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gemma4-research/tooling/google-official/docs/ai-google-dev_prompt_formatting_gemma4.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">
<main role="main" id="main-content" class="devsite-main-content"
has-book-nav
has-sidebar
>
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<div class="devsite-sidebar-content">
<devsite-toc class="devsite-nav"
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<devsite-content>
<article class="devsite-article"><style>
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body[layout=docs][theme="googledevai-theme"]:not([type="reference"]) .devsite-main-content,
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body[layout=docs][theme="googledevai-theme"] devsite-content {
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body[layout=docs][theme="googledevai-theme"] .devsite-article {
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/* from DevSite's buttons.scss */
.tfo-notebook-buttons a,
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border .2s;
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background: 0;
border: 0;
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/* on rendered notebook page,
remove link to webpage since we're already here */
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height: 20px;
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color: #bdbdbd;
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background-color: #f2f2f2;
color: #5f6368;
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@media screen and (max-width: 600px) {
.tfo-notebook-buttons td {
display: block;
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}
devsite-nav-buttons button {
margin-left: 0;
margin-top: 5px;
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code {
border-radius: 6px
}
devsite-book-nav .devsite-nav-list>.devsite-nav-heading:not(.devsite-nav-divider) {
border-top: 0;
padding-bottom: 0.9rem;
font-size: 1rem;
}
/*
* TODO(b/439059414): Remove this workaround in favor of a project-level
* body_class when possible.
*/
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display: none;
}
/*
* Gemini API body class.
* https://source.corp.google.com/piper///depot/google3/third_party/devsite/googledevai/en/gemini-api/_project.yaml;l=7
*/
.gemini-api devsite-thumb-rating[position="header"],
.gemini-api devsite-feedback[position="header"] {
/* Hide the thumb rating and feedback widgets at the top of the page. */
display: none;
}
/*
* Shaded table styles look like a .pricing-table but are more flexible around
* content sizes in each column.
*/
.shaded-table {
border-collapse: separate;
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border-radius: 8px;
overflow: hidden;
}
.shaded-table th {
background-color: #f2f2f2;
text-align: left;
padding: 8px;
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<h1 class="devsite-page-title" tabindex="-1">
Gemma 4 Prompt Formatting<devsite-actions hidden data-nosnippet>
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<p>
</p>
<p>Starting with Gemma 4, we introduce new control tokens. For Gemma 3 and lower,
see the <a href="/gemma/docs/core/prompt-structure">previous document</a>.</p>
<p>The following sections specify the control tokens used by Gemma 4 and their use
cases. Note that the control tokens are reserved in and specific to our
tokenizer.</p>
<ul>
<li>Token to indicate a system instruction: <code translate="no" dir="ltr">system</code></li>
<li>Token to indicate a user turn: <code translate="no" dir="ltr">user</code></li>
<li>Token to indicate a model turn: <code translate="no" dir="ltr">model</code></li>
