somosnlp/GemmaColRAC-AeroExpert

TEXT GENERATIONConcurrency Cost:1Model Size:2.6BQuant:BF16Ctx Length:8kPublished:Apr 9, 2024License:apache-2.0Architecture:Transformer0.0K Open Weights Cold

GemmaColRAC-AeroExpert is a 2.6 billion parameter specialized language model developed by Edison Bejarano, Nicolai Potes, and Santiago Pineda. Fine-tuned from a Gemma 2B base model, it is designed for processing and understanding Colombian Aeronautical Regulations (RAC) in Spanish. This model offers improved accuracy and resource efficiency compared to previous iterations, making it suitable for professionals and students in the aviation industry seeking enhanced access to regulatory information.

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Model Overview

GemmaColRAC-AeroExpert is the fifth iteration of a specialized language model developed by Edison Bejarano, Nicolai Potes, and Santiago Pineda, with funding from Fundación Universitaria Los Libertadores, SomosNLP, and HuggingFace. This 2.6 billion parameter model is fine-tuned from a Gemma 2B base, focusing specifically on Colombian Aeronautical Regulations (RAC) in Spanish. It represents a significant improvement over prior versions, offering enhanced accuracy and more efficient GPU resource utilization.

Key Capabilities

  • Specialized Knowledge: Provides advanced language processing for Colombian Aeronautical Regulations.
  • Improved Performance: Exhibits better accuracy and efficiency compared to its predecessors.
  • Sustainability Focus: Developed with an emphasis on minimizing environmental impact during training, including optimized energy consumption.
  • Expert Evaluation: Performance in simplifying RAC content was evaluated by aeronautical experts, showing strong results in most areas.

Good For

  • Aviation Professionals: Assisting with understanding and navigating complex Colombian aviation regulations.
  • Students: Providing enhanced access to regulatory information for academic purposes.
  • Regulatory Compliance: Supporting efforts to ensure adherence to Colombian aeronautical standards.

Limitations

The model may inherit biases from its training data, which consists primarily of legal texts. It is not intended for making legally binding decisions without human oversight, and users should exercise caution for critical decision-making.