---
base_model: unsloth/Qwen2.5-1.5B-Instruct
library_name: peft
license: mit
datasets:
- Rustamshry/medical_o1_reasoning_SFT_az
language:
- az
pipeline_tag: question-answering
tags:
- biology
- medical
---

# Model Card for Qwen2.5-1.5B-Medical-Az

### Model Description

This model is a fine-tuned version of Qwen2.5-1.5B-Instruct on an Azerbaijani medical reasoning dataset.
It is designed to understand complex medical instructions, interpret clinical cases, and generate informed answers in Azerbaijani.

- **Developed by:** Rustam Shiriyev
- **Model type:** Causal Language Model
- **Language(s) (NLP):** Azerbaijani
- **License:** MIT
- **Finetuned from model:** unsloth/Qwen2.5-1.5B-Instruct
- **Fine-tuning Method:** Supervised Fine-Tuning (SFT) using Unsloth + LoRA
- **Domain:** Medical Question Answering / Reasoning
- **Dataset:** The training data consists of ~19,696 rows, translated from the FreedomIntelligence/medical-o1-reasoning-SFT dataset


## Uses

### Direct Use

You can use this model directly for:

- Medical QA tasks in Azerbaijani
- Evaluating LLMs' ability to reason about clinical data in low-resource languages
- Generating educational prompts or tutoring-style medical answers
- Research on instruction tuning and localization of medical language models

### Out-of-Scope Use

- Use in life-critical medical applications
- Any application where incorrect answers could cause harm
- Use by patients or non-medical professionals for self-diagnosis
- Deployment in commercial healthcare systems without regulatory oversight or expert validation

## Bias, Risks, and Limitations

The model has not been clinically validated and must not be used for real medical decision-making.
Trained only on a single-source dataset, so it may not generalize to all medical topics.
Performance in zero-shot generalisation (e.g., English → Azerbaijani medical transfer) has not been tested.


## How to Get Started with the Model

```python
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/Qwen2.5-1.5B-Medical-Az")

question = "45 yaşlı kişi qəfil danışıqda pozulma, yeriyişində dəyişiklik və titrəmə meydana gəlir. Ən ehtimal diaqnoz nədir?"
prompt = f"""### Question:\n{question}\n\n### Response:\n"""

input_ids = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **input_ids, 
    max_new_tokens=2000,
    #temperature=0.6,
    #top_p=0.95,
    #do_sample=True,
    #eos_token_id=tokenizer.eos_token_id
)

print(tokenizer.decode(outputs[0]))
```

## Training Details

### Training Data

The model was fine-tuned on a translated and cleaned version of FreedomIntelligence/medical-o1-reasoning-SFT, which was manually converted into Azerbaijani. 
All examples were filtered for translation quality and medical relevance.

- Dataset (Translated): Rustamshry/medical_o1_reasoning_SFT_az 
- Link of Original Dataset: huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT


### Training Procedure

The model was trained using supervised fine-tuning (SFT) with parameter-efficient fine-tuning (PEFT) via LoRA, using the Unsloth library for memory-optimized training.

- **Training regime:** fp16
- **Epochs:** 2
- **Batch size:** 2
- **Gradient accumulation steps:** 4
- **Max sequence lenght:** 2000
- **Learning rate:** 2e-5
- **Optimizer:** adamw_torch
- **fp16:** True
- **LoRa rank:** 6
- **Aplha:** 16
- **Target Modules:** 28 layers with 28 QKV, 28 O, and 28 MLP.

#### Speeds, Sizes, Times

- **Training speed:** 0.12 steps/sec
- **Total training time:** 11 hours, 26 minutes
- **Total training steps:** 4924

#### Hardware

- **GPUs Used:**. NVIDIA Tesla T4 GPUs via Kaggle Notebook

#### Result

- **Training loss:** 2.68 → 1.63

### Framework versions

- PEFT 0.14.0