<li>Token to indicate the beginning of a dialogue turn: <code translate="no" dir="ltr">&lt;|turn&gt;</code></li>
<li>Token to indicate the end of a dialogue turn: <code translate="no" dir="ltr">&lt;turn|&gt;</code></li>
</ul>
<p>Here&#39;s an example dialogue:</p>
<div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Text only"><code translate="no" dir="ltr">&lt;|turn&gt;system
You are a helpful assistant.&lt;turn|&gt;
&lt;|turn&gt;user
Hello.&lt;turn|&gt;
</code></pre></devsite-code>
<h2 id="multimodal" data-text="Multi-modalities" tabindex="-1">Multi-modalities</h2>
<table>
<thead>
<tr>
<th style="text-align: left">Multimodal Token</th>
<th style="text-align: left">Purpose</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left"><code translate="no" dir="ltr">&lt;|image&gt;</code> <br> <code translate="no" dir="ltr">&lt;image|&gt;</code></td>
<td style="text-align: left">Indicate image embeddings</td>
</tr>
<tr>
<td style="text-align: left"><code translate="no" dir="ltr">&lt;|audio&gt;</code> <br> <code translate="no" dir="ltr">&lt;audio|&gt;</code></td>
<td style="text-align: left">Indicate audio embeddings</td>
</tr>
<tr>
<td style="text-align: left"><code translate="no" dir="ltr">&lt;|image|&gt;</code> <br> <code translate="no" dir="ltr">&lt;|audio|&gt;</code></td>
<td style="text-align: left">Special placeholder tokens</td>
</tr>
</tbody>
</table>
<p>We use two special placeholder tokens (<code translate="no" dir="ltr">&lt;|image|&gt;</code> and <code translate="no" dir="ltr">&lt;|audio|&gt;</code>) to specify
where image and audio tokens should be inserted. After tokenization, these
tokens are replaced by the actual soft embeddings inside the model.</p>
<p>Here is an example dialogue:</p>
<div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr"><span class="devsite-syntax-n">prompt</span> <span class="devsite-syntax-o">=</span> <span class="devsite-syntax-s2">"""&lt;|turn&gt;user</span>
<span class="devsite-syntax-s2">Describe this image: &lt;|image|&gt;</span>
<span class="devsite-syntax-s2">And translate these audio:</span>
<span class="devsite-syntax-s2">a. &lt;|audio|&gt;</span>
<span class="devsite-syntax-s2">b. &lt;|audio|&gt;&lt;turn|&gt;</span>
&lt;<span class="devsite-syntax-s2">|turn&gt;model"""</span>
</code></pre></devsite-code>
<h2 id="agentic-tokens" data-text="Agentic and Reasoning Control Tokens" tabindex="-1">Agentic and Reasoning Control Tokens</h2>
<p>To support agentic workflows, Gemma uses specialized control tokens that
delineate internal reasoning (thinking) from external actions (function
calling). These tokens allow the model to process complex logic before providing
a final response or interacting with outside tools.</p>
<h3 id="function-calling" data-text="Function Calling" tabindex="-1">Function Calling</h3>
<p>Gemma 4 is trained on six special tokens to manage the &quot;tool use&quot; lifecycle.</p>
<table>
<thead>
<tr>
<th style="text-align: left">Token Pair</th>
<th style="text-align: left">Purpose</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left"><code translate="no" dir="ltr">&lt;|tool&gt;</code> <br> <code translate="no" dir="ltr">&lt;tool|&gt;</code></td>
<td style="text-align: left">Defines a tool</td>
</tr>
<tr>
<td style="text-align: left"><code translate="no" dir="ltr">&lt;|tool_call&gt;</code> <br> <code translate="no" dir="ltr">&lt;tool_call|&gt;</code></td>
<td style="text-align: left">Indicates a model&#39;s request to use a tool.</td>
</tr>
<tr>
<td style="text-align: left"><code translate="no" dir="ltr">&lt;|tool_response&gt;</code> <br> <code translate="no" dir="ltr">&lt;tool_response|&gt;</code></td>
<td style="text-align: left">Provides a tool&#39;s execution result back to the model.</td>
</tr>
</tbody>
</table>
