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.

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.

Basic Social

Basic Social AI App is a AI powered Social Media E-commerce app making very easy for users to build, sell and grow their brands or products. Powered with AI Social Media Search Engine and Advance Video Utility and Generation capabilities Brings E-commerce and Social Interaction under one roof. There 5 Billion social media users => Among them 500 Million Users use social media for earning [As creators]. Sadly,To start earning for a new user it takes 6 to 12 months of time, lots of strategy, initial efforts, lookout for collaborations, partnerships. Social.Earn is a platform where we have integrated AI Content Creation, Brands, products, in app store e-commerce. E-commerce industry in 4000 Billion $ and social media industry is 20 Billion $ market. But currently both operate in separated integrated way. We have build a robust tech for analyzing in-depth information about the objects in the videos (shorts or reels) and create a one click checkout process for the products endorsed in the video. We use TinyLlama for on device ultimate performance and accuracy for the user recommendations. Our state-of-art RAG pipeline integrated with knowledge Graph, which we call Relational Vector DB has outperformed the accuracy benchmarks for Social Media Search engine capabilities for our app. We are very excited and passionate to create a outbreak in way we shop with our E-commerce infused Social Media App Basic Social.

Your Intimate Personal Therapist

This is a personal, intimate AI app targeted at users who seek psychological guidance, specifically those who seek counselling on matters related to cognitive behaviour. The app uses AI driven natural language that engages in meaningful, useful and empathetic dialogue with users. Specifically, it understands the user intent, emotions in each of user's prompt and engages in positive challenging, encouraging and useful responses. In each session, it summarizes the emotion triggers in all the user prompts until that point and displays the top 5 emotion triggers identified with scores as a chart. At the end of the session, it summarizes the whole conversation between the user and the AI (virtual Therapist) and also makes personalised medical and non-medical recommendations/ action plan.