Case study: Gemini
Définition
Gemini is Google’s famille de LLMs with native multimodal support: text, image, audio, and video in one model. It succeeds earlier Google models (par ex. 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, coding, and agent-style tool use.
Comment ça fonctionne
Les entrées multimodales (texte, image, audio, vidéo) sont encodées et fusionnées dans un transformer unifiétack. The decoder generates text (or structured output) conditioned on all modalities. Scale tiers: smaller models (par ex. 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.
Cas d'utilisation
Gemini fits when 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 raisonnement via API or Google products
Documentation externe
- Google AI – Gemini — API and overview
- Google – Gemini models — Model tiers and capabilities
Voir aussi
- LLMs
- Multimodal AI
- BART — Predecessor in the encoder-decoder line