# CHFReportGenerator 🫀

An evidence-anchored, research-focused system for automated Congestive Heart Failure (CHF) analysis that provides diagnostic decision support, clinically grounded report generation, and explainable evidence highlighting supporting regions.
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## 🔖 Project at a Glance

- **Project name:** CHFReportGenerator 
- **Primary goal:** Generate clinically grounded and explainable CHF reports  
- **Focus:** Interpretability, transparency, and reproducibility  
- **Intended users:** Researchers, PhD evaluators, clinicians (research support)
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## ✨ Key Features & Contributions

- **Evidence-anchored reporting**  
  Every generated finding is explicitly linked to supporting evidence.
- **Clinically grounded narratives**  
  Outputs are written in structured, clinically meaningful language.
- **Parameter-efficient fine-tuning (QLoRA)**  
  Adapts a large language model with minimal computational cost.
- **Research-first design**  
  Built to support academic evaluation and reproducibility.
- **Hardware-efficient**  
  4-bit quantization enables large-model usage on limited GPU resources.
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## 🧠 Model Overview

- **Base model:** Qwen2.5-VL-7B-Instruct  
- **Model type:** Vision-Language Large Language Model  
- **Quantization:** 4-bit (BitsAndBytes)  
- **Framework:** Unsloth  
- **Maximum sequence length:** 2048 tokens  
- **Fine-Tuning Method:** QLoRA 

The base model provides general reasoning and language understanding,
while CHF-specific behavior is introduced through lightweight adapters.
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## ⚙️ Installation

### Requirements

- Python 3.8 or higher  
- CUDA-enabled GPU (recommended)  
- PyTorch  
- Hugging Face Transformers  
- Unsloth  
- BitsAndBytes  

All dependencies are listed in `requirements.txt`.

### Step-by-Step Setup

```bash
git clone https://huggingface.co/aiyubali/CHFReportGenerator
cd CHFReportGenerator
pip install -r requirements.txt