Model Card for AMD-Accelerated Geospatial Command Generation Model

This model is designed to generate geospatial commands from natural language input, leveraging AMD accelerator cloud compute for enhanced performance.

Model Details

Model Description

This model is optimized for geospatial command generation tasks using AMD's advanced hardware acceleration. It translates natural language queries into executable geospatial commands for various GIS platforms.

Developed by: Anurag Kumar Singh, Neeraj Krishna
Funded by: Internal research funding
Shared by: DevelopersSky Research Team
Model type: Geospatial language model for command generation
Language(s) (NLP): English
License: MIT
Finetuned from model: Base transformer model (3 billion parameters)

Model Sources

Repository: https://github.com/developers-sky
Paper: Geospatial Command Generation Using Large Language Models on AMD Hardware (preprint)
Demo: https://huggingface.co/spaces/DevelopersSky/geospatial-commands-demo

Uses

Direct Use

This model can be directly used to translate natural language queries into geospatial commands for various GIS platforms. It's particularly useful for:

Generating complex geospatial queries from simple descriptions
Assisting GIS analysts in command formulation
Automating geospatial workflows

Downstream Use

The model can be fine-tuned for specific GIS platforms or integrated into larger geospatial analysis systems. Potential applications include:

Custom GIS interfaces with natural language input
Automated geospatial data processing pipelines
Intelligent geospatial assistants for urban planning or environmental monitoring

Out-of-Scope Use

This model should not be used for:

Non-geospatial natural language processing tasks
Real-time processing without proper optimization
Generating commands for unsupported GIS platforms
Making critical decisions without human oversight

Bias, Risks, and Limitations

The model may show bias towards more commonly used GIS commands and operations
Performance may vary for specialized or uncommon geospatial tasks
The model's knowledge is limited to its training data cutoff and may not reflect the latest GIS platform updates
There's a risk of generating syntactically correct but semantically inappropriate commands for complex queries

Recommendations

Users should verify generated commands before execution, especially for critical or large-scale operations
Regular updates and fine-tuning are recommended to maintain accuracy with evolving GIS platforms
Implement safeguards to prevent execution of potentially harmful commands
Use in conjunction with human expertise for complex geospatial analyses

How to Get Started with the Model

To use the model, you can start with the following code:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "DevelopersSky/geospatial-command-generator"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

query = "Show me all the forests within 10 km of downtown Seattle"
inputs = tokenizer(query, return_tensors="pt")
outputs = model.generate(**inputs)
command = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(command)