<aside class="note"><strong>Note:</strong><span> <code translate="no" dir="ltr">&lt;|tool_response&gt;</code> acts as an additional stop sequence for the inference
engine.</span></aside>
<p><strong>Delimiter for String Values: <code translate="no" dir="ltr">&lt;|&quot;|&gt;</code></strong></p>
<p>A single token, <code translate="no" dir="ltr">&lt;|&quot;|&gt;</code>, is used as a delimiter for <strong>all string values</strong>
within the structured data blocks.</p>
<ul>
<li><strong>Purpose:</strong> This token ensures that any special characters (such as <code translate="no" dir="ltr">{</code>,
<code translate="no" dir="ltr">}</code>, <code translate="no" dir="ltr">,</code>, or quotes) inside a string are treated as literal text and not as
part of the data structure&#39;s underlying syntax.</li>
<li><strong>Usage:</strong> All string literals in your function declarations, calls, and
responses must be enclosed using this token (e.g., <code translate="no" dir="ltr">key:&lt;|&quot;|&gt;string
value&lt;|&quot;|&gt;</code>).</li>
</ul>
<h3 id="thinking-mode" data-text="Thinking Mode" tabindex="-1">Thinking Mode</h3>
<p>To activate thinking mode, include the <code translate="no" dir="ltr">&lt;|think|&gt;</code> control token within the
system instruction.</p>
<table>
<thead>
<tr>
<th style="text-align: left">Control Token</th>
<th style="text-align: left">Purpose</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left"><code translate="no" dir="ltr">&lt;|think|&gt;</code></td>
<td style="text-align: left">Activates thinking mode</td>
</tr>
<tr>
<td style="text-align: left"><code translate="no" dir="ltr">&lt;|channel&gt;</code> <br> <code translate="no" dir="ltr">&lt;channel|&gt;</code></td>
<td style="text-align: left">Indicates a model&#39;s internal process.</td>
</tr>
</tbody>
</table>
<aside class="note"><strong>Note:</strong><span> <code translate="no" dir="ltr">&lt;|channel&gt;</code> is always followed by the word &quot;thought&quot; when thinking mode
is active.</span></aside>
<p>Here is an example dialogue:</p>
<div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Text only"><code translate="no" dir="ltr">&lt;|turn&gt;system
&lt;|think|&gt;&lt;turn|&gt;
&lt;|turn&gt;user
What is the water formula?&lt;turn|&gt;
&lt;|turn&gt;model
&lt;|channel&gt;thought
...
&lt;channel|&gt;The most common interpretation of "the water formula" refers...&lt;turn|&gt;
</code></pre></devsite-code>
<p>Thinking mode is designed to be enabled at the conversation level. This should
be consolidated into a single system turn alongside your other system
instructions, such as tool definitions.</p>
<h3 id="function-calling-with-thinking" data-text="Reasoning and Function Calling Example" tabindex="-1">Reasoning and Function Calling Example</h3>
<p>In an agentic turn, the model may &quot;think&quot; privately before deciding to call a
function. The lifecycle follows this sequence:</p>
<ol>
<li>User Inquiry: The user asks a question.</li>
<li>Internal Reasoning: The model thinks privately in the thought channel.</li>
<li>Tool Request: The model halts generation to request a tool call.</li>
<li>Execution &amp; Injection: The application executes the tool and appends the response.</li>
<li>Final Response: The model reads the response and generates the final answer.</li>
</ol>
<p>The following example demonstrates a model using a weather tool:</p>
<div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Text only"><code translate="no" dir="ltr">&lt;|turn&gt;system
&lt;|think|&gt;You are a helpful assistant.&lt;|tool&gt;declaration:get_current_temperature{...}&lt;tool|&gt;&lt;turn|&gt;
&lt;|turn&gt;user
What's the temperature in London?&lt;turn|&gt;
&lt;|turn&gt;model
&lt;|channel&gt;thought
...
