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战斗力指数爆表高达 10000
IP属地:北京
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long or short
04-29 13:34
$老虎证券(TIGR)$
这波虎子要偷偷突破 10 块吗
long or short
04-29 13:31
牛
清晨重磅!阿里发布并开源Qwen3,无缝集成思考模式、多语言、便于Agent调用
long or short
04-29 05:04
我平仓了50.0股
$英伟达(NVDA)$
,情况不是很对劲,先跑路跑路,盘前再看看机会,能不能买进去
long or short
04-29 03:57
我开仓了50.0股
$英伟达(NVDA)$
,夜跑买入英伟达,后期继续看好,看到是150,英伟达加油加油
long or short
04-29 03:56
我开仓了1手
$NVDA 20250502 101.0 PUT$
,随便买入一些期权!后期继续看好英伟达,101 价格很安全,,,,
long or short
04-25
我平仓了100.0股
$京东(JD)$
,好像还是突破不了,为了避免一手大坑,还是先走为妙
long or short
04-25
$NVIDIA(NVDA)$
够强
long or short
04-25
我开仓了5.0股
$微软(MSFT)$
,多交易多分享,多抽奖,看看能不能抽中 300 美元大奖,哈哈哈😂😂😂
long or short
04-25
我开仓了100.0股
$京东(JD)$
,买一点京东,支持下东哥,随时外卖烧钱,特别是现在搞补贴
long or short
04-25
我开仓了10.0股
$微软(MSFT)$
,多交易多分享,多抽奖,看看能不能抽中 300 美元大奖,哈哈哈😂
long or short
04-25
我平仓了100.0股
$英伟达(NVDA)$
,达子达子!英伟达还是这么给力!给了我机会跑路!等盘中回落再冲
long or short
04-25
我平仓了1手
$NVDA 20250425 105.0 CALL$
,最后一天,不想持有了,走人走人,没想到达子还是这么的强势!
long or short
04-25
我开仓了100.0股
$英伟达(NVDA)$
,夜盘 夜盘,加一点点仓,没想到夜盘达子也能上涨这么多!
long or short
04-24
我平仓了100.0股
$英伟达(NVDA)$
,平仓睡大觉,晚点再看看情况,要不要接回来,周五了
long or short
04-24
下个月了再加油
long or short
04-24
我开仓了100.0股
$英伟达(NVDA)$
,今天运气真好,交易分享抽奖抽中了 50 美元💵,太开心了,不枉我多多练习多分享
long or short
04-23
我平仓了20.0股
$微软(MSFT)$
,盘前盘前又又卖飞了,看看能不能再低价接回来,等待
long or short
04-23
我平仓了1手
$NVDA 20250425 103.0 PUT$
,没啥好说的,换成了下周的 put.免得出一些幺蛾子,看看今晚还会不会涨
long or short
04-22
我平仓了15.0股
$微软(MSFT)$
,反弹来的有点小快,先 t 一部分,后面看看情况,再决定咋不要加回去
long or short
04-22
我平仓了1.0组
$英伟达 DIAGONAL 250502/250425 PUT 99.0/PUT 102.0$
,roll 一下,只要不大跌,这次应该能打平,权利金刚好覆盖股价下跌
去老虎APP查看更多动态
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07:00","market":"us","language":"zh","title":"清晨重磅!阿里发布并开源Qwen3,无缝集成思考模式、多语言、便于Agent调用","url":"https://stock-news.laohu8.com/highlight/detail?id=2531930992","media":"市场资讯","summary":"$阿里巴巴$周一发布并开源通义千问3.0系列模型,并称其在数学和编程等多个方面均可与DeepSeek的性能相媲美。与其他主流模型相比,Qwen3还显著降低了部署成本。阿里表示,Qwen3无缝集成两种思考模式,支持119种语言,便于Agent调用。阿里巴巴称,Qwen3的训练数据包括教材、问答对、代码片段等多种内容。据介绍,Qwen3预训练过程分为三个阶段。