Case study: Gemini
Definition
Gemini is Google’s Familie von LLMs with native multimodal support: Text, Bild, Audio und Video in einem Modell. Es folgt auf frühere Google models (z. B. BART in the encoder-decoder line) and is offered in multiple scale tiers (Nano, Pro, Ultra) for different latency and capability trade-offs.
Gemini is trained and deployed across Google products (Search, Workspace, Vertex AI, Android). Use case: chat, multimodal understanding and generation, Programmierung, and agent-style tool use.
Funktionsweise
Multimodal inputs (text, image, audio, video) werden kodiert und in einem einheitlichen verschmolzen transformer Stack. The decoder generates text (or structured output) conditioned on all modalities. Scale tiers: kleineres Modells (z. B. Nano) for edge and on-device; larger (Pro, Ultra) for maximum capability in the cloud. Integration: same models power Gemini in Search, Workspace, and Vertex AI APIs. Prompt engineering and RAG or tools extend use in applications.
Anwendungsfälle
Gemini passt, wenn you need multimodal understanding or generation and optional integration with Google’s Stack.
- Chat and assistants with image, document, or video understanding
- Multimodal search, summarization, and content generation
- Coding and Schlussfolgern via API or Google products
Externe Dokumentation
- Google AI – Gemini — API and overview
- Google – Gemini models — Model tiers and capabilities
Siehe auch
- LLMs
- Multimodal AI
- BART — Predecessor in the encoder-decoder line