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eecebe7ef5890dcb6fa83d62740d7dbcb43ff737
gemma4-research/tooling/google-official/docs/ai-google-dev_model_card_4.html
T
Mortdecai eecebe7ef5 docs: add canonical tooling corpus (147 files) from Google/HF/frameworks 
Five-lane parallel research pass. Each subdir under tooling/ has its own
README indexing downloaded files with verified upstream sources.

- google-official/: deepmind-gemma JAX examples, gemma_pytorch scripts,
  gemma.cpp API server docs, google-gemma/cookbook notebooks, ai.google.dev
  HTML snapshots, Gemma 3 tech report
- huggingface/: 8 gemma-4-* model cards, chat-template .jinja files,
  tokenizer_config.json, transformers gemma4/ source, launch blog posts,
  official HF Spaces app.py
- inference-frameworks/: vLLM/llama.cpp/MLX/Keras-hub/TGI/Gemini API/Vertex AI
  comparison, run_commands.sh with 8 working launches, 9 code snippets
- gemma-family/: 12 per-variant briefs (ShieldGemma 2, CodeGemma, PaliGemma 2,
  Recurrent/Data/Med/TxGemma, Embedding/Translate/Function/Dolphin/SignGemma)
- fine-tuning/: Unsloth Gemma 4 notebooks, Axolotl YAMLs (incl 26B-A4B MoE),
  TRL scripts, Google cookbook fine-tune notebooks, recipe-recommendation.md

Findings that update earlier CORPUS_* docs are flagged in tooling/README.md
(not applied) — notably the new <|turn>/<turn|> prompt format, gemma_pytorch
abandonment, gemma.cpp Gemini-API server, transformers AutoModelForMultimodalLM,
FA2 head_dim=512 break, 26B-A4B MoE quantization rules, no Gemma 4 tech
report PDF yet, no Gemma-4-generation specialized siblings yet.

Pre-commit secrets hook bypassed per user authorization — flagged "secrets"
are base64 notebook cell outputs and example Ed25519 keys in the HDP
agentic-security demo, not real credentials.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-18 12:24:48 -04:00

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<section id="gc-wrapper">
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has-book-nav
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<devsite-content>
<article class="devsite-article"><style>
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remove link to webpage since we're already here */
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devsite-nav-buttons button {
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code {
border-radius: 6px
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border-top: 0;
padding-bottom: 0.9rem;
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/*
* TODO(b/439059414): Remove this workaround in favor of a project-level
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/*
* Gemini API body class.
* https://source.corp.google.com/piper///depot/google3/third_party/devsite/googledevai/en/gemini-api/_project.yaml;l=7
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/*
* Shaded table styles look like a .pricing-table but are more flexible around
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<h1 class="devsite-page-title" tabindex="-1">
Gemma 4 model card<devsite-actions hidden data-nosnippet>
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<p>
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<p><img src="/static/gemma/images/gemma4_banner.png" alt="Gemma 4 Banner"></p>
<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>
<br>
<b>License</b>: <a href="https://ai.google.dev/gemma/apache_2" target="_blank">Apache 2.0</a> | <b>Authors</b>: <a href="https://deepmind.google/models/gemma/" target="_blank">Google DeepMind</a>
</p>
<p>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 small models)
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.</p>
<p>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 four distinct sizes: <strong>E2B</strong>, <strong>E4B</strong>, <strong>26B A4B</strong>, and
<strong>31B</strong>. 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.</p>
<p>Gemma 4 introduces key <strong>capability and architectural advancements</strong>:</p>
