README.md

---
base_model: unsloth/Qwen2.5-1.5B-Instruct
library_name: peft
license: mit
datasets:
- ituperceptron/turkish_medical_reasoning
language:
- tr
pipeline_tag: text-generation
tags:
- medical
- biology
- transformers
- unsloth
- trl
---

# 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/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