Archana-st25/Energy-Executive-Llama3

Energy Executive Llama 3.1 (Persona-Aware Agent)

This model is a specialized fine-tune of Llama 3.1 8B, designed for senior decision-makers in the energy sector. It translates complex regulatory language into actionable strategic alerts.

🎯 Project Purpose

In the energy market, a single regulation (like a CERC order) has opposite impacts on different stakeholders. This model uses Persona-Aware Instruction Tuning to provide divergent advice for:

• Power Generators (Sellers): Focus on revenue certainty, evacuation risks, and project bankability.
• Utilities/Discoms (Buyers): Focus on cost pass-through, grid reliability, and tariff impacts.

📚 Training Data & Sources

The model was fine-tuned on a high-density dataset consisting of:

1. CERC Orders (2024-25): Technical rulings on ISTS licenses, GNA, and grid code compliance (e.g., Petition No. 513/TL/2024).
2. IEA World Energy Outlook 2025: Strategic macro-trends including AI-driven load growth and global decarbonization pathways.

🛠 Technical Specifications

• Architecture: Llama-3.1 8B
• Fine-Tuning Method: QLoRA (4-bit quantization)
• Optimization: Unsloth (2x faster training)
• Quantization: GGUF (q4_k_m) for local deployment.

🚀 Example Prompt

Instruction: Analyze this clause as a Utility/Discom: "The transmission license is subject to annual fee payments." Response: * Strategic Impact: License fees are a recurring O&M expense.

• Financial Risk: Medium (Tariff pass-through dependency).
• Action Item: Ensure these fees are included in the upcoming ARR filing to the SERC.