<ul>
<li><p><strong>Reasoning</strong> – All models in the family are designed as highly capable
reasoners, with configurable thinking modes.</p></li>
<li><p><strong>Extended Multimodalities</strong> – Processes Text, Image with variable aspect
ratio and resolution support (all models), Video, and Audio (featured
natively on the E2B and E4B models).</p></li>
<li><p><strong>Diverse &amp; Efficient Architectures</strong> – Offers Dense and Mixture-of-Experts
(MoE) variants of different sizes for scalable deployment.</p></li>
<li><p><strong>Optimized for On-Device</strong> – Smaller models are specifically designed for
efficient local execution on laptops and mobile devices.</p></li>
<li><p><strong>Increased Context Window</strong> – The small models feature a 128K context
window, while the medium models support 256K.</p></li>
<li><p><strong>Enhanced Coding &amp; Agentic Capabilities</strong> – Achieves notable improvements
in coding benchmarks alongside native function-calling support, powering
highly capable autonomous agents.</p></li>
<li><p><strong>Native System Prompt Support</strong> – Gemma 4 introduces native support for the
<code translate="no" dir="ltr">system</code> role, enabling more structured and controllable conversations.</p></li>
</ul>
<h2 id="models_overview" data-text="Models Overview" tabindex="-1"><strong>Models Overview</strong></h2>
<p>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 (26B A4B, 31B). They are well-suited for
reasoning, agentic workflows, coding, and multimodal understanding.</p>
<p>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).</p>
<h3 id="dense_models" data-text="Dense Models" tabindex="-1">Dense Models</h3>
<table>
<thead>
<tr>
<th style="text-align: left">Property</th>
<th style="text-align: left">E2B</th>
<th style="text-align: left">E4B</th>
<th style="text-align: left">31B Dense</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left"><strong>Total Parameters</strong></td>
<td style="text-align: left">2.3B effective (5.1B with embeddings)</td>
<td style="text-align: left">4.5B effective (8B with embeddings)</td>
<td style="text-align: left">30.7B</td>
</tr>
<tr>
<td style="text-align: left"><strong>Layers</strong></td>
<td style="text-align: left">35</td>
<td style="text-align: left">42</td>
<td style="text-align: left">60</td>
</tr>
<tr>
<td style="text-align: left"><strong>Sliding Window</strong></td>
<td style="text-align: left">512 tokens</td>
<td style="text-align: left">512 tokens</td>
<td style="text-align: left">1024 tokens</td>
</tr>
<tr>
<td style="text-align: left"><strong>Context Length</strong></td>
<td style="text-align: left">128K tokens</td>
<td style="text-align: left">128K tokens</td>
<td style="text-align: left">256K tokens</td>
</tr>
<tr>
<td style="text-align: left"><strong>Vocabulary Size</strong></td>
<td style="text-align: left">262K</td>
<td style="text-align: left">262K</td>
<td style="text-align: left">262K</td>
</tr>
<tr>
<td style="text-align: left"><strong>Supported Modalities</strong></td>
<td style="text-align: left">Text, Image, Audio</td>
<td style="text-align: left">Text, Image, Audio</td>
<td style="text-align: left">Text, Image</td>
</tr>
<tr>
<td style="text-align: left"><strong>Vision Encoder Parameters</strong></td>
<td style="text-align: left"><em>~150M</em></td>
<td style="text-align: left"><em>~150M</em></td>
<td style="text-align: left"><em>~550M</em></td>
</tr>
<tr>
<td style="text-align: left"><strong>Audio Encoder Parameters</strong></td>
<td style="text-align: left"><em>~300M</em></td>
<td style="text-align: left"><em>~300M</em></td>
<td style="text-align: left">No Audio</td>
</tr>
</tbody>
</table>
<p>The &quot;E&quot; in E2B and E4B stands for &quot;effective&quot; parameters. The smaller models
incorporate Per-Layer Embeddings (PLE) to maximize parameter efficiency in
on-device deployments. Rather than adding more layers or parameters to the
model, PLE gives each decoder layer its own small embedding for every token.
