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--- |
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base_model: unsloth/Qwen2.5-1.5B-Instruct |
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library_name: peft |
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license: mit |
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datasets: |
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- ituperceptron/turkish_medical_reasoning |
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language: |
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- tr |
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pipeline_tag: question-answering |
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tags: |
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- medical |
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- biology |
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--- |
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# Model Card for Turkish-Medical-R1 |
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## Model Details |
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This model is a fine-tuned version of Qwen2.5-1.5B-Instruct for medical reasoning in Turkish. The model was trained on ituperceptron/turkish_medical_reasoning dataset, which contains |
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instruction-tuned examples focused on clinical reasoning, diagnosis, patient care, and medical decision-making. |
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### Model Description |
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- **Developed by:** Rustam Shiriyev |
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- **Language(s) (NLP):** Turkish |
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- **License:** MIT |
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- **Finetuned from model:** unsloth/Qwen2.5-1.5B-Instruct |
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## Uses |
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### Direct Use |
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- Medical Q&A in Turkish |
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- Clinical reasoning tasks (educational or non-diagnostic) |
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- Research on medical domain adaptation and multilingual LLMs |
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### Out-of-Scope Use |
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This model is intended for research and educational purposes only. It should not be used for real-world medical decision-making or patient care. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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from huggingface_hub import login |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from peft import PeftModel |
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login(token="") |
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tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen2.5-1.5B-Instruct",) |
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base_model = AutoModelForCausalLM.from_pretrained( |
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"unsloth/Qwen2.5-1.5B-Instruct", |
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device_map={"": 0}, token="" |
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) |
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model = PeftModel.from_pretrained(base_model,"Rustamshry/Rustamshry/Turkish-Medical-R1") |
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question = "Medüller tiroid karsinomu örneklerinin elektron mikroskopisinde gözlemlenen spesifik özellik nedir?" |
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prompt = ( |
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"### Talimat:\n" |
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"Siz bir tıbb alanında uzmanlaşmış yapay zeka asistanısınız. Gelen soruları yalnızca Türkçe olarak, " |
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"açıklayıcı bir şekilde yanıtlayın.\n\n" |
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f"### Soru:\n{question.strip()}\n\n" |
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f"### Cevap:\n" |
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) |
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input_ids = tokenizer(prompt, return_tensors="pt").to(model.device) |
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outputs = model.generate( |
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**input_ids, |
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max_new_tokens=2048, |
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) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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## Training Data |
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- Dataset: ituperceptron/turkish_medical_reasoning; Translated version of FreedomIntelligence/medical-o1-reasoning-SFT (Turkish, ~7K examples) |
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## Evaluation |
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No formal quantitative evaluation yet. |
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### Framework versions |
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- PEFT 0.15.2 |