gpt-oss-20b可在16GB内存的设备上运行，gpt-oss-120b需要约80GB内存，适合包括Mac电脑在内的个人 设备。 gpt-oss-120b在核心推理基准测试中接近o4-mini，gpt-oss-20b则达到或超过o3-mini，且在特定任务上表 现更优。 这些模型设计为低成本选项，支持本地运行、工具使用和思维链处理，适合开发者和研究人员定制。 OpenAI发布了gpt-oss-120b和gpt-oss-20b两款开放权重语言模型，这是自2019年GPT-2以来首次。 ...

当天早些时候，OpenAI发布了两个开放权重AI模型，分别是GPT-oss-120b和GPT-oss-20b。 微软首席执行官萨蒂亚·纳德拉当地时间8月5日发文称，很高兴将OpenAI的gpt-oss模型引入Azure"AI应 用和智能体工厂"AI Foundry，并通过Foundry Local将其带到Windows平台。纳德拉称，这是混合AI的实 际应用：用户可以灵活组合不同模型，优化性能与成本，并直接在数据所在的位置进行处理。 ...

RT Taelin (@VictorTaelin)My initial impression on OpenAI's OSS model is aligned with what they advertised. It does feel closer to o3 than to other open models, except it is much faster and cheaper. Some providers offer it at 3000 tokens/s, which is insane. It is definitely smarter than Kimi K2, R1 and Qwen 3. I tested all models for a bit, and got very decisive results in favor of OpenAI-OSS-120b.Unfortunately, there is one thing these models can't do yet - my damn job. So, hope you guys have fun. I'll be b ...

Financial Support for New Industrialization - The People's Bank of China and other regulatory bodies issued guidelines to support new industrialization, focusing on key sectors like integrated circuits and industrial mother machines [1][2] - Banks are encouraged to provide long-term financing for technology breakthroughs and facilitate easier access to capital for companies achieving core technology advancements [1][2] Emerging Industries and Financing - New industries such as information technology, renewable energy, and biomedicine will have access to multi-tiered capital markets for financing [2] - Long-term funds from government investment funds and insurance will focus on future manufacturing and energy sectors [2] Support for Small and Medium Enterprises - Financial institutions are urged to reduce reliance on guarantees and provide financing based on data and asset credit [2] - A national credit information platform for small and micro enterprises is being developed to facilitate easier access to credit [2] Green Transition Financing - Financial support will be directed towards high-carbon industries that meet green transformation criteria, with a focus on green credit and bonds [2] - A specialized financial standard system will be established to enhance funding for green projects [2] Digital Integration and Services - Digital infrastructure projects like 5G and industrial internet will receive long-term loans and financing options [2] - Banks are developing digital platforms to provide one-stop services for financing and settlement, improving efficiency for small businesses [2] Risk Management in Financial Institutions - Financial institutions are required to monitor the use of funds to prevent misuse and ensure compliance with regulations [3] - Joint risk assessments will be conducted to share high-risk information and manage potential financial risks [3] Market Trends and Predictions - Major financial institutions have warned clients to prepare for potential declines in U.S. stock prices, with predictions of a 10% to 15% correction in the S&P 500 index [17][18] - The retail forecast for passenger vehicles in 2025 has been slightly adjusted upward, indicating a growth of 6% [19]