These embedding tables are large but are only used for quick lookups, which is
why the effective parameter count is much smaller than the total.</p>
<h3 id="mixture-of-experts_moe_model" data-text="Mixture-of-Experts (MoE) Model" tabindex="-1">Mixture-of-Experts (MoE) Model</h3>
<table>
<thead>
<tr>
<th style="text-align: left">Property</th>
<th style="text-align: left">26B A4B MoE</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left"><strong>Total Parameters</strong></td>
<td style="text-align: left">25.2B</td>
</tr>
<tr>
<td style="text-align: left"><strong>Active Parameters</strong></td>
<td style="text-align: left">3.8B</td>
</tr>
<tr>
<td style="text-align: left"><strong>Layers</strong></td>
<td style="text-align: left">30</td>
</tr>
<tr>
<td style="text-align: left"><strong>Sliding Window</strong></td>
<td style="text-align: left">1024 tokens</td>
</tr>
<tr>
<td style="text-align: left"><strong>Context Length</strong></td>
<td style="text-align: left">256K tokens</td>
</tr>
<tr>
<td style="text-align: left"><strong>Vocabulary Size</strong></td>
<td style="text-align: left">262K</td>
</tr>
<tr>
<td style="text-align: left"><strong>Expert Count</strong></td>
<td style="text-align: left">8 active / 128 total and 1 shared</td>
</tr>
<tr>
<td style="text-align: left"><strong>Supported Modalities</strong></td>
<td style="text-align: left">Text, Image</td>
</tr>
<tr>
<td style="text-align: left"><strong>Vision Encoder Parameters</strong></td>
<td style="text-align: left"><em>~550M</em></td>
</tr>
</tbody>
</table>
<p>The &quot;A&quot; in 26B A4B stands for &quot;active parameters&quot; in contrast to the total
number of parameters the model contains. By only activating a 4B subset of
parameters during inference, the Mixture-of-Experts model runs much faster than
its 26B total might suggest. This makes it an excellent choice for fast
inference compared to the dense 31B model since it runs almost as fast as a
4B-parameter model.</p>
<h2 id="benchmark_results" data-text="Benchmark Results" tabindex="-1"><strong>Benchmark Results</strong></h2>
<p>These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation. Evaluation results marked
in the table are for instruction-tuned models.</p>
<table>
<thead>
<tr>
<th style="text-align: left"></th>
<th style="text-align: left">Gemma 4 31B</th>
<th style="text-align: left">Gemma 4 26B A4B</th>
<th style="text-align: left">Gemma 4 E4B</th>
<th style="text-align: left">Gemma 4 E2B</th>
<th style="text-align: left">Gemma 3 27B (no think)</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left">MMLU Pro</td>
<td style="text-align: left">85.2%</td>
<td style="text-align: left">82.6%</td>
<td style="text-align: left">69.4%</td>
<td style="text-align: left">60.0%</td>
<td style="text-align: left">67.6%</td>
</tr>
<tr>
<td style="text-align: left">AIME 2026 no tools</td>
<td style="text-align: left">89.2%</td>
<td style="text-align: left">88.3%</td>
<td style="text-align: left">42.5%</td>
<td style="text-align: left">37.5%</td>
<td style="text-align: left">20.8%</td>
</tr>
<tr>
<td style="text-align: left">LiveCodeBench v6</td>
<td style="text-align: left">80.0%</td>
<td style="text-align: left">77.1%</td>
<td style="text-align: left">52.0%</td>
<td style="text-align: left">44.0%</td>
<td style="text-align: left">29.1%</td>
</tr>
<tr>
<td style="text-align: left">Codeforces ELO</td>
<td style="text-align: left">2150</td>
<td style="text-align: left">1718</td>
<td style="text-align: left">940</td>
<td style="text-align: left">633</td>
<td style="text-align: left">110</td>
</tr>
<tr>
<td style="text-align: left">GPQA Diamond</td>
<td style="text-align: left">84.3%</td>
<td style="text-align: left">82.3%</td>
<td style="text-align: left">58.6%</td>
<td style="text-align: left">43.4%</td>
<td style="text-align: left">42.4%</td>
</tr>
<tr>
<td style="text-align: left">Tau2 (average over 3)</td>
<td style="text-align: left">76.9%</td>
<td style="text-align: left">68.2%</td>
<td style="text-align: left">42.2%</td>
<td style="text-align: left">24.5%</td>
<td style="text-align: left">16.2%</td>
</tr>
<tr>
<td style="text-align: left">HLE no tools</td>
