| --- |
| 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 |
