---
language:
- en
- bn
- es
- id
license: apache-2.0
base_model: google/gemma-4-e2b-it
tags:
- unsloth
- gemma4
- trl
- spatial
- humanitarian
- tool-calling
- lora
- offline
- edge-ai
- raspberry-pi
pipeline_tag: text-generation
---

# gemma-4-e2b-spatial-lora

> **Gemma 4 E2B fine-tuned for humanitarian spatial tool calling**  
> Part of [GemmaTerrain](https://huggingface.co/tasfuuu19) — Multimodal GeoAI for the Unconnected

[![Base Model](https://img.shields.io/badge/Base-Gemma%204%20E2B-blue)](https://huggingface.co/google/gemma-4-e2b-it)
[![License](https://img.shields.io/badge/License-Apache%202.0-yellow)](https://opensource.org/licenses/Apache-2.0)
[![Trained with Unsloth](https://img.shields.io/badge/Trained%20with-Unsloth-purple)](https://github.com/unslothai/unsloth)

---

## 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:**

| Input | Output |
|---|---|
| `"Find the nearest hospital to Camp 6"` | `{"name": "find_nearest_poi_with_route", "arguments": {"poi_type": "hospital", "lat": 21.2, "lon": 92.16}}` |
| `"15 minute walking radius from Condado"` | `{"name": "generate_isochrone", "arguments": {"lat": 18.46, "lon": -66.07, "max_minutes": 15}}` |
| `"Camp 6 er kache hospital kothay?"` | `{"name": "find_nearest_poi_with_route", "arguments": {"poi_type": "hospital", "lat": 21.2, "lon": 92.16}}` |
| `"Farmacias dentro de 1km de Ocean Park"` | `{"name": "list_pois", "arguments": {"poi_type": "pharmacy", "lat": 18.46, "lon": -66.05, "radius_m": 1000}}` |

---

## Spatial Tools

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

| Tool | Use case |
|---|---|
| `find_nearest_poi_with_route` | "Nearest hospital to X" — returns closest POI + walking route |
| `list_pois` | "Clinics within 2km of X" — returns all POIs in radius |
| `calculate_route` | "Walk from A to B" — returns distance + time |
| `generate_isochrone` | "15 min walking area from X" — returns reachable boundary |
| `find_along_route` | "Pharmacies along the way from A to B" |
| `geocode_place` | "Where is X?" — resolves place name to coordinates |

**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`

---

## Training Details

| Parameter | Value |
|---|---|
| Base model | `google/gemma-4-e2b-it` |
| Method | QLoRA (4-bit quantization) |
| LoRA rank | 16 |
| LoRA alpha | 32 |
| Target modules | `q_proj`, `k_proj`, `v_proj`, `o_proj` |
| Max sequence length | 512 tokens |
| Training examples | 28 unique × augmented to 5,000 |
| Epochs | 3 |
| Batch size | 2 (effective: 8 with grad accumulation) |
| Learning rate | 2e-4 |
| Precision | fp16 |
| Framework | Unsloth + TRL SFTTrainer |
| Hardware | Kaggle T4 (15GB) |
| Training time | ~45 minutes |

---

## Dataset

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

| Location | Context |
|---|---|
| **Cox's Bazar, Bangladesh** | Rohingya refugee camps (Camp 3, 6, 8W, 9, 12, 15) |
| **San Juan, Puerto Rico** | Post-hurricane disaster response (Condado, Santurce, Miramar) |
| **Jakarta, Indonesia** | Urban humanitarian operations (Menteng, Gelora, Gambir, Kemang) |

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

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

---

## 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
```

---

## Usage

### With Unsloth (recommended)

```python
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

```python
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

```python
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}}
```

---

## 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.

---

## 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

---

## License

Apache 2.0 — same as the base Gemma 4 model.