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@@ -14,11 +14,11 @@ tags:
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  - medical
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  ---
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- # qure: Small Medical AI Model
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  ## Overview
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- 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.
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  While the model is open-source to foster innovation, a proprietary version with enhanced clinical applications is under active development.
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@@ -34,13 +34,13 @@ While the model is open-source to foster innovation, a proprietary version with
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  3. **Research Enablement**: Provide insights for researchers working on medical datasets.
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  ## Installation
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- To use qure, ensure you have Python 3.8+ and the necessary dependencies installed.
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  ### Step 1: Clone the Repository
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  ```bash
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- git clone https://github.com/yourusername/qure.git
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- cd qure
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  ```
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  ### Step 2: Install Dependencies
@@ -54,7 +54,7 @@ pip install -r requirements.txt
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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- model_name = "yourusername/qure"
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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  model = AutoModelForCausalLM.from_pretrained(model_name)
@@ -63,12 +63,12 @@ model = AutoModelForCausalLM.from_pretrained(model_name)
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  - **Training Time**: 15 hours for fine-tuning on a medical dataset of 50,000 samples (depending on the hardware used).
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  - **Inference Latency**: ~300ms per sample on a single A100 GPU for text analysis, and ~500ms for image analysis.
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- 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.
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  ## Model Card
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  ### License
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- qure is licensed under the MIT License, encouraging widespread use and adaptation.
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  ### Base Model
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  - **Architecture**: Meta-Llama/Llama-3.2-11B-Vision-Instruct
@@ -80,17 +80,17 @@ qure is licensed under the MIT License, encouraging widespread use and adaptatio
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  - Healthcare
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  ### Roadmap
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- While qure remains an open-source initiative, we are actively developing a proprietary version. This closed-source version will include:
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  - Real-time patient monitoring capabilities.
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  - Enhanced diagnostic accuracy with custom-trained datasets.
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  - Proprietary algorithms for predictive analytics.
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  Stay tuned for updates!
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  ### Contribution
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- 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.
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  ### Disclaimer
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- 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.
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  ### Acknowledgements
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  This project is made possible thanks to:
 
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  - medical
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  ---
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+ # quro1: Small Medical AI Model
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  ## Overview
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+ quro1 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, quro1 combines language understanding and image analysis to assist in transforming medical data into actionable insights.
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  While the model is open-source to foster innovation, a proprietary version with enhanced clinical applications is under active development.
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  3. **Research Enablement**: Provide insights for researchers working on medical datasets.
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  ## Installation
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+ To use quro1, ensure you have Python 3.8+ and the necessary dependencies installed.
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  ### Step 1: Clone the Repository
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  ```bash
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+ git clone https://github.com/yourusername/quro1.git
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+ cd quro1
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  ```
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  ### Step 2: Install Dependencies
 
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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+ model_name = "yourusername/quro1"
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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  model = AutoModelForCausalLM.from_pretrained(model_name)
 
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  - **Training Time**: 15 hours for fine-tuning on a medical dataset of 50,000 samples (depending on the hardware used).
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  - **Inference Latency**: ~300ms per sample on a single A100 GPU for text analysis, and ~500ms for image analysis.
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+ These evaluation results show that quro1 excels in multiple domains of healthcare AI, offering both high accuracy in medical text understanding and strong performance in image analysis tasks.
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  ## Model Card
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  ### License
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+ quro1 is licensed under the MIT License, encouraging widespread use and adaptation.
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  ### Base Model
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  - **Architecture**: Meta-Llama/Llama-3.2-11B-Vision-Instruct
 
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  - Healthcare
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  ### Roadmap
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+ While quro1 remains an open-source initiative, we are actively developing a proprietary version. This closed-source version will include:
84
  - Real-time patient monitoring capabilities.
85
  - Enhanced diagnostic accuracy with custom-trained datasets.
86
  - Proprietary algorithms for predictive analytics.
87
  Stay tuned for updates!
88
 
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  ### Contribution
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+ We welcome contributions from the community to make quro1 better. Feel free to fork the repository and submit pull requests. For feature suggestions, please create an issue in the repository.
91
 
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  ### Disclaimer
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+ quro1 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.
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  ### Acknowledgements
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  This project is made possible thanks to: