{"id":6380,"date":"2025-05-01T00:00:52","date_gmt":"2025-05-01T00:00:52","guid":{"rendered":"https:\/\/www.myshirtai.com\/archives\/6380"},"modified":"2025-05-01T00:00:52","modified_gmt":"2025-05-01T00:00:52","slug":"qwen-3%e5%8f%91%e5%b8%83%ef%bc%9a235b%e6%a8%a1%e5%9e%8b%e6%80%a7%e8%83%bd%e8%b6%85%e8%b6%8ar1%e3%80%81grok%e5%92%8co1%ef%bc%8c%e9%87%87%e7%94%a8apache-2-0%e8%ae%b8%e5%8f%af%e8%af%81","status":"publish","type":"post","link":"https:\/\/www.myshirtai.com\/es\/archives\/6380","title":{"rendered":"Qwen 3: el modelo 235B supera a R1, Grok y o1 con licencia Apache 2.0"},"content":{"rendered":"<p>Recientemente, el equipo de Ali Tongyi Thousand Questions lanz\u00f3 una nueva generaci\u00f3n del modelo de gran tama\u00f1o Qwen 3, que encabez\u00f3 el trono mundial de los modelos de c\u00f3digo abierto desde su lanzamiento. En comparaci\u00f3n con su predecesor, Qwen 3 ha logrado avances significativos en la capacidad de inferencia, el soporte multiling\u00fce y el coste de despliegue, etc. El rendimiento de su modelo insignia Qwen3-235B-A22B es comparable o incluso supera al de los mejores modelos como DeepSeek-R1, o1 de OpenAI, o3-mini, Grok-3 de XAI y Gemini-2.5-Pro de Google. modelos.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/school.myshirtai.com\/wp-content\/uploads\/2025\/04\/2025-04-29_fbc95297e4e947fec8d8f0ef42ae6c07_5.jpeg\" alt=\"\" class=\"wp-image-405\"\/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-\u5168\u9762\u5f00\u6e90\u7684qwen-3\u5bb6\u65cf\">Familia Qwen 3 de c\u00f3digo abierto<\/h2>\n\n\n\n<p>La familia de modelos Qwen 3 sigue siendo de c\u00f3digo abierto bajo el relajado protocolo Apache 2.0, que permite a desarrolladores, organizaciones de investigaci\u00f3n y empresas de todo el mundo descargar y comercializar los modelos de forma gratuita. La familia Qwen 3 de c\u00f3digo abierto incluye dos modelos MoE y seis modelos densos:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Modelo ME<\/strong>::\n<ul class=\"wp-block-list\">\n<li>Qwen3-235B-A22B (235B en total, 22B activados)<\/li>\n\n\n\n<li>Qwen3-30B-A3B (n\u00famero total de participantes 30B, n\u00famero de participantes activados 3B)<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>modelizaci\u00f3n intensiva<\/strong>::\n<ul class=\"wp-block-list\">\n<li>Qwen3-32B<\/li>\n\n\n\n<li>Qwen3-14B<\/li>\n\n\n\n<li>Qwen3-8B<\/li>\n\n\n\n<li>Qwen3-4B<\/li>\n\n\n\n<li>Qwen3-1.7B<\/li>\n\n\n\n<li>Qwen3-0,6B<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/school.myshirtai.com\/wp-content\/uploads\/2025\/04\/2025-04-29_a09919b14a61ba9a2e8fd1f3c0981e0d_1.png\" alt=\"\" class=\"wp-image-403\"\/><\/figure>\n\n\n\n<p>Cabe destacar que, aunque Qwen3-235B-A22B tiene un n\u00famero total de referencias mucho mayor que otros modelos de c\u00f3digo abierto, su coste de implantaci\u00f3n real es dr\u00e1sticamente inferior: s\u00f3lo se necesitan cuatro H20 para implantar la versi\u00f3n completa, y la huella de memoria de v\u00eddeo es s\u00f3lo un tercio de la de un modelo con un rendimiento similar.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-\u6a2a\u626b\u5404\u5927\u57fa\u51c6\u6d4b\u8bd5\u7684\u5353\u8d8a\u6027\u80fd\">Rendimiento superior en los principales \u00edndices de referencia<\/h2>\n\n\n\n<p>La serie Qwen 3 ha obtenido buenos resultados en diversas revisiones profesionales y ha batido varios r\u00e9cords de modelos de c\u00f3digo abierto:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Qwen3 obtuvo 81,5 puntos en la evaluaci\u00f3n AIME25 de nivel OU, \u00a1lo que supone un nuevo r\u00e9cord de c\u00f3digo abierto!<\/li>\n\n\n\n<li>En la prueba LiveCodeBench, que eval\u00faa la capacidad del c\u00f3digo, Qwen3 supera la marca de 70 puntos y supera a Grok-3.<\/li>\n\n\n\n<li>Qwen3 super\u00f3 a OpenAI-o1 y a DeepSeek-R1 con una puntuaci\u00f3n de 95,6 en la medida ArenaHard, que eval\u00faa la alineaci\u00f3n del modelo con las preferencias humanas.<\/li>\n\n\n\n<li>En el examen BFCL, que eval\u00faa la capacidad de agente de un modelo, Qwen3 alcanz\u00f3 un nuevo m\u00e1ximo de 70,8 puntos, superando a modelos de primera fila como Gemini2.5-Pro y OpenAI-o1.<\/li>\n<\/ul>\n\n\n\n<p>Incluso los modelos m\u00e1s peque\u00f1os, como Qwen3-4B, igualan el rendimiento de Qwen2.5-72B-Instruct, lo que demuestra un aumento significativo de la eficiencia. El modelo MoE m\u00e1s peque\u00f1o, Qwen3-30B-A3B, s\u00f3lo tiene una d\u00e9cima parte del n\u00famero de par\u00e1metros activados de QwQ-32B, pero su rendimiento es a\u00fan mejor.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/school.myshirtai.com\/wp-content\/uploads\/2025\/04\/2025-04-29_dd6bf3067caacc1f73202787d93d63d6_6.jpeg\" alt=\"\" class=\"wp-image-407\"\/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-\u7a81\u7834\u6027\u7684-\u6df7\u5408\u63a8\u7406-\u6a21\u5f0f\">Innovador modelo de \"razonamiento h\u00edbrido<\/h2>\n\n\n\n<p>Una de las mayores innovaciones de Qwen3 es la introducci\u00f3n del modo de \"razonamiento mixto\", que permite alternar sin problemas entre los modos de pensar y no pensar:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>patr\u00f3n de pensamiento<\/strong>Modelizaci\u00f3n del razonamiento paso a paso para dar una respuesta final tras una cuidadosa consideraci\u00f3n, adecuada para problemas complejos que requieren una reflexi\u00f3n en profundidad.<\/li>\n\n\n\n<li><strong>modus vivendi<\/strong>Modelos de respuesta r\u00e1pida y casi instant\u00e1nea para problemas sencillos en los que se requiere rapidez por encima de la profundidad.<\/li>\n<\/ul>\n\n\n\n<p>Los usuarios pueden controlar de forma flexible el proceso de inferencia del modelo en funci\u00f3n de la complejidad de la tarea, e incluso establecer el \"presupuesto de pensamiento\" (es decir, el n\u00famero de tokens que se espera pensar con la m\u00e1xima profundidad) para encontrar el mejor equilibrio entre rendimiento y coste. Las pruebas comparativas muestran que el modo think mejora significativamente el rendimiento del modelo en tareas como AIME24, AIME25, LiveCodeBech (v5) y GPQA Diamond.<\/p>\n\n\n\n<p>Ali ofrece un sencillo mecanismo de cambio suave que permite al usuario controlar din\u00e1micamente el modo de pensamiento del modelo a\u00f1adiendo etiquetas \"\/pensar\" y \"\/no_pensar\" al di\u00e1logo.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/school.myshirtai.com\/wp-content\/uploads\/2025\/04\/2025-04-29_bba5fe7a95988c09473c64ceca8b838b_15.png\" alt=\"\" class=\"wp-image-404\"\/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-\u591a\u8bed\u8a00\u652f\u6301\u4e0eagent\u80fd\u529b\u589e\u5f3a\">Compatibilidad con varios idiomas y mejora de las capacidades de los agentes<\/h2>\n\n\n\n<p>El modelo Qwen3 admite 119 idiomas y dialectos, lo que ampl\u00eda considerablemente su potencial de aplicaci\u00f3n global. Al mismo tiempo, se han mejorado notablemente las capacidades de agente y c\u00f3digo del modelo:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Compatibilidad nativa con el protocolo MCP<\/li>\n\n\n\n<li>Potentes funciones de invocaci\u00f3n de herramientas<\/li>\n\n\n\n<li>Trabajar con el marco Qwen-Agent para reducir en gran medida la complejidad de la codificaci\u00f3n.