<td style="text-align: left">19.5%</td>
<td style="text-align: left">8.7%</td>
<td style="text-align: left">-</td>
<td style="text-align: left">-</td>
<td style="text-align: left">-</td>
</tr>
<tr>
<td style="text-align: left">HLE with search</td>
<td style="text-align: left">26.5%</td>
<td style="text-align: left">17.2%</td>
<td style="text-align: left">-</td>
<td style="text-align: left">-</td>
<td style="text-align: left">-</td>
</tr>
<tr>
<td style="text-align: left">BigBench Extra Hard</td>
<td style="text-align: left">74.4%</td>
<td style="text-align: left">64.8%</td>
<td style="text-align: left">33.1%</td>
<td style="text-align: left">21.9%</td>
<td style="text-align: left">19.3%</td>
</tr>
<tr>
<td style="text-align: left">MMMLU</td>
<td style="text-align: left">88.4%</td>
<td style="text-align: left">86.3%</td>
<td style="text-align: left">76.6%</td>
<td style="text-align: left">67.4%</td>
<td style="text-align: left">70.7%</td>
</tr>
<tr>
<td style="text-align: left"><strong>Vision</strong></td>
<td style="text-align: left"></td>
<td style="text-align: left"></td>
<td style="text-align: left"></td>
<td style="text-align: left"></td>
<td style="text-align: left"></td>
</tr>
<tr>
<td style="text-align: left">MMMU Pro</td>
<td style="text-align: left">76.9%</td>
<td style="text-align: left">73.8%</td>
<td style="text-align: left">52.6%</td>
<td style="text-align: left">44.2%</td>
<td style="text-align: left">49.7%</td>
</tr>
<tr>
<td style="text-align: left">OmniDocBench 1.5 (average edit distance, lower is better)</td>
<td style="text-align: left">0.131</td>
<td style="text-align: left">0.149</td>
<td style="text-align: left">0.181</td>
<td style="text-align: left">0.290</td>
<td style="text-align: left">0.365</td>
</tr>
<tr>
<td style="text-align: left">MATH-Vision</td>
<td style="text-align: left">85.6%</td>
<td style="text-align: left">82.4%</td>
<td style="text-align: left">59.5%</td>
<td style="text-align: left">52.4%</td>
<td style="text-align: left">46.0%</td>
</tr>
<tr>
<td style="text-align: left">MedXPertQA MM</td>
<td style="text-align: left">61.3%</td>
<td style="text-align: left">58.1%</td>
<td style="text-align: left">28.7%</td>
<td style="text-align: left">23.5%</td>
<td style="text-align: left">-</td>
</tr>
<tr>
<td style="text-align: left"><strong>Audio</strong></td>
<td style="text-align: left"></td>
<td style="text-align: left"></td>
<td style="text-align: left"></td>
<td style="text-align: left"></td>
<td style="text-align: left"></td>
</tr>
<tr>
<td style="text-align: left">CoVoST</td>
<td style="text-align: left">-</td>
<td style="text-align: left">-</td>
<td style="text-align: left">35.54</td>
<td style="text-align: left">33.47</td>
<td style="text-align: left">-</td>
</tr>
<tr>
<td style="text-align: left">FLEURS (lower is better)</td>
<td style="text-align: left">-</td>
<td style="text-align: left">-</td>
<td style="text-align: left">0.08</td>
<td style="text-align: left">0.09</td>
<td style="text-align: left">-</td>
</tr>
<tr>
<td style="text-align: left"><strong>Long Context</strong></td>
<td style="text-align: left"></td>
<td style="text-align: left"></td>
<td style="text-align: left"></td>
<td style="text-align: left"></td>
<td style="text-align: left"></td>
</tr>
<tr>
<td style="text-align: left">MRCR v2 8 needle 128k (average)</td>
<td style="text-align: left">66.4%</td>
<td style="text-align: left">44.1%</td>
<td style="text-align: left">25.4%</td>
<td style="text-align: left">19.1%</td>
<td style="text-align: left">13.5%</td>
</tr>
</tbody>
</table>
<h2 id="core_capabilities" data-text="Core Capabilities" tabindex="-1"><strong>Core Capabilities</strong></h2>
<p>Gemma 4 models handle a broad range of tasks across text, vision, and audio. Key
capabilities include:</p>
<ul>
<li><strong>Thinking</strong> – Built-in reasoning mode that lets the model think
step-by-step before answering.</li>
<li><strong>Long Context</strong> – Context windows of up to 128K tokens (E2B/E4B) and 256K
tokens (26B A4B/31B).</li>
<li><strong>Image Understanding</strong> – Object detection, Document/PDF parsing, screen and
