In the Edge Runners 3.2 Hackathon, we're creating a Personalized Diabetes Plan app using Meta's advanced Llama 3.2 models. Our approach focuses on three key objectives: Generate Synthetic Data: Leveraging Llama 3.2's capabilities, we create high-quality, diverse datasets to enhance data variety and improve model training. Optimize Models: We train smaller models such as TinyLlama and Phi-3 to maximize their performance on edge devices like smartphones, tablets, and IoT devices. Deploy on Edge Devices: Our cloud prototypes are designed for seamless integration on edge hardware, ensuring efficient local data processing without relying heavily on centralized cloud resources. This project showcases our innovative solution's potential to run sophisticated AI models directly on edge devices, transforming diabetes management into a highly personalized experience.

Personalized Diabetes Management App Tailored Plans Based on Individual Needs: Our app leverages advanced algorithms to analyze your unique blood sugar patterns, lifestyle factors, and personal preferences to create highly personalized diabetes management plans. Comprehensive Recommendations: Receive expert guidance on diet, exercise, medication, and other lifestyle factors that can significantly impact your blood sugar control. Our recommendations are tailored to your specific needs and goals. Continuous Monitoring and Adjustments: Track your blood sugar levels, track your progress, and receive real-time feedback on your management plan. Our app allows for easy adjustments to your plan as your needs and circumstances change. Empowering Individuals: By providing personalized guidance and support, our app empowers individuals with diabetes to take control of their health and improve their quality of life. Enhanced Health Outcomes: Our personalized plans can help reduce the risk of diabetes-related complications, improve blood sugar control, and increase overall well-being. User-Friendly Interface: Our app features a user-friendly interface that makes it easy to track your progress, access your personalized plan, and connect with a community of individuals living with diabetes. Integration with Medical Devices: Our app can seamlessly integrate with your blood sugar monitor and other medical devices, providing a comprehensive view of your health data. Privacy and Security: We prioritize the privacy and security of your personal health information. Our app adheres to strict data protection standards to ensure your data is always safe and confidential.

Students and researchers face immense digital content, leading to digital eye strain and cognitive overload. The constant strain not only affects health but also hampers comprehension and retention, making efficient reading essential for maintaining productivity and well-being. Our team proudly present you with a powerful text summarization tool designed to condense extensive text into concise summaries, saving you time and enhancing comprehension. We have employed the powerful model of Llama 3 along with the frameworks of Streamlit, torch, PyPDF2 and transformers. It Supports direct text input or PDF uploads for summarization. Our app Utilizes advanced algorithms from Llama 3 to ensure accurate and context-aware summaries. Text summarization tools are gaining traction as essential aids in educational, corporate, and research sectors. Furthermore, the increasing demand for efficient reading tools amongst academics, professionals, and students is expanding its scope. One of the major strengths of our app is that it saves time on reading, supports collaborative learning and work. Weakness is that our text summarizer depends on the quality of input text for optimal output. Expanding usage in educational technologies and corporate knowledge management has increased the scope of our tool. However, High competition with existing summarization and reading assistance tools is a challenge for us to keep improving. One of the major areas of improvement is that the file upload limit is 200MB and we can improve it so that multiple files or zip folders can be uploaded to get the desired result.

Project Name: SmartCoder Developers face challenges such as: Manual Code Reviews: Time-consuming and error-prone. Boilerplate Code Generation: Tedious and repetitive. Test Case Generation: Labor-intensive and often incomplete. Code Documentation: Frequently neglected, leading to maintenance issues. So SmartCoder AI leverages Codestral to automate and enhance software development, addressing these challenges with: AI-Powered Code Review Assistant which analyzes code structure and quality, detects bugs and enforces coding standards, provides improvement suggestions. Intelligent Code Snippet Generator: Generates code snippets from natural language descriptions, customizable and integrates common libraries. AI-Driven Test Case Generator: Generates comprehensive test cases and provides coverage reports. Code Documentation Generator: Parses code and generates human-readable documentation. Market Value Code Review Assistant: Ensures consistent code quality, saving time. Code Snippet Generator: Speeds up coding and reduces errors. Test Case Generator: Enhances code reliability and saves time. Documentation Generator: Maintains comprehensive code documentation, crucial for large and open-source projects. Conclusion "SmartCoder" automates critical aspects of software development, improving productivity, code quality, and efficiency. It meets the high demand for automated development tools, making it a valuable asset for development teams.