目前,阿里巴巴正以Qwen为核心,构建其AI版图。","content":"<html><head></head><body><p> <a href=\"https://laohu8.com/S/BABA\">阿里巴巴</a>周一发布并开源通义千问3.0(Qwen3)系列模型,并称其在数学和编程等多个方面均可与DeepSeek的性能相媲美。与其他主流模型相比,Qwen3还显著降低了部署成本。阿里表示,Qwen3无缝集成两种思考模式,支持119种语言,便于Agent调用。</p><p class=\"t-img-caption\"><img src=\"https://community-static.tradeup.com/news/f8ccbeb9e8455a038ca3ef6565c82898\" tg-width=\"550\" tg-height=\"283\"/></p><p> 性能媲美DeepSeek R1、OpenAI o1,全部开源</p><p> Qwen3系列包括两个专家混合 (MoE) 模型和另外六个模型。阿里巴巴表示,最新发型的旗舰模型Qwen3-235B-A22B在代码、数学、通用能力等基准测试中,与DeepSeek-R1、o1、o3-mini、Grok-3和Gemini-2.5-Pro等顶级模型相比,表现出极具竞争力。</p><p> 此外,被称为“专家混合”(MoE,Mixture-of-Experts)模型的Qwen3-30B-A3B的激活参数数量是QwQ-32B的10%,表现更胜一筹,甚至像Qwen3-4B这样的小模型也能匹敌Qwen2.5-72B-Instruct的性能。这类系统模拟人类解决问题的思维方式,将任务划分为更小的数据集,类似于让一组各有所长的专家分别负责不同部分,从而提升整体效率。</p><p class=\"t-img-caption\"><img src=\"https://community-static.tradeup.com/news/d97424de634e7f31ee4589c2aee1b897\" tg-width=\"550\" tg-height=\"310\"/></p><p class=\"t-img-caption\"><img src=\"https://community-static.tradeup.com/news/65fb3fcd877483917690160dcd2250a7\" tg-width=\"550\" tg-height=\"310\"/></p><p> 同时,阿里巴巴还开源了两个MoE模型的权重:拥有2350多亿总参数和220多亿激活参数的Qwen3-235B-A22B,以及拥有约300亿总参数和30亿激活参数的小型MoE 模型Qwen3-30B-A3B。此外,六个Dense模型也已开源,包括Qwen3-32B、Qwen3-14B、Qwen3-8B、Qwen3-4B、Qwen3-1.7B和Qwen3-0.6B,均在Apache 2.0许可下开源。</p><p class=\"t-img-caption\"><img src=\"https://community-static.tradeup.com/news/f1f1a3c34174865a6754c9ed5a5d8fda\" tg-width=\"550\" tg-height=\"290\"/></p><p class=\"t-img-caption\"><img src=\"https://community-static.tradeup.com/news/ad74a2913c1f4020630d398c81f80e71\" tg-width=\"550\" tg-height=\"185\"/></p><p> “混合型”模型,两种思考模式</p><p> 阿里巴巴表示,Qwen 3系列是“混合型”模型,既可以花时间“推理”以解决复杂问题,也可以快速回答简单请求,分别叫做“思考模式”和“非思考模式”。“思考模式”中的推理能力使得模型能够有效地进行自我事实核查,类似于OpenAI的o3模型,但代价是推理过程中的延迟时间较高。</p><p> Qwen团队在博客文章中写道:</p><blockquote><p>这种灵活性使用户能够根据具体任务控制模型进行“思考”的程度。例如,复杂的问题可以通过扩展推理步骤来解决,而简单的问题则可以直接快速作答,无需延迟。</p><p>至关重要的是,这两种模式的结合大大增强了模型实现稳定且高效的“思考预算”控制能力。如上文所述,Qwen3展现出可扩展且平滑的性能提升,这与分配的计算推理预算直接相关。</p><p>这样的设计让用户能够更轻松地为不同任务配置特定的预算,在成本效益和推理质量之间实现更优的平衡。