Redme.md file updated according to this system

README.md

@@ -1,8 +1,6 @@
 # CHFReportGenerator 🫀
 
-An evidence-anchored, research-focused system for automated report generation
-in Congestive Heart Failure (CHF) analysis.
-
+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.
 ---
 
 ## 🔖 Project at a Glance
@@ -11,26 +9,20 @@ in Congestive Heart Failure (CHF) analysis.
 - **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
@@ -40,36 +32,10 @@ in Congestive Heart Failure (CHF) analysis.
 - **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.
-
----
-
-## 🔧 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