test-gemma-isro

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)