📃 Paper ｜🤗 huggingface repository

📒 Table of Contents

• 📒 Table of Contents
• 📍 Overview
• 🏃 Training process
• 📊 Results
• ⚠️ Risks and Limitations
• ⛔️ License
• 🤝 Collaborators

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📍 Overview

We present gaokerena-R, a model trained with a limited-data approach to enhance the Persian medical reasoning capabilities of the aya-expanse-8b model. Despite using less data, gaokerena-R outperforms our previous model, gaokerena-V, which was trained on a much larger dataset. This demonstrates the effectiveness of our reasoning-focused training strategy under data-constrained conditions.

🏃 Training process

📊 Results

	gaokerena-R + aya-expanse-8b(verifier)	gaokerena-V	aya-expanse-8b
MMLU-anatomy(fa)	47.40	48.14	40.74
MMLU-medicalgenetics(fa)	56.0	53.0	49.0
MMLU-collegemedicine(fa)	50.28	43.93	44.51
MMLU-clinicalknowledge(fa)	58.86	55.47	52.07
MMLU-professionalmedicine(fa)	48.89	47.05	45.58
MMLU-collegebiology(fa)	54.86	47.22	45.14
MMLU(avg)	52.98	49.31	46.64
IBMSEE Sept 2023	46.42	38.69	34.52
Prompt	COT for the main model & Straight for the verifier	Straight	Straight
Inference_time	$\approx 5 \times 35 + 8 s$	$\approx 10s$	$\approx 10s$

⚠️ Risks and Limitations

While Gaokerena aims to provide relatively accurate information, it is not a substitute for professional medical advice. The model may have limitations in:

• Handling medical emergencies.
• Addressing highly specialized or rare medical conditions.
• Offering region-specific guidance, as the training data does not include localized Persian medical practices.

⛔️ License

CC BY-NC-SA 4.0 (non-commercial use only)

🤝 Collaborators

1. Mehrdad Ghassabi
2. Sadra Hakim
3. Dr. Hamid Reza Baradaran Kashani
4. Pedram Rostami