Gemma 2

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Gemma 2

Gemma 2 is the second-generation open large language model (LLM) from Google, built to advance performance, scalability, and responsible AI development. This model family supports diverse applications, from complex conversational AI to advanced content moderation systems. Based on Google’s leading transformer architecture, Gemma 2 builds on the Gemma foundation, incorporating enhanced safety, accessibility, and multi-platform deployment capabilities.

General
Relese date	February 2024
Author	Google DeepMind in collaboration with Google AI teams
Website	Google AI Gemma
Repository	Google AI Developer Resources
Type	Advanced open-source AI, large language model

Key Features:

Enhanced Parameter Options: Gemma 2 is available in configurations up to 27 billion parameters (2B, 9B, and 27B) for handling large-scale, complex language tasks.
ShieldGemma Safety Classifiers: ShieldGemma, a suite of safety-focused classifiers, helps detect and mitigate harmful content, addressing issues like hate speech, harassment, and explicit material.
Efficient Transformer Architecture: Supports processing up to 8192 tokens in a single pass, enhancing performance in long-form text processing and providing nuanced output across complex language tasks.
Flexible, Scalable Deployment: Optimized for both edge devices and cloud infrastructure, making it suitable for local, distributed, and high-demand deployment environments.
Integration with Leading AI Platforms: Works seamlessly with Google Cloud’s Vertex AI, Keras, JAX, and PyTorch, providing adaptable and efficient customization options for developers.

Applications:

Enterprise AI: Suitable for high-scale, complex applications such as automated data analysis, market predictions, and large-scale content creation.
Content Moderation Systems: The ShieldGemma classifiers filter harmful content, making it a good choice for moderation in social media, online communities, and customer service settings.
Multilingual Applications: With enhanced token processing, Gemma 2 is optimal for tasks requiring intricate language understanding and cross-language generation, ideal for global customer support and translation tools.
Conversational AI and Chatbots: Instruction-tuned models within Gemma 2 make it highly effective for advanced conversational systems, chatbots, and interactive voice assistants.

Get started building with Gemma 2:

You can start developing with Gemma 2 by accessing model weights from Google AI Studio and Kaggle. With integrated ShieldGemma safety tools, the model is prepped for responsible and large-scale deployments. The model’s compatibility with Google Cloud, Vertex AI, and popular AI frameworks makes it easy to customize for both edge and cloud solutions. Explore the full suite of resources on Google AI Gemma to unlock the potential of Gemma 2 and build robust, responsible AI applications.

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Google Gemma 2 AI technology Hackathon projects

Discover innovative solutions crafted with Google Gemma 2 AI technology, developed by our community members during our engaging hackathons.

CareSight AI - Autonomous Healthcare Assistant

CareSight AI is an autonomous, multimodal healthcare assistant designed to reduce clinical paperwork and improve care readiness across the patient journey. The system uses computer vision, OCR, speech recognition, and LLM reasoning (Gemma 3N) to perceive and structure unorganized healthcare data such as documents, images, and spoken responses. Patients begin with a mobile web app to upload IDs, insurance cards, lab reports, and clinical images, or provide voice responses. CareSight AI extracts structured data, validates completeness, and prepares an intake summary. During nurse check-in, the system assists by auto-filling forms, flagging inconsistencies, and supporting preliminary screenings such as cognitive memory tests by recording sessions, transcribing responses, and automatically scoring recall accuracy. Doctors receive a concise, explainable summary highlighting key findings and uncertainties. CareSight AI functions as an autonomous agent following a perception–reasoning–action loop, supporting clinicians without replacing them and enabling scalable, remote-ready healthcare workflows.

FraudLens AI Security Platform

FraudLens AI is an AI-powered security platform designed to address two rapidly converging threats in modern digital trading environments: behavioural fraud and expanding technical attack surfaces. As AI-generated phishing and highly personalised scams increase, attackers increasingly exploit user trust rather than purely technical vulnerabilities. The platform combines real-time fraud detection with an AI-driven attack surface pentester. FraudLens AI analyses suspicious emails, chat transcripts, documents, and AI-generated content to detect behavioural manipulation, social engineering, and fraud indicators. At the same time, the AI Pentester module simulates active penetration testing by reasoning about domains and URLs, modelling attacker behaviour, identifying potential attack vectors, and prioritising risks without performing real exploitation. Built using Groq’s high-speed AI inference, the system delivers low-latency analysis, clear risk scoring, and human-readable security reports. FraudLens AI helps organisations reduce fraud, improve security visibility, and scale protection across both human and technical layers in a continuously evolving threat landscape.