&lt;channel|&gt;&lt;|tool_call&gt;call:get_current_temperature{location:&lt;|"|&gt;London&lt;|"|&gt;}&lt;tool_call|&gt;&lt;|tool_response&gt;
</code></pre></devsite-code>
<p>Your application should parse the model&#39;s response to extract the function name
and arguments, execute the function, and then append the <code translate="no" dir="ltr">tool_calls</code> and
<code translate="no" dir="ltr">tool_responses</code> to the chat history under the <code translate="no" dir="ltr">assistant</code> role.</p>
<div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Text only"><code translate="no" dir="ltr">&lt;|turn&gt;model
&lt;|tool_call&gt;call:get_current_weather{location:&lt;|"|&gt;London&lt;|"|&gt;}&lt;tool_call|&gt;&lt;|tool_response&gt;response:get_current_weather{temperature:15,weather:&lt;|"|&gt;sunny&lt;|"|&gt;}&lt;tool_response|&gt;
</code></pre></devsite-code>
<p>Finally, Gemma reads the tool response and replies to the user.</p>
<div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Text only"><code translate="no" dir="ltr">The temperature in London is 15 degrees and it is sunny.&lt;turn|&gt;
</code></pre></devsite-code>
<p>Here is the complete JSON chat history for this example:</p>
<div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="JSON"><code translate="no" dir="ltr"><span class="devsite-syntax-p">[</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">{</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-nt">"role"</span><span class="devsite-syntax-p">:</span><span class="devsite-syntax-w"> </span><span class="devsite-syntax-s2">"system"</span><span class="devsite-syntax-p">,</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-nt">"content"</span><span class="devsite-syntax-p">:</span><span class="devsite-syntax-w"> </span><span class="devsite-syntax-s2">"You are a helpful assistant."</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">},</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">{</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-nt">"role"</span><span class="devsite-syntax-p">:</span><span class="devsite-syntax-w"> </span><span class="devsite-syntax-s2">"user"</span><span class="devsite-syntax-p">,</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-nt">"content"</span><span class="devsite-syntax-p">:</span><span class="devsite-syntax-w"> </span><span class="devsite-syntax-s2">"What's the temperature in London?"</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">},</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">{</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-nt">"role"</span><span class="devsite-syntax-p">:</span><span class="devsite-syntax-w"> </span><span class="devsite-syntax-s2">"assistant"</span><span class="devsite-syntax-p">,</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-nt">"tool_calls"</span><span class="devsite-syntax-p">:</span><span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">[</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">{</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-nt">"function"</span><span class="devsite-syntax-p">:</span><span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">{</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-nt">"name"</span><span class="devsite-syntax-p">:</span><span class="devsite-syntax-w"> </span><span class="devsite-syntax-s2">"get_current_weather"</span><span class="devsite-syntax-p">,</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-nt">"arguments"</span><span class="devsite-syntax-p">:</span><span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">{</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-nt">"location"</span><span class="devsite-syntax-p">:</span><span class="devsite-syntax-w"> </span><span class="devsite-syntax-s2">"London"</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">}</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">}</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">}</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">],</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-nt">"tool_responses"</span><span class="devsite-syntax-p">:</span><span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">[</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">{</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-nt">"name"</span><span class="devsite-syntax-p">:</span><span class="devsite-syntax-w"> </span><span class="devsite-syntax-s2">"get_current_weather"</span><span class="devsite-syntax-p">,</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-nt">"response"</span><span class="devsite-syntax-p">:</span><span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">{</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-nt">"temperature"</span><span class="devsite-syntax-p">:</span><span class="devsite-syntax-w"> </span><span class="devsite-syntax-mi">15</span><span class="devsite-syntax-p">,</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-nt">"weather"</span><span class="devsite-syntax-p">:</span><span class="devsite-syntax-w"> </span><span class="devsite-syntax-s2">"sunny"</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">}</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">}</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">],</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-nt">"content"</span><span class="devsite-syntax-p">:</span><span class="devsite-syntax-w"> </span><span class="devsite-syntax-s2">"The temperature in London is 15 degrees and it is sunny."</span>
