🩺 Radiology Assistant (Qwen3.5-2B Fine-Tuned with QLoRA)
📌 Model Overview
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
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