</p></blockquote><p class=\"t-img-caption\"><img src=\"https://community-static.tradeup.com/news/df695e8a53d40062e329714b9d8ee90e\" tg-width=\"550\" tg-height=\"341\"/></p><p> 训练数据量是Qwen2.5的两倍,便于Agent调用</p><p> 阿里巴巴表示,Qwen3系列支持119种语言,并基于近36万亿个token(标记)进行训练,使用的数据量是Qwen2.5的两倍。Token是模型处理的基本数据单元,约100万个token相当于75万英文单词。阿里巴巴称,Qwen3的训练数据包括教材、问答对、代码片段等多种内容。</p><p> 据介绍,Qwen3预训练过程分为三个阶段。在第一阶段(S1),模型在超过30万亿个token上进行了预训练,上下文长度为4K token。这一阶段为模型提供了基本的语言技能和通用知识。</p><p> 在第二阶段(S2),训练则通过增加知识密集型数据(如 STEM、编程和推理任务)的比例来改进数据集,随后模型又在额外的5万亿个token上进行了预训练。在最后阶段则使用高质量的长上下文数据将上下文长度扩展到32K token,确保模型能够有效地处理更长的输入。</p><p class=\"t-img-caption\"><img src=\"https://community-static.tradeup.com/news/def960fe0ae9611c8302ccbd3837bd7b\" tg-width=\"550\" tg-height=\"374\"/></p><p> 阿里巴巴表示,由于模型架构的改进、训练数据的增加以及更有效的训练方法,Qwen3 Dense基础模型的整体性能与参数更多的Qwen2.5基础模型相当。例如,Qwen3-1.7B/4B/8B/14B/32B-Base分别与Qwen2.5-3B/7B/14B/32B/72B-Base 表现相当。特别是在 STEM、编码和推理等领域,Qwen3 Dense基础模型的表现甚至超过了更大规模的Qwen2.5 模型。对于Qwen3 MoE基础模型,它们在仅使用10%激活参数的情况下达到了与Qwen2.5 Dense基础模型相似的性能,显著节省了训练和推理成本。</p><p> 而在后训练阶段,阿里使用多样的的长思维链数据对模型进行了微调,涵盖了数学、代码、逻辑推理和 STEM 问题等多种任务和领域,为模型配备基本的推理能力。然后通过大规模强化学习,利用基于规则的奖励来增强模型的探索和钻研能力。</p><p> 阿里巴巴表示,Qwen3在调用工具(tool-calling)、执行指令以及复制特定数据格式等能力方面表现出色,推荐用户使用Qwen-Agent来充分发挥Qwen3的Agent能力。Qwen-Agent内部封装了工具调用模板和工具调用解析器,大大降低了代码复杂性。</p><p> 除了提供下载版本外,Qwen3还可以通过Fireworks AI、Hyperbolic等云服务提供商使用。</p><p> 目标仍对准AGI</p><p> OpenAI、<a href=\"https://laohu8.com/S/GOOG\">谷歌</a>和Anthropic近期也陆续推出了多款新模型。OpenAI近日表示,也计划在未来几个月发布一款更加“开放”的模型,模仿人类推理方式,这标志着其策略出现转变,此前DeepSeek和阿里巴巴已经率先推出了开源AI系统。</p><p> 目前,阿里巴巴正以Qwen为核心,构建其AI版图。今年2月,首席执行官吴泳铭表示,公司目前的“首要目标”是实现通用人工智能(AGI)——即打造具备人类智力水平的AI系统。</p><p> 阿里表示,Qwen3代表了该公司在通往通用人工智能(AGI)和超级人工智能(ASI)旅程中的一个重要里程碑。展望未来,阿里计划从多个维度提升模型,包括优化模型架构和训练方法,以实现几个关键目标:扩展数据规模、增加模型大小、延长上下文长度、拓宽模态范围,并利用环境反馈推进强化学习以进行长周期推理。</p><p> 开源社区振奋</p><p> 阿里Qwen3的发布让AI社区感到激动,有网友献上经典Meme:</p><p class=\"t-img-caption\"><img src=\"https://community-static.tradeup.com/news/37010a2f029ab947cf2d94ccd84dd1ee\" tg-width=\"550\" tg-height=\"422\"/></p><p> 有网友说,</p><blockquote><p>在我的测试中,235B在高维张量运算方面的表现相当于Sonnet。</p><p>这是一个非常出色的模型,</p><p>感谢你们。</p></blockquote><p class=\"t-img-caption\"><img src=\"https://community-static.tradeup.com/news/47dfd4bcc5836d2abca139a7542e89b9\" tg-width=\"550\" tg-height=\"167\"/></p><p> 有网友对Qwen3赞不绝口:</p><blockquote><p>如果不是亲眼看到屏幕上实时生成的tokens,我根本不会相信那些基准测试结果。???? 