Meta Llama 3.2 AI technology Top Builders

Explore the top contributors showcasing the highest number of Meta Llama 3.2 AI technology app submissions within our community.

Llama 3.2

Llama 3.2 is Meta’s latest advancement in open-source large language models (LLMs), designed to make AI more accessible across various platforms and tasks, especially with its new multimodal capabilities. This version focuses on lightweight models optimized for edge devices, while also introducing the ability to process both text and images, broadening the scope of AI applications.

General
AuthorMeta
Release dateSeptember 2024
Websitehttps://www.llama.com/
Documentationhttps://www.llama.com/docs/overview
CollectionLlama 3.2 meta-llama Collection
Model Sizes1B, 3B, 11B, 90B parameters
Technology TypeLarge Language Model (LLM), Multimodal

Key Features

  • Multimodal Processing: Llama 3.2 can handle both text and image inputs, making it useful for visual understanding tasks such as document analysis, image captioning, and visual question answering.

  • Lightweight Models for Edge Devices: The 1B and 3B parameter models are optimized for mobile and IoT devices, allowing for real-time AI applications on low-powered hardware. These models support a context length of 12K tokens and are compatible with hardware from Qualcomm and MediaTek, making them versatile for edge deployments.

  • Vision-Centric Models: The 11B and 90B models introduce vision capabilities to Llama, enabling advanced applications like augmented reality (AR) and complex image recognition.

  • On-Device AI: These models are specifically designed to run efficiently on ARM-based devices, bringing powerful AI capabilities to mobile and edge environments without needing extensive cloud infrastructure.

Applications

  • Multimodal AI Tasks: Llama 3.2’s multimodal capabilities allow it to analyze both text and images, which opens up opportunities in fields like:

  • Document Analysis: Automatically process and extract information from scanned documents.

  • Image Captioning and Object Recognition: Generate descriptions or identify objects within images.

  • Visual Question Answering: Answer questions based on visual inputs, making it a valuable tool for accessibility and automation in various industries.

  • On-Device AI: Due to its lightweight architecture, Llama 3.2 can be deployed on mobile devices or IoT systems for real-time processing, even in environments with limited resources or no internet connection.

  • AR and Vision-Based Applications: Developers can integrate Llama 3.2 into augmented reality systems, where quick image recognition or contextual understanding is essential.

Start Building with Llama 3.2

Getting started with Llama 3.2 is easy, whether you're a seasoned developer or just starting out with AI. Meta provides a comprehensive set of resources, including detailed documentation, setup guides, and tutorials to help you integrate Llama 3.2 into your applications. You can choose from various model sizes depending on your use case, whether it’s running locally on your device or deploying in a large-scale cloud environment. Llama 3.2’s open-source nature allows for customization and fine-tuning for specialized needs.

👉 Start building with Llama 3.2

Meta Llama 3.2 AI technology Hackathon projects

Discover innovative solutions crafted with Meta Llama 3.2 AI technology, developed by our community members during our engaging hackathons.

Synth Dev

Synth Dev

## Problem 1. AI coding assistants (Copilot, Cursor, Aider.chat) accelerate software development. 2. People typically code not by reading documentation but by asking Llama, ChatGPT, Claude, or other LLMs. 3. LLMs struggle to understand documentation as it requires reasoning. 4. New projects or updated documentation often get overshadowed by legacy code. ## Solution - To help LLMs comprehend new documentation, we need to generate a large number of usage examples. ## How we do it 1. Download the documentation from the URL and clean it by removing menus, headers, footers, tables of contents, and other boilerplate. 2. Analyze the documentation to extract main ideas, tools, use cases, and target audiences. 3. Brainstorm relevant use cases. 4. Refine each use case. 5. Conduct a human review of the code. 6. Organize the validated use cases into a dataset or RAG system. ## Tools we used https://github.com/kirilligum/synth-dev - **Restack**: To run, debug, log, and restart all steps of the pipeline. - **TogetherAI**: For LLM API and example usage. See: https://github.com/kirilligum/synth-dev/blob/main/streamlit_fastapi_togetherai_llama/src/functions/function.py - **Llama**: We used Llama 3.2 3b, breaking the pipeline into smaller steps to leverage a more cost-effective model. Scientific research shows that creating more data with smaller models is more efficient than using larger models. See: https://github.com/kirilligum/synth-dev/blob/main/streamlit_fastapi_togetherai_llama/src/functions/function.py - **LlamaIndex**: For LLM calls, prototyping, initial web crawling, and RAG. See: https://github.com/kirilligum/synth-dev/blob/main/streamlit_fastapi_togetherai_llama/src/functions/function.py

AlphaBeam

AlphaBeam

AlphaBeam is a multilingual platform for Conversational Business intelligence, that redefines data interaction by seamlessly helping business users to explore their data without having to rely on their tech teams every time. While traditional BI tools have empowered data exploration through dashboards and reports, they often cater to a specialized audience, requiring technical expertise and leaving out crucial stakeholders. This results in missed insights, delayed decisions, and a limited understanding of their data's true potential. To address this shortcoming in fostering a truly interactive and user-centric experience for non-technical users, AlphaBeam seamlessly blends conversational capabilities with advanced analytics, creating a symbiotic relationship between business users and their data. It also fosters business interaction in local African languages, to capture cultural contexts. At a glance, AlphaBeam is a data-agnostic solution to ingest data from different sources, a semantic layer which translates raw data into business vocabularies and user queries into precise metrics and visualisations, a conversational interface for ad-hoc analysis and AI-powered insights through Llama 3.2 in 50+ African languages, and a visualisation layer which transforms the retrieved data into compelling dashboards. These capabilities empower users to carry out the following: - Conversational Inquiries: Business users can ask questions about already existing dashboards in English or 50+ African languages, just as they would converse with a colleague. They could dig deeper into the data behind the visualisations, gaining a comprehensive understanding of the data. - Comprehensive Metric Exploration: Engage in a conversational dialogue to uncover insights about any metric or data point. - Decision Making at the Speed of Data: Through conversational querying, AlphaBeam empowers users to make informed decisions very quickly based on readily available insights.