UI understanding, chart comprehension, OCR (including multilingual),
handwriting recognition, and pointing. Images can be processed at variable
aspect ratios and resolutions.</li>
<li><strong>Video Understanding</strong> – Analyze video by processing sequences of frames.</li>
<li><strong>Interleaved Multimodal Input</strong> – Freely mix text and images in any order
within a single prompt.</li>
<li><strong>Function Calling</strong> – Native support for structured tool use, enabling
agentic workflows.</li>
<li><strong>Coding</strong> – Code generation, completion, and correction.</li>
<li><strong>Multilingual</strong> – Out-of-the-box support for 35+ languages, pre-trained on
140+ languages.</li>
<li><strong>Audio</strong> (E2B and E4B only) – Automatic speech recognition (ASR) and
speech-to-translated-text translation across multiple languages.</li>
</ul>
<h2 id="best_practices" data-text="Best Practices" tabindex="-1"><strong>Best Practices</strong></h2>
<p>For the best performance, use these configurations and best practices:</p>
<h3 id="1_sampling_parameters" data-text="1. Sampling Parameters" tabindex="-1">1. Sampling Parameters</h3>
<p>Use the following standardized sampling configuration across all use cases:</p>
<ul>
<li><code translate="no" dir="ltr">temperature=1.0</code></li>
<li><code translate="no" dir="ltr">top_p=0.95</code></li>
<li><code translate="no" dir="ltr">top_k=64</code></li>
</ul>
<h3 id="2_thinking_mode_configuration" data-text="2. Thinking Mode Configuration" tabindex="-1">2. Thinking Mode Configuration</h3>
<p>Compared to Gemma 3, the models use standard <code translate="no" dir="ltr">system</code>, <code translate="no" dir="ltr">assistant</code>, and <code translate="no" dir="ltr">user</code>
roles. To properly manage the thinking process, use the following control
tokens:</p>
<ul>
<li><strong>Trigger Thinking:</strong> Thinking is enabled by including the <code translate="no" dir="ltr">&lt;|think|&gt;</code> token
at the start of the system prompt. To disable thinking, remove the token.</li>
<li><strong>Standard Generation:</strong> When thinking is enabled, the model will output its
internal reasoning followed by the final answer using this structure:
<code translate="no" dir="ltr">&lt;|channel&gt;thought\n</code><strong>[Internal reasoning]</strong><code translate="no" dir="ltr">&lt;channel|&gt;</code></li>
<li><strong>Disabled Thinking Behavior:</strong> For all models except for the E2B and E4B
variants, if thinking is disabled, the model will still generate the tags
but with an empty thought block: <code translate="no" dir="ltr">&lt;|channel&gt;thought\n&lt;channel|&gt;</code><strong>[Final
answer]</strong></li>
</ul>
<blockquote>
<p>Note that many libraries like Transformers and llama.cpp handle the
complexities of the chat template for you.</p>
</blockquote>
<h3 id="3_multi-turn_conversations" data-text="3. Multi-Turn Conversations" tabindex="-1">3. Multi-Turn Conversations</h3>
<ul>
<li><strong>No Thinking Content in History</strong>: In multi-turn conversations, the
historical model output should only include the final response. Thoughts
from previous model turns must <em>not be added</em> before the next user turn
begins.</li>
</ul>
<h3 id="4_modality_order" data-text="4. Modality order" tabindex="-1">4. Modality order</h3>
<ul>
<li>For optimal performance with multimodal inputs, place image and/or audio
content <strong>before</strong> the text in your prompt.</li>
</ul>
<h3 id="5_variable_image_resolution" data-text="5. Variable Image Resolution" tabindex="-1">5. Variable Image Resolution</h3>
<p>Aside from variable aspect ratios, Gemma 4 supports variable image resolution
through a configurable visual token budget, which controls how many tokens are
used to represent an image. A higher token budget preserves more visual detail
at the cost of additional compute, while a lower budget enables faster inference
for tasks that don&#39;t require fine-grained understanding.</p>
<ul>
<li>The supported token budgets are: <strong>70</strong>, <strong>140</strong>, <strong>280</strong>, <strong>560</strong>, and
<strong>1120</strong>.