Core Insights - The article provides a comprehensive overview of large language models (LLMs), their definitions, architectures, capabilities, and notable developments in the field [3][6][12]. Group 1: Definition and Characteristics of LLMs - Large Language Models (LLMs) are deep learning models trained on vast amounts of text data, capable of understanding and generating natural language [3][6]. - Key features of modern LLMs include large-scale parameters (e.g., GPT-3 with 175 billion parameters), Transformer architecture, pre-training followed by fine-tuning, and multi-task adaptability [6][12]. Group 2: LLM Development and Architecture - The Transformer architecture, introduced by Google in 2017, is the foundational technology for LLMs, consisting of an encoder and decoder [9]. - Encoder-only architectures, like BERT, excel in text understanding tasks, while decoder-only architectures, such as GPT, are optimized for text generation [10][11]. Group 3: Core Capabilities of LLMs - LLMs can generate coherent text, assist in coding, answer factual questions, and perform multi-step reasoning [12][13]. - They also excel in text understanding and conversion tasks, such as summarization and sentiment analysis [13]. Group 4: Notable LLMs and Their Features - The GPT series by OpenAI is a key player in LLM development, known for its strong general capabilities and continuous innovation [15][16]. - Meta's Llama series emphasizes open-source development and multi-modal capabilities, significantly impacting the AI community [17][18]. - Alibaba's Qwen series focuses on comprehensive open-source models with strong support for Chinese and multi-language tasks [18]. Group 5: Visual Foundation Models - Visual Foundation Models are essential for processing visual inputs, enabling the connection between visual data and LLMs [25]. - They utilize architectures like Vision Transformers (ViT) and hybrid models combining CNNs and Transformers for various tasks, including image classification and cross-modal understanding [26][27]. Group 6: Speech Large Models - Speech large models are designed to handle various speech-related tasks, leveraging large-scale speech data for training [31]. - They primarily use Transformer architectures to capture long-range dependencies in speech data, facilitating tasks like speech recognition and translation [32][36]. Group 7: Multi-Modal Large Models (MLLMs) - Multi-modal large models can process and understand multiple types of data, such as text, images, and audio, enabling complex interactions [39]. - Their architecture typically includes pre-trained modal encoders, a large language model, and a modal decoder for generating outputs [40]. Group 8: Reasoning Large Models - Reasoning large models enhance the reasoning capabilities of LLMs through optimized prompting and external knowledge integration [43][44]. - They focus on improving the accuracy and controllability of complex tasks without fundamentally altering the model structure [45].

Model Release & Performance - Anthropic 发布了 Claude Opus 4.1%，是对 Claude Opus 4 的升级，尤其在 Agentic 任务、真实世界编码和推理方面 [1] - SWEBench verified 基准测试中，Claude Opus 4.1% 的得分从 Opus 4 的 72.5% 提升至 74.5%，提升了 2 个百分点 [3] - Terminal Bench 基准测试中，Claude Opus 4.1% 的终端使用能力从 39.2% 提升至 43.3%，提升了 4.1 个百分点 [4] - GPQA Diamond（研究生水平推理）基准测试中，Claude Opus 4.1% 的得分从 79.6% 提升至 80.9%，提升了 1.3 个百分点 [4] - Towbench（Agentic 工具使用）基准测试中，Claude Opus 4.1% 在零售方面的得分从 81.4% 提升至 82.4%，提升了 1 个百分点，但在航空方面从 59.6% 下降至 56%，下降了 3.6 个百分点 [5] - 多语言问答基准测试中，Claude Opus 4.1% 的得分从 88.8% 提升至 89.5%，提升了 0.7 个百分点 [5] - Amy 2025 基准测试中，Claude Opus 4.1% 的得分提升了 2.5 个百分点至 78% [5] Competitive Positioning & Future Outlook - 在 SWEBench 和 Terminal Bench 基准测试中，Claude Opus 4.1% 优于 OpenAI 的 GPT-3 和 Gemini 1.5 Pro [5] - 在 GPQA Diamond 和 Agentic 工具使用基准测试中，Claude Opus 4.1% 不及 OpenAI 的 GPT-3 和 Gemini 1.5 Pro [6] - 在高中数学竞赛基准测试中，Claude Opus 4.1% 的得分低于 OpenAI 的 GPT-3 (88.9%) 和 Gemini 1.5 Pro (88%)，仅为 78% [6] - Claude 目前被广泛认为是市场上最佳的编码模型，尤其擅长 Agentic 编码和 Agent-driven 开发 [7]

OpenAI向开源模型迈出重要一步：六年来首次推出开放权重模型。 OpenAI首席执行官山姆·奥尔特曼当地时间8月5日宣布，公司将在未来几天里带来许多新东西，其中周 二迎来一项"小而重磅"的更新——预热已久的开源模型GPT-OSS。 两款模型都以宽松的Apache 2.0许可证发布，企业在商用前无需付费或获得许可。 奥尔特曼在社交媒体表示：gpt-oss是一个重大突破，这是最先进的开放权重推理模型，具有与o4-mini 相当的强大现实世界性能，可以在你自己的电脑（或手机的较小版本）上本地运行。我们相信这是世界 上最好、最实用的开放模型。 简单而言，OpenAI在8月5日共发布两款开放权重AI推理模型。其中参数量达到1170亿的gpt-oss-120b能 力更强，可以由单个英伟达专业数据中心GPU驱动；参数量210亿的gpt-oss-20b模型，则能够在配备 16GB内存的消费级笔记本电脑上运行。 同时，亚马逊宣布将首次向客户提供OpenAI的模型，计划在其Bedrock和SageMaker平台上提供OpenAI 的开放AI权重新模型。这是云计算巨头亚马逊首次提供OpenAI的产品。 gpt-oss-20b和1 ...

