Gemma 3 - Google's third-generation open-source multimodal model supporting text, images, and short videos.

## Gemma 3 Parameter Sizes Gemma 3 offers four parameter configurations: - 100 million (100M) - 400 million (400M) - 1.2 billion (1.2B) - 2.7 billion (2.7B) These options cater to different computational resource requirements. ## Gemma 3 Multimodal Capabilities - **400M, 1.2B, and 2.7B models**: Support vision-language input (images + text) with text output - **100M model**: Text-only processing The multimodal models can analyze images up to 896x896 pixels using adaptive window algorithms. ## Gemma 3 Context Window Specifications - **400M, 1.2B, and 2.7B models**: 128K tokens - **100M model**: 32K tokens The extended context enables processing of long-form content. ## Gemma 3 Benchmark Performance The Gemma-3-27B-IT model achieved: - 1338 Elo score in LMArena benchmarks (top 10) - Outperformed competitors like Llama-405B and DeepSeek-V3 This demonstrates leading performance on single-accelerator (GPU/TPU) systems. ## Gemma 3 Training Methodology The training process included: 1. **Pretraining**: Using 2-14 trillion tokens (scaling with model size) 2. **Post-training**: - Knowledge distillation - RLHF (Reinforcement Learning from Human Feedback) - RLMF (Reinforcement Learning from Machine Feedback) - RLEF (Reinforcement Learning from Execution Feedback) 3. **Vision encoding**: Frozen SigLIP-based encoder for multimodal models ## Gemma 3 Deployment Options Available through multiple channels: - **Hugging Face**: [Model repository](https://huggingface.co/collections/google/gemma-3-release-67c6c6f89c4f76621268bb6d) - **Google AI Studio**: [Interactive platform](https://aistudio.google.com/prompts/new_chat?model=gemma-3-27b-it) - **Edge devices**: Optimized for mobile deployment (100M model is 529MB) ## Gemma 3 Safety Features Safety implementations include: - ShieldGemma 2 (400M image safety classifier) - CSAM filtering during data preprocessing - Sensitive content filtering - Alignment with Google's AI Responsibility Policy - Output labeling for critical safety categories ## Gemma 3 Use Cases Key application areas: 1. **Text processing**: QA, summarization, reasoning, code generation 2. **Image analysis**: Object recognition, text extraction, image comparison 3. **Chat AI**: Enhanced conversational abilities with structured outputs 4. **Multimodal analysis**: Combined text-image understanding tasks ## Gemma 3 Training Infrastructure Training utilized: - **TPUs**: Mix of TPUv4p, TPUv5p, and TPUv5e accelerators - **Software stack**: JAX framework and ML Pathways - **Sustainability**: Compliant with Google's environmental commitments ## Gemma 3 Version Comparison Key advancements in Gemma 3: - Multimodal capabilities (absent in earlier versions) - Extended 128K context window - Improved mathematical and reasoning performance - Enhanced safety features - Broader language support (140+ languages) The series has seen over 100M downloads and 60K community variants. ### Citation sources: - [Gemma 3](Gemma 3) - Official URL Updated: 2025-04-01