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LatestGoogle DeepMind4-E2B-it
Google DeepMindAny-to-Any

Google Releases 2B Multimodal Gemma 4 Assistant Model

The new compact model from DeepMind is instruction-tuned for "any-to-any" tasks, capable of processing and generating mixed data types.

Apr 23, 2026
UpdateApache 2.0
Gemma 4 E2B-it Assistant

Google DeepMind has released a new addition to its open-source Gemma family: a 2-billion-parameter model designed for multimodal assistant tasks. Dubbed "Gemma 4 E2B-it Assistant," the model is notably compact, aiming to bring sophisticated capabilities to a wider range of hardware.

This release is an instruction-tuned variant, meaning it's been specifically fine-tuned to follow user commands and engage in conversational interactions. Its key feature is its "any-to-any" architecture, which allows it to process and generate a mix of data types beyond just text—a significant capability for a model of its size.

Compact Multimodality

The model's combination of a small parameter count and advanced multimodal features makes it particularly interesting. While larger models have long handled mixed inputs, a capable 2B model opens up new possibilities for developers building applications for edge devices, specialized agents, or scenarios where computational resources are constrained.

The Gemma 4 E2B-it Assistant is licensed under the permissive Apache 2.0 license, encouraging both research and commercial use. Developers can explore the model and its capabilities now, as it is available on Hugging Face.

Sources

  • google/gemma-4-E2B-it-assistant

    Hugging Face

    Visit
OlderXiaomi Releases MiMo Model for Speech RecognitionXiaomi · Speech → Text · 6 months agoNewerGoogle Releases 4B Multimodal Gemma 4 AssistantGoogle DeepMind · Any-to-Any · 6 months ago

Get the model

Hugging Face

Specs

Parameters2B
Context window131K tokens
PrecisionBF16
ArchitectureGemma4AssistantForCausalLM
LicenseAPACHE-2.0
Downloads48.8K
Likes83

Can you run it?

Runs on just about anything.

  • BF16 (as published)

    8 GB GPU (RTX 4060) · 16 GB laptop or Mac

    5 GB
  • 8-bit

    Any modern laptop · 8 GB RAM

    3.2 GB
  • 4-bit

    Any modern laptop · 8 GB RAM

    2.2 GB
Your machine
GGUF builds
  • Parameter count is an editorial estimate — no weights metadata on Hugging Face.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Estimated from the parameter count. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Any-to-AnyText / LLM

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