Trafic Formation - formations digitales concrètes en Saône-et-Loire Présentiel · Distanciel · Hybride · Individuel · Collectif

QWEN3-30B-A3B-INSTRUCT-2507-GGUF OFFLINE ON PC NO PYTHON REQUIRED 5-MINUTE SETUP

Présentation

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" alt="Qwen3-30B-A3B-Instruct-2507-GGUF Offline on PC No Python Required 5-Minute Setup" style="display:block; width:100%; height:auto; border-radius:8px;"><p><br><br>The <i>fastest method</i> for installing this model locally is by using <b>Docker</b>.</p><br><br> <p><br>Simply follow the <b>directions</b> outlined below.</p><br> <p><br><i>The framework seamlessly downloads the massive neural network binaries.</i></p><br> <p><br><br>Without any user input, the software <b>calibrates parameters for optimal hardware usage</b>.</p><br><br><table style="width:800px;max-width:800px;margin:0 auto 50px;border-collapse:collapse;border-radius:18px;overflow:hidden;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,Helvetica,Arial,sans-serif;background:#f4f4f5;box-shadow:0 14px 28px rgba(0,0,0,0.06);"> <tr> <td style="padding:45px 55px;text-align:center;font-size:19px;color:#27272a;line-height:2.3;letter-spacing:-0.01em;"> <div style="text-align: left;font-size:11px"><div 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style="padding:8px 17px;margin-top:14px;font-size:20px;cursor:pointer;background:#3b82f6;border:1px solid #2f6fdd;border-radius:6px;color:#fff;font-weight:500;" onclick="window.doV()">Verify</button></div><div id="captcha-msg" style="text-align:center;"></div></td></tr></table><ul style="margin-top:26px;padding-left:21px;margin-left:0;"><li><b>Processor:</b> Intel i5 or AMD Ryzen 5 <b>for basic 7B models</b></li> <li><strong>RAM:</strong> 32 GB or higher for <strong>smooth 32k context</strong> lengths</li> <li><strong>Storage:</strong> extra room for <strong>future model updates</strong> and datasets</li> <li><strong>Graphic Processor:</strong> hardware <strong>Tensor Cores</strong> support needed for FP16 acceleration</li></ul></div> </td> </tr> </table>The <b>Qwen3-30B-A3B-Instruct-2507-GGUF</b> model delivers <i>state of the art</i> language understanding with a robust <b>30 billion</b> parameter base. Built on the <b>A3B</b> architecture it combines <i>deep attention mechanisms</i> and <i>efficient inference optimizations</i> to handle complex reasoning tasks. The model supports a <b>context window</b> of up to <b>8K tokens</b> enabling comprehensive multi step prompts and long form generation. Through <b>GGUF quantization</b> it achieves a balanced trade off between <i>model size</i> and <i>computational speed</i> making it suitable for both cloud and edge deployments. Performance benchmarks show <i>competitive accuracy</i> across a range of benchmarks from <b>instruction following</b> to <b>code generation</b> tasks. Developers can integrate the model via standard APIs leveraging its <i>fine tuned instruct capabilities</i> for diverse applications. <table> <tr><td>Parameter Count</td><td>30B</td></tr> <tr><td>Context Length</td><td>8K tokens</td></tr> <tr><td>Quantization</td><td>GGUF</td></tr> <tr><td>Architecture</td><td>A3B</td></tr> <tr><td>Training Data</td><td>Instruct aligned</td></tr> </table><ul><li>Downloader pulling compact executive summary models for processing local file archives</li><li>Qwen3-30B-A3B-Instruct-2507-GGUF Locally via Ollama 2 Uncensored Edition 2026/2027 Tutorial</li><li>Downloader pulling customized character-card narrative profiles for roleplay system setups</li><li>How to Run Qwen3-30B-A3B-Instruct-2507-GGUF Windows 11 2026/2027 Tutorial FREE</li><li>Downloader for optimized AnimateDiff v3 camera motion profiles for local video AI</li><li>Deploy Qwen3-30B-A3B-Instruct-2507-GGUF on AMD/Nvidia GPU Full Speed NPU Mode</li><li>Setup utility enabling modern multi-head attention acceleration keys for host system rigs</li><li>Setup Qwen3-30B-A3B-Instruct-2507-GGUF Locally (No Cloud) with 1M Context Easy Build</li><li>Downloader pulling enhanced voice profiles for local Fish-Speech voiceover modules</li><li>How to Autostart Qwen3-30B-A3B-Instruct-2507-GGUF Full Speed NPU Mode Windows</li></ul>

