gemma3-vl.md

Gemma 3 VL (Vision-Language)

Google's Gemma 3 VL is a family of vision-language models built on the same research and technology used to create Gemini models. The Gemma 3 VL architecture combines the text-generation capabilities of Gemma 3 with a SigLIP vision encoder for robust visual understanding.

Gemma 3 VL models support multimodal tasks including image captioning, visual question answering, OCR, and general vision-language understanding.

Gemma family models are supported via the Bridge system with auto-detected configuration and weight mapping.

Available Models

Vision-Language Models

• Gemma 3 VL 4B (google/gemma-3-4b-it): 4B parameter vision-language model

  • 34 layers, 2560 hidden size
  • 16 attention heads, 4 query groups (GQA)
  • Vision encoder: SigLIP with 729M parameters
  • Recommended: 1 node, 8 GPUs

• Gemma 3 VL 12B (google/gemma-3-12b-it): 12B parameter vision-language model

  • 48 layers, 3840 hidden size
  • 24 attention heads, 8 query groups (GQA)
  • Vision encoder: SigLIP with 729M parameters
  • Recommended: 1 node, 8 GPUs

• Gemma 3 VL 27B (google/gemma-3-27b-it): 27B parameter vision-language model

  • 62 layers, 5376 hidden size
  • 32 attention heads, 16 query groups (GQA)
  • Vision encoder: SigLIP with 729M parameters
  • Recommended: 2 nodes, 16 GPUs

All models support a sequence length of 131,072 tokens and use hybrid attention patterns (sliding window + global).

Model Architecture Features

Gemma 3 VL builds on the Gemma 3 architecture with additional multimodal capabilities:

Language Model Features:

• Hybrid Attention Pattern: Alternates between global and local sliding window attention for efficient long-context processing
• GeGLU Activation: Uses gated linear units with GELU activation for improved performance
• RMSNorm: Layer normalization without mean centering for faster computation
• Rotary Embeddings: Separate RoPE configurations for local and global attention layers

Vision-Language Features:

• SigLIP Vision Encoder: Pre-trained vision encoder with 729M parameters for robust visual understanding
• Multimodal Integration: Seamless integration of visual and textual information through learned projection layers
• Flexible Image Handling: Supports variable resolution images and multiple images per conversation

Examples

For checkpoint conversion, inference, finetuning recipes, and step-by-step training guides, see the Gemma 3 VL Examples.

Hugging Face Model Cards

• Gemma 3 VL 4B: https://huggingface.co/google/gemma-3-4b-it
• Gemma 3 VL 12B: https://huggingface.co/google/gemma-3-12b-it
• Gemma 3 VL 27B: https://huggingface.co/google/gemma-3-27b-it

Related Docs

• Related Gemma variant: Gemma 3
• Recipe usage: Recipe usage
• Customizing the training recipe configuration: Configuration overview
• Training entry points: Entry points