RoboGripAI

This project presents a simulation-first robotic system designed to perform structured physical tasks through reliable interaction with objects and its environment. The system focuses on practical task execution rather than complex physics modeling, ensuring repeatability, robustness, and measurable performance across varied simulated conditions. Simulation-first robotic system performing structured physical tasks such as pick-and-place, sorting, and simple assembly. Designed for repeatable execution under varied conditions, with basic failure handling, environmental interaction, and measurable performance metrics. A key emphasis of the system is reliability under dynamic conditions. The simulation introduces variations such as object position changes, minor environmental disturbances, and task sequence modifications. The robot is designed to adapt to these variations while maintaining consistent task success rates. Basic failure handling mechanisms are implemented, including reattempt strategies for failed grasps, collision avoidance corrections, and task state recovery protocols. The framework incorporates structured task sequencing and state-based control logic to ensure deterministic and repeatable behavior. Performance is evaluated using clear metrics such as task completion rate, execution time, grasp accuracy, recovery success rate, and system stability across multiple trials. The modular system design allows scalability for additional tasks or integration with advanced planning algorithms. By prioritizing repeatability, robustness, and measurable outcomes, this solution demonstrates practical robotic task automation in a controlled simulated environment, aligning with real-world industrial and research use cases. Overall, the project showcases a dependable robotic manipulation framework that bridges perception, decision-making, and action in a simulation-first setting, delivering consistent and benchmark-driven task execution.

FreshCredit Arc UCP Integration

FreshCredit Arc UCP Integration demonstrates a next-generation commerce and credit infrastructure where users retain control over their financial data while intelligent agents handle discovery, verification, and payment execution. The project combines Circle’s Arc USDC payment rails with Google’s Universal Commerce Protocol (UCP) to enable agent-based interactions between consumers and providers. Users authenticate via decentralized and enterprise-grade identity systems, maintain portable, user-owned financial reports at the edge, and authorize transactions only when value is exchanged. Instead of monetizing report access, FreshCredit aligns incentives by charging only on completed transactions. UCP enables standardized offer discovery and intent matching, while Arc enables instant USDC settlement across wallets and networks. Together, they form a privacy-preserving, interoperable alternative to legacy credit bureaus, optimized for AI agents, automation, and global commerce.

Oryn - AI Debt Collection on Arc

The Problem Freelancers and small businesses lose billions annually to unpaid invoices. Chasing payments is awkward, time-consuming, and often unsuccessful. The Solution Oryn is an autonomous AI agent that handles the entire debt collection process without manual intervention. How It Works 1. Upload an invoice or contract. Oryn's AI extracts client details, amounts, and due dates automatically. 2. Activate the claim. The agent sends friendly reminders via email, SMS, or WhatsApp. 3. Smart escalation. If clients don't respond, Oryn automatically adjusts tone from friendly to firm to final notice. 4. Payment on Arc. Each claim generates a USDC payment link on Circle's Arc blockchain. Sub-second finality, fees paid in USDC, no gas token complexity. 5. Real-time dashboard. Track total collected, active claims, and payment history in one place. Why Arc? Circle's new L1 blockchain enables instant, low-cost USDC transfers, making micropayments viable and cross-border collections seamless. No waiting days for bank transfers. True Agentic Commerce The AI maintains context across days and weeks, remembering every interaction and adjusting its approach based on client responses. It works autonomously toward a goal. Watch the Demo Visit oryn.cc and click "Watch Demo" or view here: https://www.canva.com/design/DAG_SFV8WHw/ORTSg3XoP7hvQOHo2AeYSA/watch

T-gain

T-Gain is an AI-powered tax and business compliance assistant that helps freelancers and small businesses understand their finances, get accurate business structure and taxrecommendations, and work seamlessly with verified tax experts. The system analyses profit and expense data, generates clear insights, and triggers automated workflows for audits, corrections, expert review, and final reporting. Tax experts receive structured data, run automated audits, make corrections, verify AI recommendations, and approve final reports before delivery. T-Gain combines intelligent automation with human oversight to deliver fast, reliable tax guidance, saving time and reducing errors for both business owners and professionals.