| --- |
| 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. |
|
|
| --- |
|
|
| ## Base Model |
|
|
| **Qwen/Qwen2.5-3B-Instruct** |
|
|
| --- |
|
|
| ## Fine-Tuning Method |
|
|
| - Framework: Unsloth |
| - Method: QLoRA |
| - PEFT (Parameter-Efficient Fine-Tuning) |
|
|
| --- |
|
|
| ## 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 |
|
|
| --- |
|
|
| ## Training Configuration |
|
|
| | Parameter | Value | |
| |-----------|-------| |
| | Epochs | 3 | |
| | Batch Size | 2 | |
| | Gradient Accumulation | 4 | |
| | Learning Rate | 2e-4 | |
| | Max Sequence Length | 2048 | |
| | GPU | Tesla T4 | |
|
|
| --- |
|
|
| ## Files |
|
|
| - adapter_model.safetensors |
| - adapter_config.json |
| - tokenizer.json |
| - tokenizer_config.json |
| - chat_template.jinja |
|
|
| --- |
|
|
| ## 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. |
|
|
| --- |
|
|
| ## Author |
|
|
| **Khalid Ahmad Raza** |
|
|
| B.Tech Computer Engineering |
|
|
| National Institute of Technology Kurukshetra |