此举可能对依赖高端芯片的大型数据中心运营商构成重大打击，包括微软(MSFT.US)、OpenAI、 Meta(META.US)和亚马逊(AMZN.US)等公司。上述企业正在大举投资人工智能业务，对先进芯片的需 求尤为迫切。一旦征税，成本可能急剧上升。 美国总统特朗普周二表示，美国政府将在"未来一周左右"宣布对半导体和制药行业的进口产品加征关 税，意在重塑全球贸易格局，并迫使关键产业回流美国本土制造。 特朗普在接受专访时称："我们将先对药品加征一项初步的小幅关税，但在一年到一年半之内，这项关 税将升至150%，最终达到250%，因为我们希望药品在美国本土生产。" 他还表示，美国政府将"另行宣布"对半导体和芯片的关税措施。美国商务部自今年4月起便对全球半导 体市场展开调查，为潜在的关税行动奠定法律基础。根据相关预测，全球半导体市场的年销售额接近 7000亿美元。 此次涉及药品、金属等关键行业的关税措施，均源自依据《贸易扩展法》第232条进行的国家安全调 查。这类调查周期一般为九个月，较特朗普此前动用紧急权力对特定国家征税具有更强的法律基础。后 者目前正面临法院的法律挑战。 除半导体之外，特朗普还警告制药行业将面临 ...

Market Overview - The US stock market experienced a collective decline on August 5, with the Dow Jones down 0.14% to 44,111.74 points, the S&P 500 down 0.49% to 6,299.19 points, and the Nasdaq Composite down 0.65% to 20,916.55 points [1][2]. Economic Indicators - The ISM reported that the US services PMI for July was 50.1, below market expectations of 51.5 and the previous month's 50.8, indicating near stagnation in service sector growth [2][3]. - Employment indicators fell from 47.2 to 46.4, marking a low point since the COVID-19 pandemic [2]. - The prices for materials and services rose to 69.9, the highest since October 2022 [2]. Policy and Regulatory Environment - The report highlighted challenges for the Federal Reserve, as rising price indices contrast with weakening activity and employment metrics [3]. - New tariff announcements from former President Trump, including a "small tariff" on imported drugs that could rise to 250%, are expected to impact market sentiment [3]. Company Performance - Major tech stocks mostly declined, with Nvidia down 0.97%, Microsoft down 1.47%, and Meta down 1.66%. Amazon was an exception, rising by 0.99% [4][5]. - Palantir's stock surged by 7.85%, reaching a market cap exceeding $400 billion, following a quarterly revenue report that surpassed $1 billion, a 48% year-over-year increase [5]. Chinese Stocks - The Nasdaq Golden Dragon China Index fell by 0.56%, with mixed performances among popular Chinese stocks [7]. Company News - OpenAI, Google, and Anthropic have been approved as AI suppliers for the US government, which is expected to accelerate the adoption of AI tools in federal operations [8][9]. - Coinbase announced plans to issue $2 billion in convertible bonds to raise funds for stock buybacks and debt repayment [10]. - AMD reported a second-quarter net profit of $781 million, a 31% year-over-year decline, with revenues of $7.69 billion, a 32% increase [11]. - Supermicro's fourth-quarter net sales were $5.76 billion, below analyst expectations, with a projected fiscal year 2026 net sales of at least $33 billion [12]. - Lucid reported a second-quarter adjusted loss of $632.1 million, with revenues of $259.4 million, a 29% increase year-over-year [13].

Open AAI has delivered on their promise to release a state-of-the-art open-source model. This is GPTOSS. It comes in two sizes, a 120 billion parameter version and a 20 billion parameter version.These are state-of-the-art openweight language models. Open weight. So, not just open- source, but they are actually releasing the weights to these models.Now, for some benchmarks, here is the code forces competition. Now the 120 billion parameter version with tools scores a 2622. That is compared to 03 a frontier m ...