Instructions to use Ispatialtechnosolutions/BraeinAi-Geospatial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ispatialtechnosolutions/BraeinAi-Geospatial with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Ispatialtechnosolutions/BraeinAi-Geospatial", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ispatialtechnosolutions/BraeinAi-Geospatial")
model = AutoModelForCausalLM.from_pretrained("Ispatialtechnosolutions/BraeinAi-Geospatial", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ispatialtechnosolutions/BraeinAi-Geospatial with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ispatialtechnosolutions/BraeinAi-Geospatial"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ispatialtechnosolutions/BraeinAi-Geospatial",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ispatialtechnosolutions/BraeinAi-Geospatial

SGLang

How to use Ispatialtechnosolutions/BraeinAi-Geospatial with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Ispatialtechnosolutions/BraeinAi-Geospatial" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ispatialtechnosolutions/BraeinAi-Geospatial",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Ispatialtechnosolutions/BraeinAi-Geospatial" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ispatialtechnosolutions/BraeinAi-Geospatial",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ispatialtechnosolutions/BraeinAi-Geospatial with Docker Model Runner:
```
docker model run hf.co/Ispatialtechnosolutions/BraeinAi-Geospatial
```

Model Card for Model ID

BraeinAi Geospatial is a fine-tuned version of Meta’s LLaMA-2 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

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

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

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

Downloads last month: -

Safetensors

Model size

7B params

Tensor type

F32

Model tree for Ispatialtechnosolutions/BraeinAi-Geospatial

Base model

meta-llama/Llama-2-7b-chat

Finetuned

(1)

this model