A Visual Guide to Gemma 4

2024-02-19 · Source: Exploring Language Models · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Advanced, extended

Summary

Google DeepMind has released the Gemma 4 family of models, featuring four variants: Gemma 4 - E2B (2 billion effective parameters), Gemma 4 - E4B (4 billion effective parameters), Gemma 4 - 31B (31 billion parameters), and Gemma 4 - 26B A4B (26 billion total parameters with 4 billion active during inference). These models incorporate architectural enhancements over Gemma 3, including interleaved local and global attention layers where global attention is always the final layer, and optimizations like Grouped Query Attention (GQA) with 8 Query heads per KV head, K=V for global attention, and low-frequency-pruned RoPE (p-RoPE) with p=0.25. All Gemma 4 models are multimodal, supporting image inputs via a Vision Transformer (ViT) based encoder with 2D RoPE for variable aspect ratios and adaptive resizing with a soft token budget. The smaller E2B and E4B models additionally support audio inputs through a Conformer-based audio encoder.

Key takeaway

For AI Architects and MLOps Engineers evaluating multimodal LLMs, the Gemma 4 family offers a range of models optimized for diverse deployment scenarios. Consider the 26B A4B for high performance with efficient inference, or the E2B/E4B for on-device applications requiring audio and image processing due to their effective parameter and per-layer embedding designs. Your choice should align with specific hardware limitations and the required multimodal input capabilities.

Key insights

Gemma 4 models offer multimodal capabilities and efficiency through architectural innovations like interleaved attention and specialized encoders.

Principles

• Interleave local and global attention for efficiency and global context.
• Optimize global attention with GQA, K=V, and p-RoPE for memory savings.
• Use 2D RoPE and adaptive resizing for variable image aspect ratios.

Method

Gemma 4 processes images via a ViT encoder with 2D RoPE, adaptive resizing, and spatial pooling to a soft token budget. Audio inputs are handled by a Conformer encoder, converting mel-spectrogram features into contextual embeddings.

In practice

• Select Gemma 4 variants based on hardware constraints and parameter efficiency needs.
• Utilize E2B/E4B for on-device multimodal applications requiring audio processing.
• Adjust image token budgets (70-1120) for resolution-performance trade-offs.

Topics

• Gemma 4 Architecture
• Multimodal AI
• Mixture-of-Experts
• Per-Layer Embeddings
• Attention Mechanisms

Best for: AI Architect, MLOps Engineer, Computer Vision Engineer, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Exploring Language Models.