{"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\/pt\/archives\/6380","title":{"rendered":"Lan\u00e7amento do Qwen 3: o modelo 235B tem um desempenho superior ao do R1, Grok e o1 com licen\u00e7a Apache 2.0"},"content":{"rendered":"<p>Recentemente, a equipa do Ali Tongyi Thousand Questions lan\u00e7ou uma nova gera\u00e7\u00e3o do grande modelo Qwen 3, que ocupou o topo do trono global de modelos de c\u00f3digo aberto aquando do seu lan\u00e7amento. Em compara\u00e7\u00e3o com o seu antecessor, o Qwen 3 fez avan\u00e7os significativos na capacidade de infer\u00eancia, suporte multilingue e custo de implementa\u00e7\u00e3o, etc. O desempenho do seu modelo principal Qwen3-235B-A22B \u00e9 compar\u00e1vel ou at\u00e9 ultrapassa o de modelos de topo como o DeepSeek-R1, o o1 da OpenAI, o o3-mini, o Grok-3 da XAI e o Gemini-2.5-Pro da 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\">Fam\u00edlia Qwen 3 totalmente de c\u00f3digo aberto<\/h2>\n\n\n\n<p>A fam\u00edlia de modelos Qwen 3 continua a ser de fonte aberta ao abrigo do protocolo Apache 2.0, o que permite aos programadores, organiza\u00e7\u00f5es de investiga\u00e7\u00e3o e empresas de todo o mundo descarregar e comercializar os modelos gratuitamente. A fam\u00edlia Qwen 3 de c\u00f3digo aberto inclui dois modelos MoE e seis modelos densos:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Modelo do MdE<\/strong>::\n<ul class=\"wp-block-list\">\n<li>Qwen3-235B-A22B (235B total, 22B ativado)<\/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>modela\u00e7\u00e3o 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>\u00c9 de salientar que, embora o Qwen3-235B-A22B tenha um n\u00famero total de refer\u00eancias muito superior ao de outros modelos de c\u00f3digo aberto, o seu custo de implementa\u00e7\u00e3o efetivo \u00e9 drasticamente inferior - apenas s\u00e3o necess\u00e1rios quatro H20s para implementar a vers\u00e3o completa e a mem\u00f3ria de v\u00eddeo ocupa apenas um ter\u00e7o da de um modelo com desempenho semelhante.<\/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\">Desempenho superior em todos os par\u00e2metros de refer\u00eancia<\/h2>\n\n\n\n<p>A s\u00e9rie Qwen 3 teve um bom desempenho em v\u00e1rias an\u00e1lises profissionais e estabeleceu uma s\u00e9rie de recordes de modelos de c\u00f3digo aberto:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>O Qwen3 obteve 81,5 pontos na avalia\u00e7\u00e3o AIME25 no n\u00edvel OU, estabelecendo um novo recorde de c\u00f3digo aberto!<\/li>\n\n\n\n<li>Na avalia\u00e7\u00e3o do LiveCodeBench, que avalia a capacidade do c\u00f3digo, o Qwen3 ultrapassa a marca dos 70 pontos e supera o Grok-3.<\/li>\n\n\n\n<li>O Qwen3 superou o OpenAI-o1 e o DeepSeek-R1 com uma pontua\u00e7\u00e3o de 95,6 na medida ArenaHard, que avalia o alinhamento das prefer\u00eancias humanas do modelo<\/li>\n\n\n\n<li>Na an\u00e1lise BFCL, que avalia a capacidade de agente de um modelo, o Qwen3 atingiu um novo m\u00e1ximo de 70,8, ultrapassando modelos de topo como o Gemini2.5-Pro e o OpenAI-o1<\/li>\n<\/ul>\n\n\n\n<p>Mesmo os modelos mais pequenos, como o Qwen3-4B, igualam o desempenho do Qwen2.5-72B-Instruct, demonstrando ganhos de efici\u00eancia significativos. O modelo MoE mais pequeno Qwen3-30B-A3B tem apenas um d\u00e9cimo do n\u00famero de par\u00e2metros de ativa\u00e7\u00e3o do QwQ-32B, mas tem um desempenho ainda melhor.