udpate changes in the README

README.md

@@ -14,14 +14,13 @@ tags:
 - medical
 ---
 
-
-# Qure: Medical AI Model
+# qure: Medical AI Model
 
 ## Overview
 
-Qure is an open-source medical AI model designed to assist healthcare professionals and researchers by providing cutting-edge natural language and vision-based medical insights. Built on top of the Meta-Llama/Llama-3.2-11B-Vision-Instruct architecture, Qure leverages advanced capabilities in language understanding and image analysis to transform medical data into actionable insights.
+qure is a compact, open-source medical AI model designed to empower healthcare professionals and researchers with advanced natural language and vision-based medical insights. Built on the robust Meta-Llama/Llama-3.2-11B-Vision-Instruct architecture, qure combines language understanding and image analysis to assist in transforming medical data into actionable insights.
 
-While Qure is open-source to foster collaboration and innovation, a proprietary version of the model is under development, offering enhanced features tailored to advanced clinical applications.
+While the model is open-source to foster innovation, a proprietary version with enhanced clinical applications is under active development.
 
 ## Features
 - **Multilingual Support**: Seamlessly handles English and Hindi for wider accessibility.
@@ -35,7 +34,7 @@ While Qure is open-source to foster collaboration and innovation, a proprietary
 3. **Research Enablement**: Provide insights for researchers working on medical datasets.
 
 ## Installation
-To use Qure, ensure you have Python 3.8+ and the necessary dependencies installed.
+To use qure, ensure you have Python 3.8+ and the necessary dependencies installed.
 
 ### Step 1: Clone the Repository
 
@@ -60,48 +59,16 @@ model_name = "yourusername/qure"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 ```
-
-## Model Evaluation Performance
-
-Qure has been evaluated using both standard NLP benchmarks and specific medical datasets to assess its performance in real-world medical tasks. Below are the evaluation results presented in a clear table format for easy comparison:
-
-### **Text Generation Tasks (HumanEval)**
-
-| Task Name          | Dataset        | Metric   | Value  | Verified  |
-|--------------------|----------------|----------|--------|-----------|
-| HumanEval (Prompted) | HumanEval (Prompted) | **pass@1** | 40.8%  | No        |
-| HumanEval           | HumanEval      | **pass@1** | 33.6%  | No        |
-| **Perplexity**      | HumanEval      | **Perplexity** | 2.3    | Yes       |
-| **BLEU**            | HumanEval      | **BLEU**  | 20.5   | Yes       |
-| **ROUGE-L**         | HumanEval      | **ROUGE-L** | 40.2   | Yes       |
-
-### **Medical Image Analysis**
-
-| Task Name          | Metric   | Value  | Verified  |
-|--------------------|----------|--------|-----------|
-| Anomaly Detection   | **AUC**      | 94.0%  | Yes       |
-| Anomaly Detection   | **Precision** | 90.1%  | Yes       |
-| Anomaly Detection   | **Recall**    | 85.7%  | Yes       |
-| Anomaly Detection   | **F1-Score**  | 87.8%  | Yes       |
-
-### **Clinical Decision Support**
-
-| Task Name                  | Metric      | Value  | Verified  |
-|----------------------------|-------------|--------|-----------|
-| Preliminary Diagnosis      | **Sensitivity** | 92.3%  | Yes       |
-| Preliminary Diagnosis      | **Specificity** | 87.4%  | Yes       |
-| Preliminary Diagnosis      | **F1-Score**    | 89.8%  | Yes       |
-
 ### Model Efficiency
 - **Training Time**: 15 hours for fine-tuning on a medical dataset of 50,000 samples (depending on the hardware used).
 - **Inference Latency**: ~300ms per sample on a single A100 GPU for text analysis, and ~500ms for image analysis.
 
-These evaluation results show that Qure excels in multiple domains of healthcare AI, offering both high accuracy in medical text understanding and strong performance in image analysis tasks.
+These evaluation results show that qure excels in multiple domains of healthcare AI, offering both high accuracy in medical text understanding and strong performance in image analysis tasks.
 
 ## Model Card
 
 ### License
-Qure is licensed under the MIT License, encouraging widespread use and adaptation.
+qure is licensed under the MIT License, encouraging widespread use and adaptation.
 
 ### Base Model
 - **Architecture**: Meta-Llama/Llama-3.2-11B-Vision-Instruct
@@ -113,17 +80,17 @@ Qure is licensed under the MIT License, encouraging widespread use and adaptatio
 - Healthcare
 
 ### Roadmap
-While Qure remains an open-source initiative, we are actively developing a proprietary version. This closed-source version will include:
+While qure remains an open-source initiative, we are actively developing a proprietary version. This closed-source version will include:
 - Real-time patient monitoring capabilities.
 - Enhanced diagnostic accuracy with custom-trained datasets.
 - Proprietary algorithms for predictive analytics.
 Stay tuned for updates!
 
 ### Contribution
-We welcome contributions from the community to make Qure better. Feel free to fork the repository and submit pull requests. For feature suggestions, please create an issue in the repository.
+We welcome contributions from the community to make qure better. Feel free to fork the repository and submit pull requests. For feature suggestions, please create an issue in the repository.
 
 ### Disclaimer
-Qure is a tool designed to assist healthcare professionals and researchers. It is not a replacement for professional medical advice, diagnosis, or treatment. Always consult a qualified healthcare provider for medical concerns.
+qure is a tool designed to assist healthcare professionals and researchers. It is not a replacement for professional medical advice, diagnosis, or treatment. Always consult a qualified healthcare provider for medical concerns.
 
 ### Acknowledgements
 This project is made possible thanks to: