---
library_name: transformers
tags:
- gis
- geospatial
- fine-tuned
- text-generation
license: apache-2.0
language:
- en
base_model:
- meta-llama/Llama-2-7b-chat
---

# Model Card for Model ID

**BraeinAi Geospatial** is a fine-tuned version of Meta’s [LLaMA-2](https://huggingface.co/meta-llama) model for **Geographic Information Systems (GIS)** tasks.  
It is designed to assist with spatial data concepts, GIS software usage, standards (OGC WMS/WFS/WCS), and related technical queries.  

- **Developed by:** Ispatialtechnosolutions  
- **Model type:** Causal Language Model  
- **Language(s):** English  
- **License:** Apache 2.0  
- **Base Model:** LLaMA-2 (Meta)
- **Fine-tune method:** LoRA → merged weights  
- **Intended use:** GIS assistant (ArcGIS/ArcPy/QGIS/WMS/WFS/Portal)

---
## Model Details

### Model Description

**BraeinAi Geospatial** is a fine-tuned version of Meta’s [LLaMA-2](https://huggingface.co/meta-llama) model for **Geographic Information Systems (GIS)** tasks.  
It is designed to assist with spatial data concepts, GIS software usage, standards (OGC WMS/WFS/WCS), and related technical queries.  


This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Ispatialtechnosolutions
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** Causal Language Model
- **Language(s) (NLP):** English
- **License:** Apache 2.0

### Model Sources [optional]

- **Repository:** [Ispatialtechnosolutions/BraeinAi-Geospatial](https://huggingface.co/Ispatialtechnosolutions/BraeinAi-Geospatial)  

## Uses

### Direct Use

- Answering GIS-related technical queries  
- Helping with geospatial software usage (e.g., QGIS, ArcGIS, GDAL)  
- Assisting with OGC standards (WMS, WFS, WMTS, etc.)  
- Supporting spatial data processing and remote sensing tasks 

### Downstream Use [optional]

- Integrating into GIS chatbots  
- Embedding in decision-support tools for spatial analysis  
- Educational use in GIS learning environments  

### Out-of-Scope Use

- Integrating into GIS chatbots  
- Embedding in decision-support tools for spatial analysis  
- Educational use in GIS learning environments  

## Bias, Risks, and Limitations

- May generate **hallucinated commands** for GIS software not grounded in documentation  
- Limited to **English-language queries**  
- Not a replacement for domain experts in mission-critical applications 

### Recommendations

- Validate outputs before production use  
- Use in supervised / decision-support settings, not as final authority  

---
## How to Get Started with the Model


```python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

repo_id = "Ispatialtechnosolutions/BraeinAi-Geospatial"

tok = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(repo_id)

pipe = pipeline("text-generation", model=model, tokenizer=tok)

print(pipe("How do I publish a WMS in GeoServer?", max_new_tokens=200)[0]["generated_text"])


## Training Details

### Training Data

This model was fine-tuned on a curated GIS domain dataset including:

Spatial standards (OGC WMS/WFS/WMTS/WCS docs)

GIS tutorials & training manuals

QGIS/ArcGIS user documentation

Geospatial developer blogs

### Training Procedure

Base Model: LLaMA-2

Method: LoRA fine-tuning

Precision: bf16 mixed precision

Batch size per device: 1–2

Gradient accumulation steps: 8–16

Effective batch size: 4

Epochs: 2
LR         = 2e-4



## Evaluation

Metrics

Perplexity: Lower than base LLaMA-2 on GIS test set

Qualitative evaluation: Produces domain-specific and contextually relevant answers

Example Query & Response

Input: "What is the difference between WMS and WFS?"
Output: "WMS (Web Map Service) delivers rendered images of maps, while WFS (Web Feature Service) delivers vector features in formats like GML/GeoJSON for analysis."

## Environmental Impact

Training hardware: A100 128GB RAM GPUs
24gb Graphic Card


## Technical Specifications [optional]

### Model Architecture and Objective

Architecture: LLaMA-2 (Causal Decoder-only Transformer)

Parameter count: Same as LLaMA-2 base used

Library:  Transformers

## Citation [optional]

@misc{llama2gis2024,
  title = {BraeinAi-Geospatial: A Domain-Specialized GIS Language Model},
  author = {Ispatialtechnosolutions},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Ispatialtechnosolutions/BraeinAi-Geospatial}},
}


## Model Card Contact
Maintainer: Ispatialtechnosolutions
Email: connectus@ispatialtec.com