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22af59756f62ae1c839f3bc9b23dc8817707012b
gemma4-research/tooling/google-official/docs/ai-google-dev_core.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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display: block;
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devsite-nav-buttons button {
margin-left: 0;
margin-top: 5px;
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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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display: none;
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/*
* Shaded table styles look like a .pricing-table but are more flexible around
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border-bottom-right-radius: 8px;
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<h1 class="devsite-page-title" tabindex="-1">
Gemma 4 model overview<devsite-actions hidden data-nosnippet>
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<p>Gemma is a family of generative artificial intelligence models and you can
use them in a wide variety of generation tasks, including question answering,
summarization, and reasoning. Gemma models are provided with open weights and
permit responsible
<a href="/gemma/terms">commercial use</a>,
allowing you to tune and deploy them in your own projects and applications.</p>
<p>Gemma 4 model family spans three distinct architectures tailored for specific
hardware requirements:</p>
<ul>
<li><strong>Small Sizes:</strong> 2B and 4B effective parameter models built for
ultra-mobile, edge, and browser deployment (e.g., Pixel, Chrome).</li>
<li><strong>Dense:</strong> A powerful 31B parameter dense model that bridges the gap between
server-grade performance and local execution.</li>
<li><strong>Mixture-of-Experts:</strong> A highly efficient 26B MoE model designed for
high-throughput, advanced reasoning.</li>
</ul>
<p>You can download Gemma 4 models from
<a href="https://www.kaggle.com/models?query=gemma-4&amp;publisher=google">Kaggle</a> and
<a href="https://huggingface.co/collections/google/gemma-4">Hugging Face</a>.
For more technical details on Gemma 4, see the
<a href="/gemma/docs/core/model_card_4">Model Card</a>.
Earlier versions of Gemma core models are also available for download. For more
information, see <a href="#previous-models">Previous Gemma models</a>.</p>
<p><a class="button" href="https://www.kaggle.com/models?query=gemma-4&publisher=google">Get it on Kaggle</a>
<a class="button" href="https://huggingface.co/collections/google/gemma-4">Get it on Hugging Face</a></p>
<h2 id="capabilities" data-text="Capabilities" tabindex="-1">Capabilities</h2>
<ul>
<li><strong>Reasoning:</strong> All models in the family are designed as highly capable
reasoners, with configurable <a href="/gemma/docs/capabilities/thinking">thinking
modes</a>.</li>
<li><strong>Extended Multimodalities:</strong> Processes Text,
<a href="/gemma/docs/capabilities/vision/image">Image</a> with variable aspect ratio
and resolution support (all models),
<a href="/gemma/docs/capabilities/vision/video">Video</a>, and
<a href="/gemma/docs/capabilities/audio">Audio</a> (featured natively on the E2B and
E4B models).</li>
<li><strong>Increased Context Window:</strong> Small models feature a 128K context window,
while the medium models support 256K.</li>
<li><strong>Enhanced Coding &amp; Agentic Capabilities:</strong> Achieves notable improvements in
coding benchmarks alongside built-in <a href="/gemma/docs/capabilities/text/function-calling-gemma4">function-calling
support</a>, powering
highly capable autonomous agents.</li>
<li><strong>Native System Prompt Support:</strong> Gemma 4 introduces built-in support for
the system role, enabling more structured and controllable conversations.</li>
</ul>
<h2 id="parameter-sizes-and-quantization" data-text="Parameter sizes and quantization" tabindex="-1">Parameter sizes and quantization</h2>
<p>Gemma 4 models are available in 4 parameter sizes: E2B, E4B, 31B and 26B A4B.
