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Gemma

Gemma is a lightweight, open large language model (LLM) from Google, optimized for efficient AI applications. As part of the Google Gemma family, it uses a transformer-based architecture tailored for responsible and accessible AI usage. Developed as a foundational model, Gemma serves various basic language processing needs, including chatbots, content summarization, and multilingual support.

General
Relese dateFebruary 2024​
AuthorGoogle DeepMind in collaboration with Google AI teams
Website[Google AI Gemma]https://ai.google.dev/gemma
RepositoryGoogle AI Developer Resources​
TypeOpen-source AI, transformer-based LLM

Key Features

  • Efficient Deployment: Available in parameter sizes like 2.5B and 7B, Gemma balances capability with efficiency, enabling deployments on both edge devices and cloud infrastructure​.

  • Flexible Tuning Options: Offers pre-trained and instruction-tuned variants, allowing developers to optimize for specific use cases or deploy as-is.

  • Decoder-Only Transformer Architecture: Uses a streamlined decoder-only design, enabling Gemma 1 to process up to 8192 tokens in one pass for better handling of long-form text​.

  • Safety and Accessibility Tools: Integrates responsible AI features, promoting transparency and safety in AI outputs​.

Applications:

  • Chatbot Development: Optimized for conversational tasks, Gemma provides foundational capabilities for chatbot applications.

  • Summarization and Paraphrasing: Its pre-trained model structure makes it suitable for summarizing content across languages and contexts.

  • Multilingual Processing: Supports multilingual inputs, making it adaptable for global applications and translation services​.

Get started building with Gemma:

Developers can quickly integrate Gemma into applications by accessing its model weights on Google AI Studio and Kaggle. The model’s lightweight design ensures that it can run efficiently on most hardware configurations, including mobile and edge devices. For optimal performance, utilize frameworks such as Keras or JAX to customize and deploy Gemma for your specific use case. Get started today by exploring the tools and resources available on the Google AI Gemma platform​.

Google Gemma AI technology Hackathon projects

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

 ModelMarket on Arc

ModelMarket on Arc

ModelMarket on Arc solves a single hard problem for the agentic economy: per-call billing for AI models has been economically impossible. On Ethereum mainnet, a single USDC transfer costs ~$2.40 in gas. Charging $0.001 per call loses $2.399. Even L2s leave cents of overhead. Result: every API stays on monthly billing, and AI agents can never pay each other in real time. Arc changes that. USDC is the native gas token. Gas per transfer: ~$0.0001. A $0.001 call now nets $0.0009 — 90% margin. Per-call pricing finally clears. ModelMarket is the proof-of-concept marketplace. Six sellers list inference at $0.0005–$0.008 per call: Google Gemini 2.5 Flash and Gemini 2.5 Pro (cloud frontier models), Gemma 2 2B and Llama 3.2 1B running locally via Ollama (anyone with a laptop becomes a seller), Llama 3.3 70B via HuggingFace Fireworks (open-weights premium), and a deterministic NL→Shell endpoint. A buyer agent fires 60+ inference calls across all six in under 15 seconds. Each is a real HTTP 402 → EIP-3009 sign → on-chain USDC settlement → 200 response loop. The dashboard shows live earnings per seller, updating every second. We integrated Circle Nanopayments (x402 protocol), Arc testnet, USDC, Circle Wallets (developer-controlled), and Circle Developer Console. The Gemma local seller proves the bigger vision: the agentic economy is not just frontier APIs charging agents — it's anyone monetizing local compute, paid in USDC, settled on Arc. Stripe-shaped DX: drop-in middleware on the seller, drop-in axios interceptor on the buyer. Arc is the only chain where this clears. ModelMarket is the proof.