简直像魔法一样????</p></blockquote><p class=\"t-img-caption\"><img src=\"https://community-static.tradeup.com/news/0ec359e6a46b35b95a40e0a98d68444e\" tg-width=\"550\" tg-height=\"92\"/></p><p> 而开源AI的支持者则更加兴奋。有网友说:</p><blockquote><p>“有了一个开源32B大模型,性能跟Gemini 2.5 Pro不相上下。”</p><p>“我们彻底杀回来了!”</p></blockquote><p class=\"t-img-caption\"><img src=\"https://community-static.tradeup.com/news/f51dacd5e0b9425e6ada252e378b8d44\" tg-width=\"550\" tg-height=\"139\"/></p><p> 网友感谢阿里积极推动开源:</p><p class=\"t-img-caption\"><img src=\"https://community-static.tradeup.com/news/1e18b5c0eaadec78dbcaa2bbc36caf7d\" tg-width=\"550\" tg-height=\"92\"/></p><p></p><p></p></body></html>","source":"sina","collect":0,"html":"<!DOCTYPE html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n<meta name=\"viewport\" content=\"width=device-width,initial-scale=1.0,minimum-scale=1.0,maximum-scale=1.0,user-scalable=no\"/>\n<meta name=\"format-detection\" content=\"telephone=no,email=no,address=no\" 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.h-name {font-size: 13px; color: #eee; margin: 0;}\n.head .h-time {font-size: 11px; color: #7E829C; margin: 0;line-height: 11px;}\n.small {font-size: 12.5px; display: inline-block; transform: scale(0.9); -webkit-transform: scale(0.9); transform-origin: left; -webkit-transform-origin: left;}\n.smaller {font-size: 12.5px; display: inline-block; transform: scale(0.8); -webkit-transform: scale(0.8); transform-origin: left; -webkit-transform-origin: left;}\n.bt-text {font-size: 12px;margin: 1.5em 0 0 0}\n.bt-text p {margin: 0}\n</style>\n</head>\n<body>\n<div class=\"wrapper\">\n<header>\n<h2 class=\"title\">\n清晨重磅!阿里发布并开源Qwen3,无缝集成思考模式、多语言、便于Agent调用\n</h2>\n\n<h4 class=\"meta\">\n\n\n2025-04-29 07:00 北京时间 <a href=https://finance.sina.com.cn/world/2025-04-29/doc-ineuurue1312786.shtml><strong>市场资讯</strong></a>\n\n\n</h4>\n\n</header>\n<article>\n<div>\n<p>阿里巴巴周一发布并开源通义千问3.0(Qwen3)系列模型,并称其在数学和编程等多个方面均可与DeepSeek的性能相媲美。与其他主流模型相比,Qwen3还显著降低了部署成本。