<\/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\">Modelo inovador de \"racioc\u00ednio misto\"<\/h2>\n\n\n\n<p>Uma das maiores inova\u00e7\u00f5es do Qwen3 \u00e9 a introdu\u00e7\u00e3o do modo de \"racioc\u00ednio misto\", que permite alternar sem problemas entre os modos de pensar e n\u00e3o pensar:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>padr\u00e3o de pensamento<\/strong>Modela\u00e7\u00e3o do racioc\u00ednio passo a passo para dar uma resposta final ap\u00f3s uma an\u00e1lise cuidadosa, adequada para problemas complexos que exigem uma reflex\u00e3o aprofundada<\/li>\n\n\n\n<li><strong>modus vivendi<\/strong>Os modelos fornecem uma resposta r\u00e1pida e quase instant\u00e2nea para problemas simples em que a velocidade \u00e9 necess\u00e1ria acima da profundidade<\/li>\n<\/ul>\n\n\n\n<p>Os utilizadores podem controlar de forma flex\u00edvel o processo de infer\u00eancia do modelo de acordo com a complexidade da tarefa e at\u00e9 definir o \"think budget\" (ou seja, o n\u00famero de tokens que se espera pensar com a profundidade m\u00e1xima) para encontrar o melhor equil\u00edbrio entre desempenho e custo. Os testes de refer\u00eancia mostram que o modo \"pensar\" melhora significativamente o desempenho do modelo em tarefas como o AIME24, o AIME25, o LiveCodeBech (v5) e o GPQA Diamond.<\/p>\n\n\n\n<p>Ali fornece um mecanismo simples de comuta\u00e7\u00e3o suave que permite ao utilizador controlar dinamicamente o modo de pensamento do modelo, adicionando etiquetas \"\/think\" e \"\/no_think\" ao 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\">Suporte multilingue e melhoria das capacidades do agente<\/h2>\n\n\n\n<p>O modelo Qwen3 suporta 119 l\u00ednguas e dialectos, alargando significativamente o seu potencial de aplica\u00e7\u00e3o global. Ao mesmo tempo, as capacidades de agente e de c\u00f3digo do modelo foram significativamente melhoradas:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Suporte nativo para o protocolo MCP<\/li>\n\n\n\n<li>Poderosas capacidades de invoca\u00e7\u00e3o de ferramentas<\/li>\n\n\n\n<li>Trabalhar com a estrutura Qwen-Agent para reduzir significativamente a complexidade da codifica\u00e7\u00e3o<\/li>\n\n\n\n<li>Atinge um desempenho de topo em tarefas complexas baseadas em intelig\u00eancias<\/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: pr\u00e9-forma\u00e7\u00e3o de 36 bili\u00f5es de fichas<\/h2>\n\n\n\n<p>O desempenho superior do Qwen3 baseia-se em enormes dados de forma\u00e7\u00e3o e num processo de forma\u00e7\u00e3o bem concebido:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>O volume de dados de pr\u00e9-treino atinge 36 trili\u00f5es de fichas, quase o dobro do Qwen 2.5<\/li>\n\n\n\n<li>Cobertura de 119 l\u00ednguas e dialectos<\/li>\n\n\n\n<li>Informa\u00e7\u00f5es de alta qualidade extra\u00eddas de documentos como PDFs, para al\u00e9m de dados da Web<\/li>\n\n\n\n<li>Gera\u00e7\u00e3o de grandes quantidades de dados sint\u00e9ticos com Qwen2.5-Math e Qwen2.5-Coder para capacidades matem\u00e1ticas e de c\u00f3digo melhoradas<\/li>\n<\/ul>\n\n\n\n<p>O processo de pr\u00e9-treino divide-se em tr\u00eas fases:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Cria\u00e7\u00e3o de capacidades lingu\u00edsticas b\u00e1sicas: pr\u00e9-treino em mais de 30 bili\u00f5es de tokens com comprimento de contexto de 4K tokens<\/li>\n\n\n\n<li>Otimiza\u00e7\u00e3o do conhecimento denso: aumentar a propor\u00e7\u00e3o de dados para tarefas STEM, de programa\u00e7\u00e3o e de racioc\u00ednio, etc., e continuar a forma\u00e7\u00e3o com mais 5 bili\u00f5es de fichas<\/li>\n\n\n\n<li>Extens\u00e3o da capacidade de contexto: utilizar dados de contexto longos de alta qualidade para aumentar o comprimento do contexto para 32K