<span class="devsite-syntax-w"> </span><span class="devsite-syntax-p">}</span>
<span class="devsite-syntax-p">]</span>
</code></pre></devsite-code>
<h3 id="managing-thought-context" data-text="Managing Thought Context Between Turns" tabindex="-1">Managing Thought Context Between Turns</h3>
<p>Properly managing the model&#39;s generated thoughts is critical for maintaining
performance across multi-turn conversations.</p>
<ul>
<li><strong>Standard Multi-Turn Conversations:</strong> You must remove (strip) the model&#39;s
generated thoughts from the previous turn before passing the conversation
history back to the model for the next turn. If you want to disable thinking
mode mid-conversation, you can remove the <code translate="no" dir="ltr">&lt;|think|&gt;</code> token when you strip
the previous thoughts.</li>
<li><strong>Function Calling (Exception):</strong> If a single model turn involves function
or tool calls, thoughts must <strong>NOT</strong> be removed between the function calls.</li>
</ul>
<p><strong>Agentic Workflows and Long-Running Tasks</strong></p>
<p>Because raw thoughts are stripped between standard turns, developers building
long-running agents may want to retain reasoning context to prevent the model
from entering cyclical reasoning loops.</p>
<ul>
<li><strong>Summarizing Thoughts:</strong> A highly recommended inference technique is to
extract, summarize, and feed the model&#39;s previous thoughts back into the
context window as standard text.</li>
<li><strong>Formatting Constraints:</strong> Because Gemma 4 was not explicitly trained with
raw thoughts included in the prompt (outside of the specific tool-call
scenario mentioned above), there is no strict or specific format expected by
the model for these injected thoughts. You have the flexibility to format
summarized reasoning in whatever way best suits your specific agentic
architecture.</li>
</ul>
<h2 id="integration-notes" data-text="Integration Notes" tabindex="-1">Integration Notes</h2>
<ul>
<li><strong>Internal State:</strong> The <code translate="no" dir="ltr">&lt;|channel&gt;</code> and <code translate="no" dir="ltr">&lt;channel|&gt;</code> tokens are typically
used for Chain-of-Thought (CoT) processing. In standard user-facing
applications, this content is usually hidden from the end-user.</li>
<li><strong>Tool Loop:</strong> The <code translate="no" dir="ltr">tool_call</code> and <code translate="no" dir="ltr">tool_response</code> tokens facilitate a
&quot;handshake&quot; between the model your application environment. The application
intercepts the <code translate="no" dir="ltr">tool_call</code>, executes the underlying code, and feeds the
result back to the model within the <code translate="no" dir="ltr">tool_response</code> tokens.</li>
<li><strong>Model Behavior:</strong> Larger models (e.g., gemma-4-26B-A4B-it, gemma-4-31B-it)
may occasionally generate a thought channel even when thinking mode is
explicitly turned off. To stabilize model behavior in these edge cases,
consider adding an empty thinking token to the prompt.</li>
</ul>
<h2 id="tuning-big-models-no-thinking" data-text="Tip: Fine-Tuning Big Models with No-Thinking Datasets" tabindex="-1">Tip: Fine-Tuning Big Models with No-Thinking Datasets</h2>
<p>When fine-tuning larger Gemma models with a dataset that does not include
thinking, you can achieve better results by adding the empty channel to your
training prompts:</p>
<div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Text only"><code translate="no" dir="ltr">&lt;|turn&gt;model
&lt;|channel&gt;thought
&lt;channel|&gt;
</code></pre></devsite-code>
<h2 id="adaptive-thought-efficiency" data-text="Tip: Adaptive Thought Efficiency using System Instructions" tabindex="-1">Tip: Adaptive Thought Efficiency using System Instructions</h2>
<p>While &quot;thinking&quot; in Gemma 4 is officially supported as an ON or OFF boolean
feature, the model has exceptionally strong instruction-following capabilities
that allow you to modulate its thinking behavior dynamically.</p>
<p>Rather than relying on a hardcoded framework parameter for &quot;high&quot; or &quot;low&quot;
thinking, you can use System Instructions (SI) to guide the model into a reduced
thinking mode. By explicitly instructing the model to think efficiently or at a
lower depth (a concept we refer to as a &quot;LOW&quot; thinking instruction), you can
achieve adaptive thought efficiency.</p>
<ul>
<li><strong>Reduced Cost:</strong> Testing has shown that applying a &quot;LOW&quot; thinking System
Instruction can reduce the number of thinking tokens generated by
approximately 20%.</li>
<li><strong>Proof of Concept:</strong> Because this behavior is a byproduct of the model&#39;s
instructability rather than a specifically trained, there is no single
&quot;perfect&quot; prompt. The &quot;LOW&quot; instruction is a proof of concept.</li>
<li><strong>Customization:</strong> We highly encourage developers to play around with their
own custom System Instructions. You can fine-tune the depth, length, and
style of the model&#39;s thinking process to perfectly balance latency, cost,
and output quality for your specific use cases.</li>
</ul>
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