阿里表示,Qwen3无缝集成两种思考模式,支持119种语言,便于Agent调用。 性能媲美DeepSeek R1、OpenAI o1,全部开源 Qwen3系列包括两个专家混合 (MoE) 模型和另外六个模型。阿里巴巴表示,...</p>\n\n<a href=\"https://finance.sina.com.cn/world/2025-04-29/doc-ineuurue1312786.shtml\">Web Link</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"https://static.tigerbbs.com/05ddeb1f7be03fe06014ae549a8abe22","relate_stocks":{"09988":"阿里巴巴-W","BABA":"阿里巴巴"},"source_url":"https://finance.sina.com.cn/world/2025-04-29/doc-ineuurue1312786.shtml","is_english":false,"share_image_url":"https://static.laohu8.com/b0d1b7e8843deea78cc308b15114de44","article_id":"2531930992","content_text":"阿里巴巴周一发布并开源通义千问3.0(Qwen3)系列模型,并称其在数学和编程等多个方面均可与DeepSeek的性能相媲美。与其他主流模型相比,Qwen3还显著降低了部署成本。阿里表示,Qwen3无缝集成两种思考模式,支持119种语言,便于Agent调用。 性能媲美DeepSeek R1、OpenAI o1,全部开源 Qwen3系列包括两个专家混合 (MoE) 模型和另外六个模型。阿里巴巴表示,最新发型的旗舰模型Qwen3-235B-A22B在代码、数学、通用能力等基准测试中,与DeepSeek-R1、o1、o3-mini、Grok-3和Gemini-2.5-Pro等顶级模型相比,表现出极具竞争力。 此外,被称为“专家混合”(MoE,Mixture-of-Experts)模型的Qwen3-30B-A3B的激活参数数量是QwQ-32B的10%,表现更胜一筹,甚至像Qwen3-4B这样的小模型也能匹敌Qwen2.5-72B-Instruct的性能。这类系统模拟人类解决问题的思维方式,将任务划分为更小的数据集,类似于让一组各有所长的专家分别负责不同部分,从而提升整体效率。 同时,阿里巴巴还开源了两个MoE模型的权重:拥有2350多亿总参数和220多亿激活参数的Qwen3-235B-A22B,以及拥有约300亿总参数和30亿激活参数的小型MoE 模型Qwen3-30B-A3B。此外,六个Dense模型也已开源,包括Qwen3-32B、Qwen3-14B、Qwen3-8B、Qwen3-4B、Qwen3-1.7B和Qwen3-0.6B,均在Apache 2.0许可下开源。 “混合型”模型,两种思考模式 阿里巴巴表示,Qwen 3系列是“混合型”模型,既可以花时间“推理”以解决复杂问题,也可以快速回答简单请求,分别叫做“思考模式”和“非思考模式”。“思考模式”中的推理能力使得模型能够有效地进行自我事实核查,类似于OpenAI的o3模型,但代价是推理过程中的延迟时间较高。 Qwen团队在博客文章中写道:这种灵活性使用户能够根据具体任务控制模型进行“思考”的程度。例如,复杂的问题可以通过扩展推理步骤来解决,而简单的问题则可以直接快速作答,无需延迟。至关重要的是,这两种模式的结合大大增强了模型实现稳定且高效的“思考预算”控制能力。如上文所述,Qwen3展现出可扩展且平滑的性能提升,这与分配的计算推理预算直接相关。这样的设计让用户能够更轻松地为不同任务配置特定的预算,在成本效益和推理质量之间实现更优的平衡。 训练数据量是Qwen2.5的两倍,便于Agent调用 阿里巴巴表示,Qwen3系列支持119种语言,并基于近36万亿个token(标记)进行训练,使用的数据量是Qwen2.5的两倍。Token是模型处理的基本数据单元,约100万个token相当于75万英文单词。阿里巴巴称,Qwen3的训练数据包括教材、问答对、代码片段等多种内容。 据介绍,Qwen3预训练过程分为三个阶段。在第一阶段(S1),模型在超过30万亿个token上进行了预训练,上下文长度为4K token。这一阶段为模型提供了基本的语言技能和通用知识。 在第二阶段(S2),训练则通过增加知识密集型数据(如 STEM、编程和推理任务)的比例来改进数据集,随后模型又在额外的5万亿个token上进行了预训练。在最后阶段则使用高质量的长上下文数据将上下文长度扩展到32K token,确保模型能够有效地处理更长的输入。 