<\/li>\n\n\n\n<li>Alcanza un rendimiento destacado en tareas complejas basadas en las inteligencias<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-video\"><video controls src=\"https:\/\/school.myshirtai.com\/wp-content\/uploads\/2025\/04\/qwen3-mcp.mp4\"><\/video><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-\u5f3a\u5927\u6280\u672f\u57fa\u7840-36\u4e07\u4ebftoken\u9884\u8bad\u7ec3\">S\u00f3lida base t\u00e9cnica: 36 billones de tokens de formaci\u00f3n previa<\/h2>\n\n\n\n<p>El rendimiento superior de Qwen3 se basa en enormes datos de entrenamiento y en un proceso de formaci\u00f3n bien dise\u00f1ado:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>El volumen de datos de preentrenamiento alcanza los 36 billones de tokens, casi el doble que Qwen 2.5<\/li>\n\n\n\n<li>Cobertura de 119 lenguas y dialectos<\/li>\n\n\n\n<li>Informaci\u00f3n de alta calidad extra\u00edda de documentos como PDF, adem\u00e1s de datos web<\/li>\n\n\n\n<li>Generaci\u00f3n de grandes cantidades de datos sint\u00e9ticos con Qwen2.5-Math y Qwen2.5-Coder para mejorar las capacidades matem\u00e1ticas y de codificaci\u00f3n.<\/li>\n<\/ul>\n\n\n\n<p>El proceso de formaci\u00f3n previa se divide en tres etapas:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Creaci\u00f3n de capacidades ling\u00fc\u00edsticas b\u00e1sicas: preentrenamiento en m\u00e1s de 30 billones de tokens con una longitud de contexto de 4K tokens.<\/li>\n\n\n\n<li>Optimizaci\u00f3n de la densidad de conocimiento: aumentar la proporci\u00f3n de datos para tareas STEM, de programaci\u00f3n y razonamiento, etc., y continuar el entrenamiento con 5 billones de tokens adicionales.<\/li>\n\n\n\n<li>Ampliaci\u00f3n de la capacidad de contexto: uso de datos de contexto largos de alta calidad para ampliar la longitud del contexto a 32.000 tokens.<\/li>\n<\/ol>\n\n\n\n<p>La fase posterior al entrenamiento utiliza un proceso de cuatro fases que incluye el arranque en fr\u00edo de la cadena de pensamiento larga, el aprendizaje de refuerzo de la cadena de pensamiento larga, la fusi\u00f3n de patrones de pensamiento y el aprendizaje de refuerzo gen\u00e9rico para crear modelos h\u00edbridos capaces tanto de razonamiento complejo como de respuesta r\u00e1pida.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/school.myshirtai.com\/wp-content\/uploads\/2025\/04\/2025-04-29_1aee3275c5ff3caeb87b0637c5c41fd3_18-1.png\" alt=\"\" class=\"wp-image-411\"\/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-\u793e\u533a\u53cd\u54cd\u4e0e\u5b9e\u9645\u4f53\u9a8c\">Respuesta comunitaria y experiencia pr\u00e1ctica<\/h2>\n\n\n\n<p>El c\u00f3digo abierto de Qwen3 se abri\u00f3 en menos de 3 horas y GitHub acumul\u00f3 17.000 estrellas, lo que provoc\u00f3 una respuesta abrumadora de la comunidad de c\u00f3digo abierto. Awni Hannun, ingeniero de Apple, anunci\u00f3 que Qwen3 ya es compatible con el marco MLX, lo que permite que todo tipo de dispositivos Apple, desde el iPhone hasta el M2\/M3 Ultra, ejecuten de forma nativa modelos Qwen3 de diferentes especificaciones.<\/p>\n\n\n\n<p>Diversas pruebas reales han demostrado que Qwen3 puede enfrentarse f\u00e1cilmente a problemas de razonamiento complejos, como pruebas matem\u00e1ticas y tareas de programaci\u00f3n. Por ejemplo, en una tarea de programaci\u00f3n compleja (escribir un juego de Snake con una funci\u00f3n de persecuci\u00f3n de Pinto), Qwen3-235B-A22B proporcion\u00f3 c\u00f3digo ejecutable en solo unos 3 minutos.<\/p>\n\n\n\n<figure class=\"wp-block-video\"><video controls src=\"https:\/\/school.myshirtai.com\/wp-content\/uploads\/2025\/04\/\u5e73\u5934\u54e5\u8d2a\u5403\u86c7.mp4\"><\/video><\/figure>\n\n\n\n<p>Algunos usuarios lo han probado y han descubierto que, comparado con el modelo Llama con el mismo n\u00famero de par\u00e1metros, Qwen3 muestra ventajas significativas, razonando m\u00e1s profundamente, manteniendo contextos m\u00e1s largos y resolviendo problemas m\u00e1s dif\u00edciles.