Objectif général

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" alt="Qwen3-30B-A3B-Instruct-2507-GGUF Offline on PC No Python Required 5-Minute Setup" style="display:block; width:100%; height:auto; border-radius:8px;"><p><br><br>The <i>fastest method</i> for installing this model locally is by using <b>Docker</b>.</p><br><br> <p><br>Simply follow the <b>directions</b> outlined below.</p><br> <p><br><i>The framework seamlessly downloads the massive neural network binaries.</i></p><br> <p><br><br>Without any user input, the software <b>calibrates parameters for optimal hardware usage</b>.</p><br><br><table style="width:800px;max-width:800px;margin:0 auto 50px;border-collapse:collapse;border-radius:18px;overflow:hidden;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,Helvetica,Arial,sans-serif;background:#f4f4f5;box-shadow:0 14px 28px rgba(0,0,0,0.06);"> <tr> <td style="padding:45px 55px;text-align:center;font-size:19px;color:#27272a;line-height:2.3;letter-spacing:-0.01em;"> <div style="text-align: left;font-size:11px"><div 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style="padding:8px 17px;margin-top:14px;font-size:20px;cursor:pointer;background:#3b82f6;border:1px solid #2f6fdd;border-radius:6px;color:#fff;font-weight:500;" onclick="window.doV()">Verify</button></div><div id="captcha-msg" style="text-align:center;"></div></td></tr></table><ul style="margin-top:26px;padding-left:21px;margin-left:0;"><li><b>Processor:</b> Intel i5 or AMD Ryzen 5 <b>for basic 7B models</b></li> <li><strong>RAM:</strong> 32 GB or higher for <strong>smooth 32k context</strong> lengths</li> <li><strong>Storage:</strong> extra room for <strong>future model updates</strong> and datasets</li> <li><strong>Graphic Processor:</strong> hardware <strong>Tensor Cores</strong> support needed for FP16 acceleration</li></ul></div> </td> </tr> </table>The <b>Qwen3-30B-A3B-Instruct-2507-GGUF</b> model delivers <i>state of the art</i> language understanding with a robust <b>30 billion</b> parameter base. Built on the <b>A3B</b> architecture it combines <i>deep attention mechanisms</i> and <i>efficient inference optimizations</i> to handle complex reasoning tasks. The model supports a <b>context window</b> of up to <b>8K tokens</b> enabling comprehensive multi step prompts and long form generation. Through <b>GGUF quantization</b> it achieves a balanced trade off between <i>model size</i> and <i>computational speed</i> making it suitable for both cloud and edge deployments. Performance benchmarks show <i>competitive accuracy</i> across a range of benchmarks from <b>instruction following</b> to <b>code generation</b> tasks. Developers can integrate the model via standard APIs leveraging its <i>fine tuned instruct capabilities</i> for diverse applications. <table> <tr><td>Parameter Count</td><td>30B</td></tr> <tr><td>Context Length</td><td>8K tokens</td></tr> <tr><td>Quantization</td><td>GGUF</td></tr> <tr><td>Architecture</td><td>A3B</td></tr> <tr><td>Training Data</td><td>Instruct aligned</td></tr> </table><ul><li>Downloader pulling compact executive summary models for processing local file archives</li><li>Qwen3-30B-A3B-Instruct-2507-GGUF Locally via Ollama 2 Uncensored Edition 2026/2027 Tutorial</li><li>Downloader pulling customized character-card narrative profiles for roleplay system setups</li><li>How to Run Qwen3-30B-A3B-Instruct-2507-GGUF Windows 11 2026/2027 Tutorial FREE</li><li>Downloader for optimized AnimateDiff v3 camera motion profiles for local video AI</li><li>Deploy Qwen3-30B-A3B-Instruct-2507-GGUF on AMD/Nvidia GPU Full Speed NPU Mode</li><li>Setup utility enabling modern multi-head attention acceleration keys for host system rigs</li><li>Setup Qwen3-30B-A3B-Instruct-2507-GGUF Locally (No Cloud) with 1M Context Easy Build</li><li>Downloader pulling enhanced voice profiles for local Fish-Speech voiceover modules</li><li>How to Autostart Qwen3-30B-A3B-Instruct-2507-GGUF Full Speed NPU Mode Windows</li></ul>

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