---
license: mit
language:
- en
metrics:
- accuracy
- bleu
library_name: adapter-transformers
tags:
- geospatial
- amd-accelerated
- natural-language-processing
- command-generation
- text-generation-inference
- transformers
---

# 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](https://arxiv.org/abs/2023.12345) (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:

```python
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)