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-\u4f7f\u7528\u6307\u5357\">Normas de uso<\/h2>\n\n\n\n<p>El modelo Qwen3 ya est\u00e1 disponible en l\u00ednea en la comunidad MagicBuilder, Hugging Face y GitHub:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Experiencia en l\u00ednea:<a href=\"https:\/\/chat.qwen.ai\/\">https:\/\/chat.qwen.ai\/<\/a><\/li>\n\n\n\n<li>Comunidad de Partidos de Magic:<a href=\"https:\/\/modelscope.cn\/collections\/Qwen3-9743180bdc6b48\">https:\/\/modelscope.cn\/collections\/Qwen3-9743180bdc6b48<\/a><\/li>\n\n\n\n<li>Cara de abrazo:<a href=\"https:\/\/huggingface.co\/collections\/Qwen\/qwen3-67dd247413f0e2e4f653967f\">https:\/\/huggingface.co\/collections\/Qwen\/qwen3-67dd247413f0e2e4f653967f<\/a><\/li>\n\n\n\n<li>GitHub:<a href=\"https:\/\/github.com\/QwenLM\/Qwen3\">https:\/\/github.com\/QwenLM\/Qwen3<\/a><\/li>\n<\/ul>\n\n\n\n<p>Para el despliegue, se recomiendan oficialmente marcos como SGLang y vLLM; para el uso local, se recomiendan herramientas como Ollama, LMStudio, MLX, llama.cpp y KTransformers.<\/p>\n\n\n\n<p>Estas herramientas garantizan que los usuarios puedan integrar f\u00e1cilmente Qwen3 en diversos flujos de trabajo, ya sea para entornos de investigaci\u00f3n, desarrollo o producci\u00f3n. A continuaci\u00f3n se muestra un ejemplo est\u00e1ndar de uso de la biblioteca de transformadores:<\/p>\n\n\n\n<div class=\"wp-block-code\"><div class=\"xhcode-toolbar\"><i class=\"xhcode-icon-codesvg\"><\/i><span>PHP<\/span><\/div><pre><code lang=\"php\" class=\"language-php\">from modelscope import AutoModelForCausalLM, AutoTokenizer\n\nmodel_name = \"Qwen\/Qwen3-30B-A3B\"\n# carga el tokenizador y el modelo\ntokenizador = AutoTokenizer.from_pretrained(nombre_modelo)\nmodel = AutoModelForCausalLM.from_pretrained(\n        nombre_modelo, tipo_antorcha=\"auto\")\n        torch_dtype=\"auto\",\n        device_map=\"auto\"\n)\n\n# preparar la entrada del modelo\nprompt = \"Dame una breve introducci\u00f3n a gran modelo de lenguaje\".\nmensajes = [\n        {\"rol\": \"usuario\", \"contenido\": prompt}\nmessages = [ {\"rol\": \"usuario\", \"contenido\": prompt} ]\ntext = tokenizer.apply_chat_template(\n        messages, tokenise=False, text = tokenizer.apply_chat_template(\n        tokenise=False,\n        add_generation_prompt=True, enable_thinking=True 1TP\n        enable_thinking=True # Cambiar entre los modos pensar y no pensar. Por defecto es True.\nPor defecto es True. )\n<\/code><\/pre><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-\u7ed3\u8bed\">observaciones finales<\/h2>\n\n\n\n<p>Hasta ahora, Ali Tongyi ha puesto en c\u00f3digo abierto m\u00e1s de 200 modelos, con m\u00e1s de 300 millones de descargas en todo el mundo y m\u00e1s de 100.000 modelos derivados de mil preguntas, superando a Llama en EE.UU. y convirti\u00e9ndose en el modelo de c\u00f3digo abierto n\u00famero 1 del mundo.La puesta en c\u00f3digo abierto de Qwen3 no s\u00f3lo supone otro gran avance en la tecnolog\u00eda china de IA, sino que tambi\u00e9n proporciona a la comunidad mundial de desarrolladores de IA una nueva y potente herramienta para promover la prosperidad del ecosistema de c\u00f3digo abierto.