Our healthcare project features a bot that can provide information on medical issues based on questions asked by users. While our platform promotes the sharing of symptoms for informational purposes, it is not a substitute for professional medical advice. For accurate guidance on health concerns, it is crucial to consult with a qualified healthcare professional. I, as an AI, cannot diagnose or provide medical advice. It is imperative to seek assistance from a healthcare professional who can assess your condition properly. Our healthcare project includes a bot that is designed to help users find information related to medical issues. The bot can answer questions related to common symptoms, treatments, and conditions. However, it is important to understand that the information provided on our platform is not intended to replace professional medical advice. While you can share your symptoms and ask questions on our platform, it is always recommended that you consult with a qualified healthcare professional for accurate diagnosis and treatment. As an AI assistant, I am programmed to provide useful information and support, but I cannot diagnose or treat medical conditions. It is essential to seek the guidance of a healthcare professional who can properly assess your condition and provide appropriate treatment. Our platform aims to promote awareness and education about health concerns, but it is not a substitute for medical advice. Please prioritize your health and seek the assistance of a healthcare expert for any medical concerns you may have.

Revolutionize your home design with Architex-AI, "Building Tomorrow's Homes with 3D Structure". We leverage cutting-edge AI (GPT-4, DALL-E) to craft personalized 3D designs tailored to your dreams. Simply tell us your preferences, upload inspiration pics, or show us your current home. Our intelligent platform interprets your input, crafting unique concepts that blend existing elements with innovative touches. Use Django's seamless interface to refine details, provide feedback, and ensure your vision comes to life. We visualize your dream home with stunning 3D renderings, thanks to DALL-E, giving you a clear picture of your future space, and with GPT-4's constant learning, we're always evolving, and adapting to new trends and technologies in architecture and construction.

We attempted to instill the deterministic, rule-based reasoning found in ELIZA into a more advanced, probabilistic model like an LLM. This serves a dual purpose: To introduce a controlled variable in the form of ELIZA's deterministic logic into the more "fuzzy" neural network-based systems. To create a synthetic dataset that can be used for various Natural Language Processing (NLP) tasks, beyond fine-tuning the LLM. [ https://huggingface.co/datasets/MIND-INTERFACES/ELIZA-EVOL-INSTRUCT ] [ https://www.kaggle.com/code/wjburns/pippa-filter/ ] ELIZA Implementation: We implemented the script meticulously retaining its original transformational grammar and keyword matching techniques. Synthetic Data Generation: ELIZA then generated dialogues based on a seed dataset. These dialogues simulated both sides of a conversation and were structured to include the reasoning steps ELIZA took to arrive at its responses. Fine-tuning: This synthetic dataset was then used to fine-tune the LLM. The LLM learned not just the structure of human-like responses but also the deterministic logic that went into crafting those responses. Validation: We subjected the fine-tuned LLM to a series of tests to ensure it had successfully integrated ELIZA's deterministic logic while retaining its ability to generate human-like text. Challenges Dataset Imbalance: During the process, we encountered issues related to data imbalance. Certain ELIZA responses occurred more frequently in the synthetic dataset, risking undue bias. We managed this through rigorous data preprocessing. Complexity Management: Handling two very different types of language models—rule-based and neural network-based—posed its unique set of challenges. Significance This project offers insights into how the strength of classic models like ELIZA can be combined with modern neural network-based systems to produce a model that is both logically rigorous and contextually aware.

🤖 You have 4 weeks to build. Join in at any point! 🌐 Connect with a global tech community. 🎉 Win from a $30,000 cash prize pool. 💾 The winning agent will become the AutoGPT in the 150000 star repository! 🚀 You may continue your startup journey after the Hackathon! 🖥️ Utilize Weaviate as the vector database for your agent with open access.

Fine-tuning is a technique that empowers pre-trained models to perform specific tasks or behaviors, opening up a world of possibilities for customization and specialization. Dive into the details of this cutting-edge technology and showcase your skills in this fast-paced hackathon!

Craft an inventive application, tool, or interactive feature that utilizes Legal AI to address legal challenges and revolutionize the industry!