gemma-4-26B-A4B-it

任务类型：image-text-to-text

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  <img src=https://ai.google.dev/gemma/images/gemma4_banner.png>
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<p align="center">
    <a href="https://huggingface.co/collections/google/gemma-4" target="_blank">Hugging Face</a> |
    <a href="https://github.com/google-gemma" target="_blank">GitHub</a> |
    <a href="https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/" target="_blank">Launch Blog</a> |
    <a href="https://ai.google.dev/gemma/docs/core" target="_blank">Documentation</a> |
    <a href="https://arxiv.org/abs/2607.02770" target="_blank">Technical Report</a>
    <br>
    <b>License</b>: <a href="https://ai.google.dev/gemma/docs/gemma_4_license" target="_blank">Apache 2.0</a> | <b>Authors</b>: <a href="https://deepmind.google/models/gemma/" target="_blank">Google DeepMind</a>
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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 & 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 & 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.

## **Models Overview**

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). 

### Dense Models

| Property | E2B | E4B | 12B Unified | 31B Dense |
| :---- | :---- | :---- | :---- | :---- |
| **Total Parameters** | 2.3B effective <br> (5.1B with embeddings) | 4.5B effective <br> (8B with embeddings) | 11.95B | 30.7B |
| **Layers** | 35 | 42 | 48 | 60 |
| **Sliding Window** | 512 tokens | 512 tokens | 1024 tokens | 1024 tokens |
| **Context Length** | 128K tokens | 128K tokens | 256K tokens  | 256K tokens  |
| **Vocabulary Size** | 262K | 262K | 262K | 262K 

上游文档较长，此处为节选。完整内容见官方项目。