tokens<\/li>\n<\/ol>\n\n\n\n<p>A fase p\u00f3s-forma\u00e7\u00e3o utiliza um processo de quatro fases que inclui o arranque a frio da cadeia de pensamento longo, a aprendizagem por refor\u00e7o da cadeia de pensamento longo, a fus\u00e3o de padr\u00f5es de pensamento e a aprendizagem por refor\u00e7o gen\u00e9rico para criar modelos h\u00edbridos capazes de racioc\u00ednio complexo e resposta 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\">Resposta da comunidade e experi\u00eancia pr\u00e1tica<\/h2>\n\n\n\n<p>O Qwen3 foi aberto em menos de 3 horas, e o GitHub obteve 17 mil estrelas, desencadeando uma resposta esmagadora da comunidade de c\u00f3digo aberto. O engenheiro da Apple, Awni Hannun, anunciou que o Qwen3 \u00e9 agora suportado pela estrutura MLX, permitindo que todos os tipos de dispositivos Apple, desde o iPhone ao M2\/M3 Ultra, executem nativamente modelos Qwen3 de diferentes especifica\u00e7\u00f5es.<\/p>\n\n\n\n<p>V\u00e1rios testes do mundo real mostraram que o Qwen3 pode lidar facilmente com problemas de racioc\u00ednio complexos, como provas matem\u00e1ticas e tarefas de programa\u00e7\u00e3o. Por exemplo, numa tarefa de programa\u00e7\u00e3o complexa (escrever um jogo Snake com uma funcionalidade de persegui\u00e7\u00e3o ao Pinto), o Qwen3-235B-A22B forneceu c\u00f3digo execut\u00e1vel em apenas cerca de 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>Alguns utilizadores testaram-no e verificaram que, em compara\u00e7\u00e3o com o modelo Llama com o mesmo n\u00famero de par\u00e2metros, o Qwen3 apresenta vantagens significativas, raciocinando mais profundamente, mantendo contextos mais longos e resolvendo problemas mais dif\u00edceis.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-\u4f7f\u7528\u6307\u5357\">Diretrizes de utiliza\u00e7\u00e3o<\/h2>\n\n\n\n<p>O modelo Qwen3 est\u00e1 agora dispon\u00edvel em linha na comunidade MagicBuilder, Hugging Face e GitHub:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Experi\u00eancia online:<a href=\"https:\/\/chat.qwen.ai\/\">https:\/\/chat.qwen.ai\/<\/a><\/li>\n\n\n\n<li>Comunidade do Magic Match:<a href=\"https:\/\/modelscope.cn\/collections\/Qwen3-9743180bdc6b48\">https:\/\/modelscope.cn\/collections\/Qwen3-9743180bdc6b48<\/a><\/li>\n\n\n\n<li>Cara de abra\u00e7o:<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 implanta\u00e7\u00e3o, estruturas como SGLang e vLLM s\u00e3o oficialmente recomendadas; para uso local, ferramentas como Ollama, LMStudio, MLX, llama.cpp e KTransformers s\u00e3o recomendadas.<\/p>\n\n\n\n<p>Estas ferramentas asseguram que os utilizadores podem facilmente integrar o Qwen3 numa variedade de fluxos de trabalho, seja para ambientes de investiga\u00e7\u00e3o, desenvolvimento ou produ\u00e7\u00e3o. Um exemplo padr\u00e3o de uso da biblioteca de transformadores \u00e9 mostrado abaixo:<\/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\nnome_do_modelo = \"Qwen\/Qwen3-30B-A3B\"\n# carregar o tokenizador e o modelo\ntokeniser = AutoTokenizer.from_pretrained(nome_do_modelo)\nmodel = AutoModelForCausalLM.from_pretrained(\n        nome_do_modelo, torch_dtype=\"auto\")\n        torch_dtype=\"auto\",\n        device_map=\"auto\"\n)\n\n# preparar a entrada do modelo\nprompt = \"D\u00ea-me uma breve introdu\u00e7\u00e3o ao modelo de linguagem grande\".\nmensagens = [\n        {\"role\": \"user\", \"content\": prompt}\nmensagens = [ {\"role\": \"user\", \"content\": prompt} ]\ntext = tokenizer.apply_chat_template(\n        mensagens, tokenise=False, text = tokenizer.apply_chat_template(\n        tokenise=False,\n        add_generation_prompt=True, enable_thinking=True 1TP\n        enable_thinking=True # Alternar entre os modos de pensar e n\u00e3o pensar. A predefini\u00e7\u00e3o \u00e9 Verdadeiro.