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README.md

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+# CHFReportGenerator 🫀
+
+An evidence-anchored, research-focused system for automated report generation
+in Congestive Heart Failure (CHF) analysis.
+
+---
+
+## 🔖 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)
+
+---
+
+## ✨ 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.
+
+---
+
+## 🧠 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  
+
+The base model provides general reasoning and language understanding,
+while CHF-specific behavior is introduced through lightweight adapters.
+
+---
+
+## 🔧 Fine-Tuning Method: QLoRA (Brief)
+
+This project uses **QLoRA (Quantized Low-Rank Adaptation)**, which combines:
+
+- 4-bit quantization of the base model  
+- LoRA-based parameter-efficient fine-tuning  
+
+### How QLoRA is Applied
+
+- The base model is loaded in **4-bit precision**
+- All base model weights remain **frozen**
+- Trainable **LoRA adapters** are introduced
+- Only adapter parameters are updated during training
+
+### Why QLoRA?
+
+- Enables fine-tuning of a 7B model on limited hardware  
+- Preserves general medical and linguistic knowledge  
+- Reduces overfitting and hallucination risk  
+- Supports reproducible, explainable research  
+
+QLoRA allows CHF-specific adaptation without retraining the full model,
+making it well-suited for PhD-level experimentation.
+
+---
+
+## ⚙️ 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