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