---
license: apache-2.0
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: peft
tags:
- lora
- qlora
- unsloth
- transformers
- industrial-ai
- cortex
---

# Cortex Copilot LoRA Adapter

## Overview

This repository contains the LoRA adapter developed for the **Cortex Copilot Engineering Challenge**.

The adapter fine-tunes **Qwen2.5-3B-Instruct** using **QLoRA** to improve responses for industrial energy management, electrical engineering concepts, Indian electricity tariff logic, and Cortex-specific metrics.

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## Base Model

**Qwen/Qwen2.5-3B-Instruct**

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## Fine-Tuning Method

- Framework: Unsloth
- Method: QLoRA
- PEFT (Parameter-Efficient Fine-Tuning)

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## Dataset

The model was fine-tuned on **399 instruction-response pairs** covering:

- Electrical Engineering Concepts
- Indian Industrial Tariff Rules
- Cortex Metric Explanations
- Energy Optimization
- Refusal Behaviour
- Tenant Isolation
- Prompt Injection Resistance

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## Training Configuration

| Parameter | Value |
|-----------|-------|
| Epochs | 3 |
| Batch Size | 2 |
| Gradient Accumulation | 4 |
| Learning Rate | 2e-4 |
| Max Sequence Length | 2048 |
| GPU | Tesla T4 |

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## Files

- adapter_model.safetensors
- adapter_config.json
- tokenizer.json
- tokenizer_config.json
- chat_template.jinja

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## Intended Use

This adapter is intended for educational purposes as part of the Cortex Copilot Engineering Challenge.

It specializes the base model for industrial energy management while relying on external telemetry data for real-time information.

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## Author

**Khalid Ahmad Raza**

B.Tech Computer Engineering

National Institute of Technology Kurukshetra