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README.md

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+---
+language: en
+license: mit
+base_model: EleutherAI/gpt-neo-125M
+tags:
+- text-generation
+- medical
+- question-answering
+- lora
+- peft
+- gpt-neo
+datasets:
+- medalpaca/medical_meadow_medqa
+---
+
+# 🏥 GPT-Neo 125M — Medical QA LoRA Fine-tune
+
+## Model Description
+موديل GPT-Neo 125M تم عمله Fine-tuning على داتا أسئلة وأجوبة طبية (USMLE style) باستخدام LoRA.
+
+- **Developed by:** Hazem Galal
+- **Supervised by:** Eng. Mahmoud Khorshed
+- **License:** MIT
+- **Base Model:** EleutherAI/gpt-neo-125M
+
+## المواصفات
+
+| المواصفة | القيمة |
+|-----------|--------|
+| Base Model | EleutherAI/gpt-neo-125M |
+| Dataset | medalpaca/medical_meadow_medqa |
+| LoRA Rank (r) | 8 |
+| LoRA Alpha | 16 |
+| Block Size | 256 |
+| Epochs | 3 |
+| Learning Rate | 2e-4 |
+
+## طريقة الاستخدام
+
+    from transformers import AutoTokenizer, AutoModelForCausalLM
+    
+    model = AutoModelForCausalLM.from_pretrained("hazemgalal1/gptneo125m-medical-qa-merged")
+    tokenizer = AutoTokenizer.from_pretrained("hazemgalal1/gptneo125m-medical-qa-merged")
+    
+    prompt = (
+        "### Instruction:\nAnswer the following medical question.\n\n"
+        "### Question:\nWhat is the first-line treatment for hypertension?\n\n"
+        "### Answer:\n"
+    )
+    
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.7)
+    print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+## تنبيه
+هذا الموديل للأغراض التعليمية فقط، وليس بديلاً عن الاستشارة الطبية المتخصصة.