TinyLlama AI technology page Top Builders

Explore the top contributors showcasing the highest number of TinyLlama AI technology page app submissions within our community.

TinyLlama

TinyLlama is a compact AI model developed by Ollama community. It offers efficient language processing capabilities in a smaller package, making it suitable for applications with limited computational resources. The model is designed for a variety of tasks, including conversational AI and real-time text generation, and supports deployment on edge devices.

General
Relese date	January 15, 2024
Author	Ollama comunity
Website	TinyLlama
Repository	https://github.com/jzhang38/TinyLlama
Type	AI Language Model

Key Models and Features

TinyLlama 1.1B: This is the primary model in the TinyLlama family, featuring 1.1 billion parameters. It is pre-trained on 3 trillion tokens, providing a robust base for various natural language understanding and generation tasks.
TinyLlama Chat: Fine-tuned specifically for conversational applications, this variant is optimized for generating human-like responses in dialogue scenarios. It leverages datasets like UltraChat and UltraFeedback for training, enhancing its conversational abilities.

Training and Performance

The model uses an autoregressive language modeling objective and employs techniques such as FlashAttention and grouped query attention to enhance computational efficiency. It has been evaluated on commonsense reasoning and problem-solving tasks, showing competitive performance compared to other models like OPT and Pythia.

The training involved a blend of natural language and code data, with a significant portion dedicated to improving the model’s handling of coding tasks.

Applications and Use Cases

Conversational AI: The TinyLlama Chat model is well-suited for chatbots and virtual assistants, capable of engaging in interactive dialogues.
Edge Deployment: Its compact size allows for deployment on devices with limited computational power, such as mobile or embedded systems.
Speculative Decoding and Real-Time Translation: The model’s efficient architecture makes it ideal for scenarios requiring real-time language processing, including in video games or other interactive media.

Availability

TinyLlama models are available under the Apache 2.0 license on platforms like Hugging Face and GitHub. They are part of an open-source project aimed at democratizing access to powerful language models.

👉 For more detailed information, you can visit the TinyLlama GitHub repository or explore the models on Hugging Face.

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TinyLlama AI technology page Hackathon projects

Discover innovative solutions crafted with TinyLlama AI technology page, developed by our community members during our engaging hackathons.

Maa-connect

Our project is a simple yet powerful network management chatbot designed to assist teachers, health facility network managers, and individuals in performing basic diagnostic analysis of their network. The chatbot will provide real-time insights into network performance, helping users troubleshoot connectivity issues, monitor network health, and optimize usage. By leveraging AI, the chatbot will analyze network conditions, suggest solutions, and guide users through resolving common problems such as slow speeds, connection drops, and device conflicts. This solution is especially useful for educational institutions and healthcare facilities where stable internet connectivity is essential for seamless operations. Teachers can use the chatbot to ensure smooth online classes, while health facility network managers can rely on it to maintain uninterrupted access to critical digital resources. Individuals can also use it to diagnose and improve their home network performance. The chatbot will have an intuitive interface, allowing users to interact in natural language. Whether through a web-based platform, mobile app, or integration with existing network monitoring tools, our chatbot aims to simplify network diagnostics and empower users with actionable insights.

AIrine - offline AI Assistant for Teachers

AIrine is a decentralized educational system designed to function in resource-limited regions. It leverages local nodes deployed on Android devices, allowing offline access to AI-powered learning tools. The system offers personalized assistance, helping both students and teachers with minimal hardware requirements. AIrine adapts to local needs, providing real-time feedback, explanations, and educational resources without the need for an internet connection. When connectivity is available, data can be updated and synchronized with other nodes, ensuring the system stays current. This innovative approach enables scalable, accessible education in areas with limited technological infrastructure.

