Azhar_Model_v0.2_Final

📜 Project Description

This model is a fine-tuned version of Qwen2.5-7B optimized for Islamic Jurisprudence (Fiqh) using the Shamela Library corpus.

📊 Training Specifications

• Base Model: Qwen2.5-7B
• Training Data: 20,000 high-quality juristic records from Shamela.
• Total Processed Tokens: ~24.5 Million Tokens.
• Optimization Steps: 3,000 Steps.
• Effective Batch Size: 16 (via Gradient Accumulation).
• Hardware: Kaggle Dual T4 GPUs.
• Total Processed Tokens: 24,576,000
• Loss Improvement: 85.21%
• Final Perplexity: 9.68

📈 Performance Gains

Metric	Initial Value	Final Value	Improvement
Cross-Entropy Loss	15.3510	2.2705	85.21%
Perplexity (PPL)	4,643,546.51	9.68	99.99%

🧪 Qualitative Comparison Results:

The model was evaluated across 4 paradigms. The Azhar Hybrid approach showed:

• Accuracy: Significant reduction in juristic hallucinations compared to the Base model.
• Linguistic Style: Successful adoption of classical 'Shamela' phrasing and scholarly terminology.
• Reliability: High consistency in providing evidence-based rulings (Fiqh).

🧪 Qualitative Evaluation (Quad-Comparison Analysis)

To ensure reliability, the model was tested across four different paradigms:

1. Base Model: Showed 15% accuracy with significant juristic hallucinations.
2. RAG Only: Accurate but lacked scholarly linguistic style.
3. FT Only (Azhar v0.2): Demonstrated "Juristic Intuition" and high fluency in classical Arabic.
4. Hybrid (FT+RAG): The optimal configuration, achieving the highest scores in both factual accuracy and stylistic authenticity.

📂 Verification Files

The full comparison results (Base vs RAG vs FT vs Hybrid) are available in the attached Azhar_Model_Quad_Comparison_v0.1.csv file in this repository.

📉 Visualized Convergence

The training exhibited a stable downward trend in both Loss and Perplexity curves, indicating successful domain adaptation without catastrophic forgetting.

⚖️ Ethical Considerations & Use Case

This model is intended for academic and research purposes to assist scholars in navigating classical texts. It should be used as a supplementary tool alongside traditional scholarly verification.

Developed by: MSc. Shamil Al-Mohammedi This model serves as the primary artifact for the research paper: "Fine-Tuning Large Language Models on Classical Arabic Juristic Corpora: A Case Study on the Shamela Library."