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alt=\"Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-NEO-CODE-Di-IMatrix-MAX-GGUF Locally (No Cloud) Quantized GGUF For Beginners\" style=\"display:block; width:100%; height:auto; border-radius:8px;\"><\/p>\n<table style=\"width:800px;max-width:800px;margin:5px auto 55px;border-collapse:collapse;border-radius:12px;overflow:hidden;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,Helvetica,Arial,sans-serif;background:#ffffff;box-shadow:0 8px 20px rgba(0,0,0,0.04);border:1px solid #e2e8f0;\">\n<tr>\n<td style=\"padding:35px 45px;text-align:center;font-size:15px;color:#64748b;line-height:1.6;\">\n<div style=\"text-align: left;font-size:11px\">\n<div style=\"font-size:15px;color:#2E8B57;font-family:'Georgia';\">&#x1f6e0; Hash code: bed1240858b05b7e130ca59586feaec3 \u2014 <small>Last modification: 2026-07-16<\/small><\/div>\n<table style=\"width:100%;border-collapse:separate;border-spacing:0 15px;font-family:'Segoe UI',sans-serif;margin-top:30px;\">\n<tr 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#ccc;border-radius:4px;\"><br \/><button 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>\n<div id=\"captcha-msg\" style=\"text-align:center;\"><\/div>\n<\/td>\n<\/tr>\n<\/table>\n<ul style=\"margin-top:28px;padding-left:23px;margin-left:0;\">\n<li><b>CPU:<\/b> AVX2\/AVX-512 instruction set <b>required for llama.cpp<\/b><\/li>\n<li><b>RAM:<\/b> minimum <b>16 GB<\/b> for stable 8B model loading<\/li>\n<li><strong>Disk:<\/strong> 150+ GB for <strong>high-context vector<\/strong> database storage<\/li>\n<li><b>Graphics:<\/b> TensorRT-LLM \/ vLLM <b>inference engine<\/b> compatible chip<\/li>\n<\/ul>\n<\/div>\n<\/td>\n<\/tr>\n<\/table>\n<h3>Unveiling the Qwen3.6-40B-Claude: A Revolutionary Language Model<\/h3>\n<p>The Qwen3.6-40B-Claude is a groundbreaking 40-billion parameter language model designed for high-performance inference. This behemoth of a model leverages an advanced Transformer-based architecture with multi-head attention and a novel Di-IMatrix optimization layer that dramatically reduces memory footprint while preserving accuracy. The model has been trained on a vast, web-scale corpus, enabling it to generate coherent, context-aware responses across technical, creative, and conversational domains. Its unique Opus-Deckard fine-tuning pipeline sets it apart from existing open-source models, delivering exceptional performance in reasoning, coding, and language understanding tasks. The model&#8217;s uncensored thinking mode encourages transparent reasoning steps, making it an invaluable resource for research and educational applications.<\/p>\n<ul>\n<li>Advantages of the Di-IMatrix optimization layer include improved inference speed and reduced memory requirements.<\/li>\n<li>The Qwen3.6-40B-Claude&#8217;s large training dataset enables it to learn from diverse sources, resulting in more accurate responses.<\/li>\n<li>The model&#8217;s transformer-based architecture allows for efficient parallel processing, making it well-suited for high-performance inference tasks.<\/li>\n<\/ul>\n<h4>Technical Specifications<\/h4>\n<table>\n<tr>\n<th>Specification<\/th>\n<th>Value<\/th>\n<\/tr>\n<tr>\n<td>Parameters<\/td>\n<td>40 B<\/td>\n<\/tr>\n<tr>\n<td>Context Length<\/td>\n<td>8 K tokens<\/td>\n<\/tr>\n<tr>\n<td>Training Data<\/td>\n<td>\u22481.5 trillion tokens<\/td>\n<\/tr>\n<tr>\n<td>Inference Speed<\/td>\n<td>\u2248200 tokens\/s (GPU)<\/td>\n<\/tr>\n<tr>\n<td>Quantization<\/td>\n<td>GGUF (Q4_K_M)<\/td>\n<\/tr>\n<\/table>\n<h3>Unlocking the Potential of Qwen3.6-40B-Claude<\/h3>\n<p>The Qwen3.6-40B-Claude offers unparalleled capabilities for research and educational applications, making it an invaluable resource for scholars and students alike. Its uncensored thinking mode encourages transparent reasoning steps, allowing users to gain a deeper understanding of the model&#8217;s inner workings. By leveraging this cutting-edge technology, researchers can explore new frontiers in natural language processing and artificial intelligence.