This model is a fine-tuned version of Qwen3.5-2B using QLoRA (4-bit) for efficient training. It is specialized for radiology-related tasks, trained on the unsloth/Radiology_mini dataset.

The goal of this model is to assist with:

Radiology report understanding
Medical question answering (radiology domain)
Clinical text interpretation

⚙️ Model Details

Base Model: unsloth/Qwen3.5-2B
Fine-Tuning Method: QLoRA (4-bit)
Framework: Likely Unsloth + Hugging Face Transformers
Task Type: Instruction Fine-Tuning / Text Generation

📊 Training Configuration

Dataset

Name: unsloth/Radiology_mini
Split Used: train
Subset: default
Evaluation Split: None

Hyperparameters

Epochs: 30
Max Sequence Length: 2048
Learning Rate: 2e-4
Training Method: Parameter-Efficient Fine-Tuning (LoRA)

🚀 Intended Use

✅ Suitable for:

Radiology Q&A systems
Medical NLP research (radiology domain)
Clinical report summarization
Educational tools for radiology

❌ Not suitable for:

Real-world medical diagnosis
Clinical decision-making
Use without human medical supervision

⚠️ Limitations

Trained on a small dataset → may lack generalization
May produce hallucinated medical facts
Not validated for clinical accuracy
Bias may exist depending on dataset content

🧪 Example Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "gawadx1/katyusha-3.5-2B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Explain what a chest X-ray showing opacity might indicate."

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🏗️ Training Procedure

This model was trained using:

QLoRA (4-bit quantization) for memory efficiency
Fine-tuning only LoRA adapters instead of full model weights
Optimized for low-resource hardware

📈 Evaluation

⚠️ No formal evaluation metrics were used.

Suggested future evaluation:

BLEU / ROUGE for report generation
Medical QA benchmarks
Human expert evaluation

🤝 Acknowledgments

Dataset: unsloth/Radiology_mini
Base Model: Qwen team
Training Framework: Unsloth

📜 License

Check the license of:

Base model (Qwen3.5-2B)
Dataset (Radiology_mini)

👨‍💻 Author

Gawadx1

🔥 Future Improvements

Add evaluation dataset
Train on larger medical datasets
Add safety alignment (RLHF / DPO)
Improve hallucination control