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Model Details
Model Provider
nesoai
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
To achieve high-fidelity reasoning in a compact 7B parameter footprint, Energy-Intelligence was developed through a Distillation & RLHF Architecture:
RLHF (Reinforcement Learning from Human Feedback):
Human evaluators review multiple responses generated by the model and select the better one. The model improves based on these preferences, making it more accurate, helpful, and aligned with real-world expectations.
Synthetic Data Generation:
We utilized synthetic data generated by the Teacher model to capture domain knowledge and real-world scenarios, enabling scalable training with improved accuracy and coverage of complex use cases.
Distillation:
The Result:
A model that possesses the intelligence of a much larger AI system while operating with the speed and cost-efficiency required for real-time industrial monitoring and analytics.
Our methodology focuses on embedding the intelligence of large-scale systems into a compact and efficient architecture:
The weights for the Energy-Intelligence engine are available in the Files & versions tab. This model is ready for deployment in RAG pipelines, automated energy reporting dashboards, and real-time anomaly detection systems.
The complete training pipeline, including data preparation, fine-tuning, and optimization workflows, is available in the following GitHub repository:
🔗 https://github.com/Savaliya03/Architecting-an-Energy-Intelligence-LLM-via-PEFT-Optimization
This repository provides implementation details of the model architecture, showcasing how Parameter Efficient Fine-Tuning (PEFT) techniques are used to reduce computational cost while maintaining high performance.
