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yashWeli/energy-suggestions-gptneo-Finetune

sourceHugging Faceupdated 1y agoView on Hugging Face
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Model Details

Model Description

This model is a Generative AI-powered energy optimization system designed to provide personalized recommendations for household electricity usage. It integrates IoT-based real-time monitoring, machine learning forecasting, and generative AI (LLMs) to deliver actionable suggestions through a web application.

  • —Developed by: Welikalage R.Y.W., Sri Lanka Institute of Information Technology
  • —Funded by [optional]: Self-funded (with hardware + AWS cloud expenses)
  • —Shared by [optional]: Fine-tuned Transformer-based Large Language Model (LLM)
  • —Model type: Fine-tuned Transformer-based Large Language Model (LLM)
  • —Language(s) (NLP): English
  • —License: [More Information Needed]
  • —Finetuned from model [optional]: Pre-trained Hugging Face Transformer LLM (domain-specific fine-tuning)

Model Sources [optional]

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  • —Repository: [More Information Needed]
  • —Paper [optional]: An Intelligent Electricity Management Unit: AI-Driven Power Forecasting and Personalized Consumption Insights with Application Integration (Project Proposal, 2025)
  • —Demo [optional]: [More Information Needed]

Uses

Personalized energy-saving suggestions for household appliances

IoT-based real-time electricity consumption tracking

Forecasting energy usage with time-series ML models

Gamified dashboards to visualize energy savings

Direct Use

Integration with smart home systems and utility providers

Extension into sustainability-focused apps

Used in research for energy efficiency and behavioral insights

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters
  • —Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • —Hardware Type: [More Information Needed]
  • —Hours used: [More Information Needed]
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  • —Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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APA:

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Glossary [optional]

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