The models can be used with their default precision (16-bit) or with a lower
precision using quantization. The different sizes and precisions represent a set
of trade-offs for your AI application. Models with higher parameters and bit
counts (higher precision) are generally more capable, but are more expensive to
run in terms of processing cycles, memory cost and power consumption. Models
with lower parameters and bit counts (lower precision) have less capabilities,
but may be sufficient for your AI task.</p>
<h3 id="gemma-4-inference-memory-requirements" data-text="Gemma 4 Inference Memory Requirements" tabindex="-1">Gemma 4 Inference Memory Requirements</h3>
<p>The following table details the approximate GPU or TPU memory requirements for
running inference with each size of the Gemma 4 model versions.</p>
<aside class="note"><strong>Note:</strong><span> These numbers may change based on your specific inference tool and
environment.</span></aside>
<table>
<thead>
<tr>
<th>Parameters</th>
<th>BF16 (16-bit)</th>
<th>SFP8 (8-bit)</th>
<th>Q4_0 (4-bit)</th>
</tr>
</thead>
<tbody>
<tr>
<td>Gemma 4 E2B</td>
<td>9.6 GB</td>
<td>4.6 GB</td>
<td>3.2 GB</td>
</tr>
<tr>
<td>Gemma 4 E4B</td>
<td>15 GB</td>
<td>7.5 GB</td>
<td>5 GB</td>
</tr>
<tr>
<td>Gemma 4 31B</td>
<td>58.3 GB</td>
<td>30.4 GB</td>
<td>17.4 GB</td>
</tr>
<tr>
<td>Gemma 4 26B A4B</td>
<td>48 GB</td>
<td>25 GB</td>
<td>15.6 GB</td>
</tr>
</tbody>
</table>
<p><strong>Table 1.</strong> Approximate GPU or TPU memory required to load Gemma 4 models based
on parameter count and quantization level.</p>
<h3 id="key-considerations-for-memory-planning" data-text="Key Considerations for Memory Planning" tabindex="-1">Key Considerations for Memory Planning</h3>
<ul>
<li><strong>Efficient Architecture (E2B and E4B):</strong> The &quot;E&quot; 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 to the model, PLE gives each decoder layer its own small
embedding for every token. These embedding tables are large but only used
for quick lookups, which is why the total memory required to load static
weights is higher than the effective parameter count suggests.</li>
<li><strong>The MoE Architecture (26B A4B):</strong> The 26B is a Mixture of Experts
model. While it only activates 4 billion parameters per token during
generation, <strong>all 26 billion parameters</strong> must be loaded into memory to
maintain fast routing and inference speeds. This is why its baseline memory
requirement is much closer to a dense 26B model than a 4B model.</li>
<li><strong>Base Weights Only:</strong> The estimates in the preceding table <em>only</em> account
for the memory required to load the static model weights. They don&#39;t include
the additional VRAM needed for supporting software or the context window.</li>
<li><strong>Context Window (KV Cache):</strong> Memory consumption will increase dynamically
based on the total number of tokens in your prompt and the generated
response. Larger context windows require significantly more VRAM on top of
the base model weights.</li>
<li><strong>Fine-Tuning Overhead:</strong> Memory requirements for <em>fine-tuning</em> Gemma models
are drastically higher than for standard inference. Your exact footprint
will depend heavily on the development framework, batch size, and whether
you are using full-precision tuning versus a Parameter-Efficient Fine-Tuning
(PEFT) method like Low-Rank Adaptation (LoRA).</li>
</ul>
<h2 id="previous-models" data-text="Previous Gemma models" tabindex="-1">Previous Gemma models</h2>
<p>You can work with previous generations of Gemma models, which are also
available from <a href="https://www.kaggle.com/models?query=gemma">Kaggle</a> and
<a href="https://huggingface.co/google/collections">Hugging Face</a>.
For more technical details about previous Gemma models, see the following
model card pages:</p>
<ul>
<li>Gemma 3 <a href="/gemma/docs/core/model_card_3">Model Card</a></li>
<li>Gemma 2 <a href="/gemma/docs/core/model_card_2">Model Card</a></li>
<li>Gemma 1 <a href="/gemma/docs/core/model_card">Model Card</a></li>
</ul>
<p>Ready to start building?
<a href="/gemma/docs/get_started">Get started</a>
with Gemma models!</p>
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