Usage

With Unsloth (recommended)

from unsloth import FastModel

model, tokenizer = FastModel.from_pretrained(
    model_name="tasfuuu19/gemma-4-e2b-spatial-lora",
    max_seq_length=512,
    load_in_4bit=True,
)

With Transformers

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("google/gemma-4-e2b-it", load_in_4bit=True)
model = PeftModel.from_pretrained(base, "tasfuuu19/gemma-4-e2b-spatial-lora")
tokenizer = AutoTokenizer.from_pretrained("tasfuuu19/gemma-4-e2b-spatial-lora")

Inference example

messages = [
    {"role": "system", "content": "You are Meridian, a humanitarian spatial assistant..."},
    {"role": "user", "content": "Find the nearest hospital to Camp 6"},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# {"name": "find_nearest_poi_with_route", "arguments": {"poi_type": "hospital", "lat": 21.2, "lon": 92.16}}

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GemmaTerrain System

This adapter is the E2B component of the GemmaTerrain dual-model routing system:

Query → Geocode Layer → Cactus Router → Gemma 4 E2B (this model) → Spatial Tool → Result
                                      ↘ Gemma 4 E4B (complex/multimodal queries)

The router sends queries to E2B for simple spatial lookups (nearest, list, route, isochrone) and to E4B for multimodal or complex multi-hop reasoning. Battery level is also a routing signal — below 20%, all queries go to E2B.

The spatial backend uses NetworKit (Dijkstra routing) + DuckDB (spatial queries) on OpenStreetMap data, running fully offline.

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Limitations

• Training set is small (28 unique examples) — generalization relies heavily on the base model
• Coordinates in training data are fixed to 3 locations; novel locations require geocoding pre-processing
• Walking speed hardcoded at 5 km/h (83.33 m/min)
• Not suitable for driving or cycling routing

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License

Apache 2.0 — same as the base Gemma 4 model.