NetConnect

Public Sector Network Connectivity Analyzer The Public Sector Network Connectivity Analyzer is a comprehensive solution designed to address the critical need for reliable network monitoring across public institutions. Our application serves as an essential tool for IT administrators managing connectivity infrastructure for schools, healthcare facilities, government offices, libraries, and other public service organizations. Core Capabilities Real-Time Network Visualization Interactive diagrams and topology maps provide clear visibility into how public institutions are connected, displaying network elements, connection points, and infrastructure components with intuitive visualization tools. Performance Monitoring System Our platform continuously tracks vital network metrics including uptime percentages, latency measurements, bandwidth utilization, and connection status across the entire public sector network, enabling proactive management. Advanced Simulation Engine IT professionals can run comprehensive simulations to test network resilience under various scenarios such as increased user loads, infrastructure failures, or cyber incidents, helping identify vulnerabilities before they impact critical services. Institution Management Portal Administrators can efficiently manage information about connected institutions, monitor their connection status in real-time, and access detailed performance metrics through a unified dashboard interface. Geographic Mapping Integration Our system incorporates geographic visualization capabilities to display the physical distribution of institutions and network infrastructure across regions, facilitating better resource allocation and planning. Technical Implementation This solution addresses the unique challenges faced by public sector organizations that require reliable connectivity for delivering essential services to communities, while providing the tools needed to ensure network resilience, performance, and security.

Maa Connect

Maa Connect is a lightweight, multilingual AI-powered network management assistant designed to empower teachers and non-technical users in underserved rural and remote communities. The platform provides accessible learning resources on network management and optimization while enabling users to address simple network issues independently. By focusing on computational efficiency and adaptability, Maa Connect aims to bridge the digital divide and ensure uninterrupted access to online education. Key Features Network Learning Platform: Offers user-friendly resources to help users understand network management concepts and best practices. AI Network Assistant: Provides step-by-step guidance for troubleshooting and fixing common network issues. Multilingual Support: Ensures accessibility by supporting multiple languages, catering to the linguistic diversity of target communities. Lightweight and Efficient: Designed for low-resource environments, requiring minimal computational power. Adaptability: Configurable for diverse network setups and requirements. Problem Statement Many underserved communities face restricted access to network management knowledge and skilled practitioners. When network problems occur, internet-dependent education often halts for extended periods, disrupting learning. Maa Connect addresses this by equipping users with essential skills and tools to maintain and optimize their networks, overcoming linguistic and cultural barriers.

Llama Legend Personalized Diabetes Plan

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.

Emergency Helper

We are excited to present our project, which focuses on addressing emergencies and environmental issues through an advanced AI-driven solution. In this hackathon, our team has developed an application that can generate accurate responses to a variety of emergency scenarios and environmental challenges. Project Overview: Model and Dataset: We utilized the LLaMA 3.1 model with 405B parameters to generate a synthetic dataset of approximately 2,000 question-answer pairs. This dataset was initially created in Excel and later converted into JSON format for model training. The TinyLLaMA 1.1 billion parameter chat version was fine-tuned using this dataset, allowing our model to provide highly contextual and relevant responses. Training and Fine-Tuning: We leveraged the resources available on Google Colab, specifically using T4 GPUs to generate the dataset. We leveraged the resources available on Kaggle, specifically using T4 x2 GPUs to train our model. After completing the fine-tuning process, we pushed the model to Hugging Face, making it accessible for deployment and further testing. Deployment: The model was deployed on Hugging Face Spaces, where we integrated a user-friendly Gradio UI interface. This interface enables users to input queries and receive real-time responses directly from the model. All project files and necessary documentation have been committed to our repository, ensuring full transparency and accessibility. Team: Our project was made possible by the collaborative efforts of a dedicated team of six members: Team Lead: Umar Majeed LinkedIn Profile Team Members: Moazzan Hassan LinkedIn Shahroz Butt LinkedIn Sidra Hammed LinkedIn Muskan Liaqat LinkedIn Sana Qaisar LinkedIn We would like to thank LabLab AI for this opportunity, and we look forward to the impact our application can make in real-world scenarios.