gemma-4-E4B-it-qat-q4_0-unquantized
is a mid-tier on-device-optimized instruction-tuned multimodal model from Google DeepMind, part of the Gemma 4 family, featuring 4.5 billion effective parameters (8B total including embeddings) optimized via Quantization-Aware Training (QAT) to retain near-bfloat16 quality while substantially lowering memory requirements. Designed for efficient local execution on laptops and capable mobile devices, it leverages Per-Layer Embeddings (PLE) for parameter efficiency and supports text, image, and audio modalities with a 128K token context window and 262K vocabulary across 140+ languages, using a hybrid attention mechanism with 512-token sliding window attention across 42 layers alongside a ~150M parameter vision encoder and ~300M parameter audio encoder. Capabilities include ASR, speech translation (CoVoST score of 35.54), image understanding, OCR, video frame analysis, native function calling, and configurable thinking/reasoning mode, with the E4B stepping up noticeably from the E2B in key benchmarks — scoring 69.4% on MMLU Pro, 58.6% on GPQA Diamond, 42.5% on AIME 2026, 52.0% on LiveCodeBench v6, 52.6% on MMMU Pro (vision), and 25.4% on the 128K long-context MRCR v2 task — making it a strong balance between the lightweight E2B and the larger server-class models, with the Q4_0 unquantized variant providing half-precision weights ideal for custom downstream compilation and research.