Gemma-4-12B-it-assistant

About Model

Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in five distinct sizes: E2B, E4B, 12B, 26B A4B, and 31B. Their diverse sizes make them deployable in environments ranging from high-end phones to laptops and servers, democratizing access to state-of-the-art AI. Gemma 4 introduces key capability and architectural advancements: Reasoning - All models in the family are designed as highly capable reasoners, with configurable thinking modes. Extended Multimodalities - Processes Text, Image with variable aspect ratio and resolution support (all models), Video, and Audio (featured natively on the E2B, E4B, and 12B models). Diverse and Efficient Architectures - Offers Dense and Mixture-of-Experts (MoE) variants of different sizes for scalable deployment. Optimized for On-Device - Smaller models are specifically designed for efficient local execution on laptops and mobile devices. Increased Context Window - The small models feature a 128K context window, while the medium models support 256K. Enhanced Coding and Agentic Capabilities - Achieves notable improvements in coding benchmarks alongside native function-calling support, powering highly capable autonomous agents. Native System Prompt Support - Gemma 4 introduces native support for the system role, enabling more structured and controllable conversations. Gemma 4 models are designed to deliver frontier-level performance at each size, targeting deployment scenarios from mobile and edge devices (E2B, E4B) to consumer GPUs and workstations (12B, 26B A4B, 31B). They are well-suited for reasoning, agentic workflows, coding, and multimodal understanding. The models employ a hybrid attention mechanism that interleaves local sliding window attention with full global attention, ensuring the final layer is always global. This hybrid design delivers the processing speed and low memory footprint of a lightweight model without sacrificing the deep awareness required for complex, long-context tasks. To optimize memory for long contexts, global layers feature unified Keys and Values, and apply Proportional RoPE (p-RoPE).