# Qwenmark2-0.5B Fine-Tuned Model Overview

This is a fine-tuned version of the Qwen2-0.5B model, a transformer-based language model developed by Alibaba Cloud. The model has been fine-tuned using **LoRA (Low-Rank Adaptation)** and **Unsupervised Parameter-Efficient Fine-Tuning (PFT)** to specialize in deep learning and machine learning educational tasks.

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## ✅ Key Features

- 🎯 **Specialization**: Deep learning & machine learning Q&A  
- 📘 **Educational Utility**: Enhanced explanation performance  
- ⚙️ **Efficient Deployment**: Optional 4-bit quantization  
- 💡 **Contextual Understanding**: Supports RAG-style inference

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## 🧠 Model Details

- **Base Model**: `Qwen/Qwen2-0.5B`
- **Architecture**: Transformer-based Causal Language Model
- **Parameters**: 0.5 Billion
- **Tokenizer**: Qwen2 tokenizer (`left padding`, `eos_token` as `pad_token` if unspecified)
- **Quantization**: Supports 4-bit via `BitsAndBytesConfig`
- **Devices Supported**: CUDA-enabled GPUs / CPU

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## 🔧 Fine-Tuning Method

### 1. LoRA Distillation
- **Data**: DeepseekR1-generated answers to curated ML/DL questions
- **Config**: `r=16`, `lora_alpha=32`, `target_modules=["q_proj", "v_proj"]`
- **Training**: 3 epochs, batch size 2, grad_accum=4, lr=2e-4, FP16
- **Output**: `./lora_finetuned`

### 2. Unsupervised PFT
- **Data**: Extracted text from `course_slides_text.txt`
- **Training**: 1 epoch, batch size 2, grad_accum=4, lr=1e-5, FP16
- **Output**: `./LoRA&pft_finetuned`

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## 🛠️ Installation

Install required packages:
```bash
pip install torch transformers peft datasets sentence-transformers pdf2image pytesseract