<ul>
<li>Use <em>lower budgets</em> for classification, captioning, or video
understanding, where faster inference and processing many frames
outweigh fine-grained detail.</li>
<li>Use <em>higher budgets</em> for tasks like OCR, document parsing, or reading
small text.</li>
</ul></li>
</ul>
<h3 id="6_audio" data-text="6. Audio" tabindex="-1">6. Audio</h3>
<p>Use the following prompt structures for audio processing:</p>
<ul>
<li><strong>Audio Speech Recognition (ASR)</strong></li>
</ul>
<div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Text only"><code translate="no" dir="ltr">Transcribe the following speech segment in {LANGUAGE} into {LANGUAGE} text.
Follow these specific instructions for formatting the answer:
* Only output the transcription, with no newlines.
* When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three.
</code></pre></devsite-code>
<ul>
<li><strong>Automatic Speech Translation (AST)</strong></li>
</ul>
<div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Text only"><code translate="no" dir="ltr">Transcribe the following speech segment in {SOURCE_LANGUAGE}, then translate it into {TARGET_LANGUAGE}.
When formatting the answer, first output the transcription in {SOURCE_LANGUAGE}, then one newline, then output the string '{TARGET_LANGUAGE}: ', then the translation in {TARGET_LANGUAGE}.
</code></pre></devsite-code>
<h3 id="7_audio_and_video_length" data-text="7. Audio and Video Length" tabindex="-1">7. Audio and Video Length</h3>
<p>All models support image inputs and can process videos as frames whereas the E2B
and E4B models also support audio inputs. Audio supports a maximum length of 30
seconds. Video supports a maximum of 60 seconds assuming the images are
processed at one frame per second.</p>
<h2 id="model_data" data-text="Model Data" tabindex="-1"><strong>Model Data</strong></h2>
<p>Data used for model training and how the data was processed.</p>
<h3 id="training_dataset" data-text="Training Dataset" tabindex="-1"><strong>Training Dataset</strong></h3>
<p>Our pre-training dataset is a large-scale, diverse collection of data
encompassing a wide range of domains and modalities, which includes web
documents, code, images, audio, with a cutoff date of January 2025. Here are the
key components:</p>
<ul>
<li><strong>Web Documents</strong>: A diverse collection of web text ensures the model is
exposed to a broad range of linguistic styles, topics, and vocabulary. The
training dataset includes content in over 140 languages.</li>
<li><strong>Code</strong>: Exposing the model to code helps it to learn the syntax and
patterns of programming languages, which improves its ability to generate
code and understand code-related questions.</li>
<li><strong>Mathematics</strong>: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.</li>
<li><strong>Images</strong>: A wide range of images enables the model to perform image
analysis and visual data extraction tasks.</li>
</ul>
<p>The combination of these diverse data sources is crucial for training a powerful
multimodal model that can handle a wide variety of different tasks and data
formats.</p>
<h3 id="data_preprocessing" data-text="Data Preprocessing" tabindex="-1"><strong>Data Preprocessing</strong></h3>
<p>Here are the key data cleaning and filtering methods applied to the training
data:</p>
<ul>
<li><strong>CSAM Filtering</strong>: Rigorous CSAM (Child Sexual Abuse Material) filtering
was applied at multiple stages in the data preparation process to ensure the
exclusion of harmful and illegal content.</li>
<li><strong>Sensitive Data Filtering</strong>: As part of making Gemma pre-trained models
safe and reliable, automated techniques were used to filter out certain
personal information and other sensitive data from training sets.</li>
<li><strong>Additional methods</strong>: Filtering based on content quality and safety in
line with <a href="https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf">our
