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    <title>Gemma-4 on AI Prompt Toolkit</title>
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    <description>Recent content in Gemma-4 on AI Prompt Toolkit</description>
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    <lastBuildDate>Fri, 12 Jun 2026 00:00:00 +0000</lastBuildDate>
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      <title>Gemma 4 Master Prompts (June 2026)</title>
      <link>/prompts/gemma-4-master-prompts/</link>
      <pubDate>Fri, 12 Jun 2026 00:00:00 +0000</pubDate>
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      <description>&lt;h2 id=&#34;gemma-4-prompt-guide&#34;&gt;Gemma 4 Prompt Guide&lt;/h2&gt;&#xA;&lt;p&gt;&lt;strong&gt;Gemma 4 12B&lt;/strong&gt; (released June 2026) is Google&amp;rsquo;s &lt;strong&gt;encoder-free any-to-any multimodal model&lt;/strong&gt; — a single unified architecture that processes text, images, audio, and video without separate modality-specific encoders. It ships with &lt;strong&gt;Apache 2.0 open weights&lt;/strong&gt;, making it the most deployable multimodal open model available.&lt;/p&gt;&#xA;&lt;h3 id=&#34;key-capabilities&#34;&gt;Key Capabilities&lt;/h3&gt;&#xA;&lt;table&gt;&#xA;  &lt;thead&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;th&gt;Feature&lt;/th&gt;&#xA;          &lt;th&gt;Specification&lt;/th&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/thead&gt;&#xA;  &lt;tbody&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Architecture&lt;/td&gt;&#xA;          &lt;td&gt;12B encoder-free any-to-any&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Context Window&lt;/td&gt;&#xA;          &lt;td&gt;256,000 tokens&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Languages&lt;/td&gt;&#xA;          &lt;td&gt;140+ natively supported&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Modalities&lt;/td&gt;&#xA;          &lt;td&gt;Text, image, audio, video&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;License&lt;/td&gt;&#xA;          &lt;td&gt;Apache 2.0 (fully open)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;      &lt;tr&gt;&#xA;          &lt;td&gt;Deployment&lt;/td&gt;&#xA;          &lt;td&gt;Laptop-class (ONNX + MLX ready)&lt;/td&gt;&#xA;      &lt;/tr&gt;&#xA;  &lt;/tbody&gt;&#xA;&lt;/table&gt;&#xA;&lt;h3 id=&#34;prompting-strategy&#34;&gt;Prompting Strategy&lt;/h3&gt;&#xA;&lt;ol&gt;&#xA;&lt;li&gt;&lt;strong&gt;Declare modalities upfront&lt;/strong&gt; — Tell Gemma 4 what types of input you&amp;rsquo;re providing&lt;/li&gt;&#xA;&lt;li&gt;&lt;strong&gt;Use the full context&lt;/strong&gt; — 256K tokens lets you include entire documents, codebases, or transcripts&lt;/li&gt;&#xA;&lt;li&gt;&lt;strong&gt;Specify output format&lt;/strong&gt; — Gemma 4 responds well to structured output format directives&lt;/li&gt;&#xA;&lt;li&gt;&lt;strong&gt;Explicit language selection&lt;/strong&gt; — For multilingual tasks, name the target language explicitly&lt;/li&gt;&#xA;&lt;li&gt;&lt;strong&gt;Sequential analysis for mixed content&lt;/strong&gt; — Break complex multi-modal tasks into ordered steps&lt;/li&gt;&#xA;&lt;/ol&gt;&#xA;&lt;h3 id=&#34;deployment&#34;&gt;Deployment&lt;/h3&gt;&#xA;&lt;p&gt;Weights available via Hugging Face. QAT (Quantization-Aware Training) enables INT4/FP8 deployment on consumer hardware. ONNX and MLX ports available for Apple Silicon.&lt;/p&gt;</description>
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