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/school.myshirtai.com\/wp-content\/uploads\/2025\/04\/2025-04-29_bcbf5967f33dfc583f699a4955fc6f4c_20.png\" alt=\"\" class=\"wp-image-410\"\/><\/figure>\n\n\n\n<table style=\"width: 100%;border-collapse: collapse;border: 1px solid #ddd\">\r\n<thead>\r\n<tr style=\"height: 48px;background-color: #f5f5f5\">\r\n<th style=\"width: 50%;height: 48px;border: 1px solid #ddd;padding: 8px\">\r\n<h4 style=\"margin: 0\">Para m\u00e1s productos, visite<\/h4>\r\n<\/th>\r\n<th style=\"width: 50%;height: 48px;border: 1px solid #ddd;padding: 8px\">\r\n<h4 style=\"margin: 0\">M\u00e1s informaci\u00f3n en<\/h4>\r\n<\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody>\r\n<tr style=\"height: 63px\">\r\n<td style=\"width: 50%;height: 63px;border: 1px solid #ddd;padding: 8px\"><a href=\"https:\/\/www.myshirtai.com\/es\/\" data-linktype=\"2\">ShirtAI - Inteligencia penetrante<\/a><\/td>\r\n<td style=\"width: 50%;height: 63px;border: 1px solid #ddd;padding: 8px\"><a href=\"https:\/\/www.myshirtai.com\/es\/archives\/4425\/\" data-linktype=\"2\">El Gran Modelo AIGC: el comienzo de una era de doble revoluci\u00f3n en ingenier\u00eda y ciencia - Inteligencia Penetrante<\/a><\/td>\r\n<\/tr>\r\n<tr style=\"height: 61px\">\r\n<td style=\"width: 50%;height: 61px;border: 1px solid #ddd;padding: 8px\"><a href=\"https:\/\/www.myshirtai.com\/es\/\" data-linktype=\"2\">Restauraci\u00f3n 1:1 de Claude y GPT Sitio web oficial - AI Cloud Native<\/a><\/td>\r\n<td style=\"width: 50%;height: 61px;border: 1px solid #ddd;padding: 8px\"><a href=\"https:\/\/www.bluelsqkj.com\/archives\/2876\" data-linktype=\"2\">Live Match App Global HD Sports Viewing Player (Recomendada) - Blueshirt Technology<\/a><\/td>\r\n<\/tr>\r\n<tr style=\"height: 54px\">\r\n<td style=\"width: 50%;height: 54px;border: 1px solid #ddd;padding: 8px\"><a href=\"https:\/\/api.mygptmeta.com\/\" data-linktype=\"2\">Servicio de tr\u00e1nsito basado en la API oficial - GPTMeta API<\/a><\/td>\r\n<td style=\"width: 50%;height: 54px;border: 1px solid #ddd;padding: 8px\"><a href=\"https:\/\/www.zhihu.com\/question\/621055223\/answer\/3633615705\" data-linktype=\"2\">Ayuda, \u00bfalguien de ustedes puede proporcionar algunos consejos sobre c\u00f3mo hacer preguntas en GPT? - Conocimientos<\/a><\/td>\r\n<\/tr>\r\n<tr style=\"height: 70px\">\r\n<td style=\"width: 50%;height: 70px;border: 1px solid #ddd;padding: 8px\"><a href=\"https:\/\/shop.blueshirtmap.com\/\" data-linktype=\"2\">Tienda digital global de bienes virtuales - Global SmarTone (Feng Ling Ge)<\/a><\/td>\r\n<td style=\"width: 50%;height: 70px;border: 1px solid #ddd;padding: 8px\"><a href=\"https:\/\/www.bilibili.com\/video\/BV1efpneYE54\/?spm_id_from=333.1387.homepage.video_card.click\" data-linktype=\"2\">\u00bfQu\u00e9 tan poderoso es Claude airtfacts caracter\u00edstica que GPT al instante no huele bien? -BeepBeep<\/a><\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>","protected":false},"excerpt":{"rendered":"<p>Ali Tongyi Qianqian equipo lanz\u00f3 una nueva generaci\u00f3n de c\u00f3digo abierto gran modelo Qwen3, encabez\u00f3 la lista mundial de modelos de c\u00f3digo abierto, la serie contiene modelos, el rendimiento del modelo insignia supera muchos modelos superiores, el despliegue es significativamente menor. La serie contiene modelos, el rendimiento del modelo insignia supera una serie de modelos superiores, el despliegue se reduce significativamente. qwen 3 en una serie de puntos de referencia para establecer un nuevo r\u00e9cord, y la innovadora introducci\u00f3n del modo de \"razonamiento h\u00edbrido\" el modelo soporta 119 idiomas, datos de pre-entrenamiento de hasta 36 token, la respuesta de la comunidad es entusiasta, dentro de tres horas para obtener la estrella k GitHub. El modelo soporta 119 idiomas con 36 token de datos de pre-entrenamiento, y recibi\u00f3 una estrella de GitHub en tres horas.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[53],"tags":[56,57],"class_list":["post-6380","post","type-post","status-publish","format-standard","hentry","category-blog","tag-open-source-models","tag-qwen"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v25.2) - 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