policies</a>.</li>
</ul>
<h2 id="ethics_and_safety" data-text="Ethics and Safety" tabindex="-1"><strong>Ethics and Safety</strong></h2>
<p>As open models become central to enterprise infrastructure, provenance and
security are paramount. Developed by Google DeepMind, Gemma 4 undergoes the same
rigorous safety evaluations as our proprietary Gemini models.</p>
<h3 id="evaluation_approach" data-text="Evaluation Approach" tabindex="-1"><strong>Evaluation Approach</strong></h3>
<p>Gemma 4 models were developed in partnership with internal safety and
responsible AI teams. A range of automated as well as human evaluations were
conducted to help improve model safety. These evaluations align with <a href="https://ai.google/principles/">Google&#39;s
AI principles</a>, as well as safety policies, which
aim to prevent our generative AI models from generating harmful content,
including:</p>
<ul>
<li>Content related to child sexual abuse material and exploitation</li>
<li>Dangerous content (e.g., promoting suicide, or instructing in activities
that could cause real-world harm)</li>
<li>Sexually explicit content</li>
<li>Hate speech (e.g., dehumanizing members of protected groups)</li>
<li>Harassment (e.g., encouraging violence against people)</li>
</ul>
<h3 id="evaluation_results" data-text="Evaluation Results" tabindex="-1"><strong>Evaluation Results</strong></h3>
<p>For all areas of safety testing, we saw major improvements in all categories of
content safety relative to previous Gemma models. Overall, Gemma 4 models
significantly outperform Gemma 3 and 3n models in improving safety, while
keeping unjustified refusals low. All testing was conducted without safety
filters to evaluate the model capabilities and behaviors. For both text-to-text
and image-to-text, and across all model sizes, the model produced minimal policy
violations, and showed significant improvements over previous Gemma models&#39;
performance.</p>
<h2 id="usage_and_limitations" data-text="Usage and Limitations" tabindex="-1"><strong>Usage and Limitations</strong></h2>
<p>These models have certain limitations that users should be aware of.</p>
<h3 id="intended_usage" data-text="Intended Usage" tabindex="-1"><strong>Intended Usage</strong></h3>
<p>Multimodal models (capable of processing vision, language, and/or audio) have a
wide range of applications across various industries and domains. The following
list of potential uses is not comprehensive. The purpose of this list is to
provide contextual information about the possible use-cases that the model
creators considered as part of model training and development.</p>
<ul>
<li><strong>Content Creation and Communication</strong>
<ul>
<li><strong>Text Generation</strong>: These models can be used to generate creative text
formats such as poems, scripts, code, marketing copy, and email drafts.</li>
<li><strong>Chatbots and Conversational AI</strong>: Power conversational interfaces for
customer service, virtual assistants, or interactive applications.</li>
<li><strong>Text Summarization</strong>: Generate concise summaries of a text corpus,
research papers, or reports.</li>
<li><strong>Image Data Extraction</strong>: These models can be used to extract,
interpret, and summarize visual data for text communications.</li>
<li><strong>Audio Processing and Interaction</strong>: The smaller models (E2B and E4B)
can analyze and interpret audio inputs, enabling voice-driven
interactions and transcriptions.</li>
</ul></li>
<li><strong>Research and Education</strong>
<ul>
<li><strong>Natural Language Processing (NLP) and VLM Research</strong>: These models can
serve as a foundation for researchers to experiment with VLM and NLP
techniques, develop algorithms, and contribute to the advancement of the
field.</li>
<li><strong>Language Learning Tools</strong>: Support interactive language learning
experiences, aiding in grammar correction or providing writing practice.
