TingWang/SlideAssistant

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:

pip install torch transformers peft datasets sentence-transformers pdf2image pytesseract