\nA predefini\u00e7\u00e3o \u00e9 Verdadeiro.)\n<\/code><\/pre><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-\u7ed3\u8bed\">observa\u00e7\u00f5es finais<\/h2>\n\n\n\n<p>At\u00e9 \u00e0 data, Ali Tongyi j\u00e1 disponibilizou mais de 200 modelos, com mais de 300 milh\u00f5es de descargas a n\u00edvel mundial e mais de 100 000 modelos derivados de mil perguntas, ultrapassando Llama nos EUA para se tornar o modelo de fonte aberta n.\u00ba 1 do mundo. A disponibiliza\u00e7\u00e3o do Qwen3 n\u00e3o s\u00f3 marca outro grande avan\u00e7o na tecnologia de IA da China, como tamb\u00e9m fornece \u00e0 comunidade global de programadores de IA uma nova e poderosa ferramenta para promover a prosperidade do ecossistema de fonte aberta.<\/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 mais produtos, consultar<\/h4>\r\n<\/th>\r\n<th style=\"width: 50%;height: 48px;border: 1px solid #ddd;padding: 8px\">\r\n<h4 style=\"margin: 0\">Ver mais em<\/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\/pt\/\" data-linktype=\"2\">ShirtAI - Intelig\u00eancia penetrante<\/a><\/td>\r\n<td style=\"width: 50%;height: 63px;border: 1px solid #ddd;padding: 8px\"><a href=\"https:\/\/www.myshirtai.com\/pt\/archives\/4425\/\" data-linktype=\"2\">O Grande Modelo do AIGC: inaugurando uma era de dupla revolu\u00e7\u00e3o na engenharia e na ci\u00eancia - Penetrating Intelligence<\/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\/pt\/\" data-linktype=\"2\">1:1 Restaura\u00e7\u00e3o de Claude e GPT Site 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\">Aplica\u00e7\u00e3o de jogos em direto Leitor de visualiza\u00e7\u00e3o de desporto HD global (recomendado) - 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\">Servi\u00e7o de tr\u00e2nsito baseado na API oficial - API GPTMeta<\/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\">Ajuda, algu\u00e9m pode dar algumas dicas sobre como fazer perguntas no GPT? - Conhecimento<\/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\">Loja digital de bens virtuais globais - 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\">Qu\u00e3o poderosa \u00e9 a funcionalidade Claude airtfacts que o GPT instantaneamente n\u00e3o cheira bem? -BeepBeep<\/a><\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>","protected":false},"excerpt":{"rendered":"<p>A equipa de Ali Tongyi Qianqian lan\u00e7ou uma nova gera\u00e7\u00e3o de modelos de grande dimens\u00e3o de fonte aberta Qwen3, que encabe\u00e7ou a lista global de modelos de fonte aberta, a s\u00e9rie cont\u00e9m modelos, o desempenho do modelo principal excede muitos modelos de topo, a implanta\u00e7\u00e3o \u00e9 significativamente menor. A s\u00e9rie cont\u00e9m modelos, o desempenho do modelo principal excede uma s\u00e9rie de modelos de topo, a implanta\u00e7\u00e3o \u00e9 significativamente reduzida. qwen 3 em uma s\u00e9rie de benchmarks para estabelecer um novo recorde, e a introdu\u00e7\u00e3o inovadora do modo de \"racioc\u00ednio h\u00edbrido\" o modelo suporta 119 idiomas, dados de pr\u00e9-treinamento at\u00e9 36 token, a resposta da comunidade \u00e9 entusi\u00e1stica, dentro de tr\u00eas horas para obter a estrela k GitHub. O modelo suporta 119 idiomas com 36 tokens de dados de pr\u00e9-treinamento e recebeu uma estrela do GitHub em tr\u00eas 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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