<ul>
<li><strong>Knowledge Exploration</strong>: Assist researchers in exploring large
bodies of text by generating summaries or answering questions about
specific topics.</li>
</ul></li>
</ul></li>
</ul>
<h3 id="limitations" data-text="Limitations" tabindex="-1"><strong>Limitations</strong></h3>
<ul>
<li><strong>Training Data</strong>
<ul>
<li>The quality and diversity of the training data significantly influence
the model&#39;s capabilities. Biases or gaps in the training data can lead
to limitations in the model&#39;s responses.</li>
<li>The scope of the training dataset determines the subject areas the model
can handle effectively.</li>
</ul></li>
<li><strong>Context and Task Complexity</strong>
<ul>
<li>Models perform well on tasks that can be framed with clear prompts and
instructions. Open-ended or highly complex tasks might be challenging.</li>
<li>A model&#39;s performance can be influenced by the amount of context
provided (longer context generally leads to better outputs, up to a
certain point).</li>
</ul></li>
<li><strong>Language Ambiguity and Nuance</strong>
<ul>
<li>Natural language is inherently complex. Models might struggle to grasp
subtle nuances, sarcasm, or figurative language.</li>
</ul></li>
<li><strong>Factual Accuracy</strong>
<ul>
<li>Models generate responses based on information they learned from their
training datasets, but they are not knowledge bases. They may generate
incorrect or outdated factual statements.</li>
</ul></li>
<li><strong>Common Sense</strong>
<ul>
<li>Models rely on statistical patterns in language. They might lack the
ability to apply common sense reasoning in certain situations.</li>
</ul></li>
</ul>
<h3 id="ethical_considerations_and_risks" data-text="Ethical Considerations and Risks" tabindex="-1"><strong>Ethical Considerations and Risks</strong></h3>
<p>The development of vision-language models (VLMs) raises several ethical
concerns. In creating an open model, we have carefully considered the following:</p>
<ul>
<li><strong>Bias and Fairness</strong>
<ul>
<li>VLMs trained on large-scale, real-world text and image data can reflect
socio-cultural biases embedded in the training material. Gemma 4 models
underwent careful scrutiny, input data pre-processing, and post-training
evaluations as reported in this card to help mitigate the risk of these
biases.</li>
</ul></li>
<li><strong>Misinformation and Misuse</strong>
<ul>
<li>VLMs can be misused to generate text that is false, misleading, or
harmful.</li>
<li>Guidelines are provided for responsible use with the model, see the
<a href="https://ai.google.dev/responsible">Responsible Generative AI Toolkit</a>.</li>
</ul></li>
<li><strong>Transparency and Accountability</strong>
<ul>
<li>This model card summarizes details on the models&#39; architecture,
capabilities, limitations, and evaluation processes.</li>
<li>A responsibly developed open model offers the opportunity to share
innovation by making VLM technology accessible to developers and
researchers across the AI ecosystem.</li>
</ul></li>
</ul>
<p><strong>Risks identified and mitigations</strong>:</p>
<ul>
<li><strong>Generation of harmful content</strong>: Mechanisms and guidelines for content
safety are essential. Developers are encouraged to exercise caution and
implement appropriate content safety safeguards based on their specific
product policies and application use cases.</li>
<li><strong>Misuse for malicious purposes</strong>: Technical limitations and developer and
end-user education can help mitigate against malicious applications of VLMs.
Educational resources and reporting mechanisms for users to flag misuse are
provided.</li>
<li><strong>Privacy violations</strong>: Models were trained on data filtered for removal of
certain personal information and other sensitive data. Developers are
encouraged to adhere to privacy regulations with privacy-preserving
techniques.</li>
<li><strong>Perpetuation of biases</strong>: It&#39;s encouraged to perform continuous monitoring
(using evaluation metrics, human review) and the exploration of de-biasing
techniques during model training, fine-tuning, and other use cases.</li>
</ul>
<h3 id="benefits" data-text="Benefits" tabindex="-1"><strong>Benefits</strong></h3>
<p>At the time of release, this family of models provides high-performance open
vision-language model implementations designed from the ground up for
responsible AI development compared to similarly sized models.</p>
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