阿里巴巴表示,由于模型架构的改进、训练数据的增加以及更有效的训练方法,Qwen3 Dense基础模型的整体性能与参数更多的Qwen2.5基础模型相当。例如,Qwen3-1.7B/4B/8B/14B/32B-Base分别与Qwen2.5-3B/7B/14B/32B/72B-Base 表现相当。特别是在 STEM、编码和推理等领域,Qwen3 Dense基础模型的表现甚至超过了更大规模的Qwen2.5 模型。对于Qwen3 MoE基础模型,它们在仅使用10%激活参数的情况下达到了与Qwen2.5 Dense基础模型相似的性能,显著节省了训练和推理成本。 而在后训练阶段,阿里使用多样的的长思维链数据对模型进行了微调,涵盖了数学、代码、逻辑推理和 STEM 问题等多种任务和领域,为模型配备基本的推理能力。然后通过大规模强化学习,利用基于规则的奖励来增强模型的探索和钻研能力。 阿里巴巴表示,Qwen3在调用工具(tool-calling)、执行指令以及复制特定数据格式等能力方面表现出色,推荐用户使用Qwen-Agent来充分发挥Qwen3的Agent能力。Qwen-Agent内部封装了工具调用模板和工具调用解析器,大大降低了代码复杂性。 除了提供下载版本外,Qwen3还可以通过Fireworks AI、Hyperbolic等云服务提供商使用。 目标仍对准AGI OpenAI、谷歌和Anthropic近期也陆续推出了多款新模型。OpenAI近日表示,也计划在未来几个月发布一款更加“开放”的模型,模仿人类推理方式,这标志着其策略出现转变,此前DeepSeek和阿里巴巴已经率先推出了开源AI系统。 目前,阿里巴巴正以Qwen为核心,构建其AI版图。今年2月,首席执行官吴泳铭表示,公司目前的“首要目标”是实现通用人工智能(AGI)——即打造具备人类智力水平的AI系统。 阿里表示,Qwen3代表了该公司在通往通用人工智能(AGI)和超级人工智能(ASI)旅程中的一个重要里程碑。展望未来,阿里计划从多个维度提升模型,包括优化模型架构和训练方法,以实现几个关键目标:扩展数据规模、增加模型大小、延长上下文长度、拓宽模态范围,并利用环境反馈推进强化学习以进行长周期推理。 开源社区振奋 阿里Qwen3的发布让AI社区感到激动,有网友献上经典Meme: 有网友说,在我的测试中,235B在高维张量运算方面的表现相当于Sonnet。这是一个非常出色的模型,感谢你们。 有网友对Qwen3赞不绝口:如果不是亲眼看到屏幕上实时生成的tokens,我根本不会相信那些基准测试结果。???? 简直像魔法一样???? 而开源AI的支持者则更加兴奋。有网友说:“有了一个开源32B大模型,性能跟Gemini 2.5 Pro不相上下。”“我们彻底杀回来了!” 网友感谢阿里积极推动开源:","news_type":1},"isVote":1,"tweetType":1,"viewCount":8,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"CN","totalScore":0},{"id":429594589147160,"gmtCreate":1745903062752,"gmtModify":1745904672542,"author":{"id":"3493179575404193","authorId":"3493179575404193","name":"long or short","avatar":"https://static.tigerbbs.com/5f4d3a07a91dabdf256b61df2d5f94eb","crmLevel":7,"crmLevelSwitch":1,"followedFlag":false,"idStr":"3493179575404193","authorIdStr":"3493179575404193"},"themes":[],"htmlText":"我平仓了50.0股<a href=\"https://laohu8.com/S/NVDA\">$英伟达(NVDA)$ 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每股收益LYR 0.03 52周最高 4.36 ROE -210.23% 市净率 4.88,经营数据很不好","html":"总市值 4,801.1万 成交量 3,937.00 市盈率TTM -4.67 流通市值 214.8万 股息 -- 市盈率LYR 52.36 总股本 3,664.97万 股息收益率 -- 每股收益TTM -0.28 流通股本 163.96万 ROA -13.69% 每股收益LYR 0.03 52周最高 4.36 ROE -210.23% 市净率 4.88,经营数据很不好"}],"imageCount":0,"langContent":"CN","totalScore":0}],"lives":[]}