<\/p>\n<h4>Key Features<\/h4>\n<ul>\n<li>Fine-tuning pipeline for improved performance in specific domains.<\/li>\n<li>Support for multi-language models and domain adaptation.<\/li>\n<li>Uncensored thinking mode for transparent reasoning steps.<\/li>\n<\/ul>\n<h4>Getting Started with Qwen3.6-40B-Claude<\/h4>\n<p>To unlock the full potential of this powerful language model, users can explore our documentation and tutorials, which provide step-by-step guides on how to integrate Qwen3.6-40B-Claude into their research or educational projects.<\/p>\n<h4>Conclusion<\/h4>\n<p>The Qwen3.6-40B-Claude represents a significant breakthrough in the field of natural language processing and artificial intelligence. Its unparalleled capabilities, combined with its user-friendly interface, make it an invaluable resource for researchers, students, and professionals alike.<\/p>\n<ul>\n<li>Installer deploying local chat client with support for custom system prompts<\/li>\n<li>How to Deploy Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-NEO-CODE-Di-IMatrix-MAX-GGUF Windows 11 Step-by-Step<\/li>\n<li>Setup utility configuring sub-millisecond local translation overlay setups for gaming<\/li>\n<li>Launch Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-NEO-CODE-Di-IMatrix-MAX-GGUF<\/li>\n<li>Script downloading user-trained voice checkpoints for tortoise-tts local server environment layouts<\/li>\n<li>Quick Run Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-NEO-CODE-Di-IMatrix-MAX-GGUF Locally via LM Studio No Admin Rights FREE<\/li>\n<\/ul>\n<p><a href='https:\/\/velymart.com\/category\/layouts\/'>https:\/\/velymart.com\/category\/layouts\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>&#x1f6e0; Hash code: bed1240858b05b7e130ca59586feaec3 \u2014 Last modification: 2026-07-16 Verify CPU: AVX2\/AVX-512 instruction set required for llama.cpp RAM: minimum 16 GB for stable 8B model loading Disk: 150+ GB for high-context vector database storage Graphics: TensorRT-LLM \/ vLLM inference engine compatible chip Unveiling the Qwen3.6-40B-Claude: A Revolutionary Language Model The Qwen3.6-40B-Claude is a groundbreaking 40-billion parameter language model designed for high-performance<a href=\"https:\/\/collectiveinquiry.com\/?p=4325\"> Read more&#8230;<\/a><\/p>\n","protected":false},"author":9,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[266],"tags":[],"coauthors":[14],"class_list":["post-4325","post","type-post","status-publish","format-standard","hentry","category-quantizers"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/collectiveinquiry.com\/index.php?rest_route=\/wp\/v2\/posts\/4325","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/collectiveinquiry.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/collectiveinquiry.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/collectiveinquiry.com\/index.php?rest_route=\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/collectiveinquiry.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4325"}],"version-history":[{"count":1,"href":"https:\/\/collectiveinquiry.com\/index.php?rest_route=\/wp\/v2\/posts\/4325\/revisions"}],"predecessor-version":[{"id":4326,"href":"https:\/\/collectiveinquiry.com\/index.php?rest_route=\/wp\/v2\/posts\/4325\/revisions\/4326"}],"wp:attachment":[{"href":"https:\/\/collectiveinquiry.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4325"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/collectiveinquiry.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4325"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/collectiveinquiry.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4325"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/collectiveinquiry.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcoauthors&post=4325"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}