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:
- Base Model: Showed 15% accuracy with significant juristic hallucinations.
- RAG Only: Accurate but lacked scholarly linguistic style.
- FT Only (Azhar v0.2): Demonstrated "Juristic Intuition" and high fluency in classical Arabic.
- 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."
