Gemma 4 E2B IT
Gemma 4 E2B is the edge-sized member of Google DeepMind's Gemma 4 family: 2.3B effective parameters (5.1B including embeddings), native text and image input, a 128K-token context window, configurable thinking and function calling, released as open weights under Apache 2.0.
Model
Gemma 4 E2B is a 35-layer dense transformer built for phones, edge devices and laptops. The E stands for effective parameters: Per-Layer Embeddings give every decoder layer its own small per-token embedding table, so the model has 5.1B parameters in total but only about 2.3B are active in compute. A ~150M-parameter vision encoder handles images at variable aspect ratios and resolutions with a configurable visual token budget. Hybrid local sliding-window and global attention with Proportional RoPE supports a 128K-token context, alongside a switchable thinking mode, native function calling and a system role.
This is the instruction-tuned variant, released as open weights under the Apache 2.0 license. Google recommends sampling at temperature 1.0, top-p 0.95 and top-k 64, placing images before the text in a prompt, and enabling thinking by prefixing the system prompt with the think control token. Full details are in Google's announcement and the model card.
Dedicated Endpoint
DocsA Dedicated Endpoint is your own deployment of Gemma 4 E2B IT, on an inference stack that Modal tunes and autoscales. Bring fine-tuned weights if you have them. Compute is billed by the second, and only while it runs.
modal endpoint create --model google/gemma-4-E2B-it