Okay, I need to translate the given document. First, I'll check the language. The content is in Chinese, so I should translate it into English. I'll read through the text to understand the context. It's about OpenAI releasing their open-source models, gpt-oss-120b and gpt-oss-20b. The article mentions technical details, licensing, performance comparisons, collaborations, and some critiques.
I need to make sure the translation is accurate. Words like "Apache 2.0许可证" become "Apache 2.0 license". Terms like "MoE架构" should be "MoE architecture". I'll also check for proper nouns like "Zhuohan Li" and ensure they are correctly transliterated.
Some sentences are complex, so I'll break them down for clarity. For example, "模型具备可调节推理强度、提供推理过程透明化等特性" translates to "models feature adjustable inference strength and transparent reasoning processes".
I need to maintain the structure of the original text, keeping paragraphs and sections intact. Also, ensure that technical terms are correctly translated and that the flow of information remains consistent.
After translating, I'll review the text to check for any grammatical errors or awkward phrasing. Make sure that the translation conveys the same meaning as the original Chinese text without adding or omitting any information.
Finally, I'll ensure that the response doesn't include any preambles or additional content, just the translated text. Let me double-check that all key points are covered and that the translation is natural and accurate.
OpenAI recently released its open-source large models, gpt-oss-120b and gpt-oss-20b, marking the end of its five-year closed-source strategy. These two models, with parameter counts of 120 billion and 20 billion respectively, can run on consumer hardware (such as NVIDIA H100 GPUs) and adopt the Apache 2.0 license, allowing commercial use without additional authorization. The models feature adjustable inference strength and transparent reasoning processes, outperforming closed-source models like o4-mini and GPT-4o in multiple benchmark tests. Technically, they utilize MoE architecture and Flash Attention algorithms, significantly reducing memory requirements and improving training efficiency.
OpenAI also collaborates with hardware manufacturers like NVIDIA and AMD to optimize model deployment across different platforms, with some versions achieving a throughput of 3,000 tokens per second. Core team member Zhuohan Li has contributed to multiple open-source technologies, such as vLLM and Alpa, promoting efficient large model deployment. However, OpenAI’s open-source initiative is not entirely transparent, as it has not disclosed training data or reinforcement learning methods, raising concerns about reproducibility among developers.
Meanwhile, Anthropic launched Claude Opus 4.1, enhancing code generation and reasoning capabilities, while Google DeepMind released Genie 3, enabling real-time interactive world generation. OpenAI’s current open-source move is more of a response to competition from open-source models and developer demands, but its commercial nature remains, leaving uncertainty about its future openness.
Translation
Okay, so I need to analyze this article about OpenAI releasing their open-source models, gpt-oss-120b and gpt-oss-20b. Let me start by reading through the document carefully to understand the key points.
First, the article mentions that OpenAI, along with Anthropic and Google DeepMind, are big players in the AI field. They all released new products, but OpenAI's move to open-source their models is highlighted. The main points are about the two new models, their specifications, the Apache 2.0 license, and their performance compared to other models like o4-mini and GPT-4o.
The models are described as having different parameter sizes—120B and 20B. They can run on consumer hardware, which is a big deal. The Apache license allows for commercial use without restrictions, which is important for developers. The models also support adjustable inference strength and provide access to the thought process, which helps in debugging and trust.
The article also talks about the technical aspects, like the use of MoE architecture and efficient training methods. There's mention of benchmark tests where these models outperformed some closed-source models. However, there are warnings about not using them for medical diagnoses.
Then there's a part about the collaboration with various companies and the hardware partnerships, like with NVIDIA and AMD. The person behind the project, Zhuohan Li, is highlighted for his contributions to open-source projects like vLLM and Vicuna.
The article also touches on other companies' updates, like Anthropic's Claude Opus 4.1 and Google's Genie 3. Finally, there's a critique that OpenAI's open-source isn't fully transparent, as they haven't shared training data or methods, which might limit reproducibility.
Now, I need to summarize this in a few paragraphs, keeping it under 50% of the original length. The summary should include the main points: the release of the models, their features, performance, licensing, technical details, collaborations, and the broader context of other companies' moves. Also, mention the critique about the openness of the release.
I should make sure to highlight the significance of the models running on consumer hardware, the Apache license, the benchmark comparisons, and the role of Zhuohan Li. Also, note the other companies' updates as part of the AI industry context. The critique about OpenAI's partial openness is important to include as well.
Wait, the user wants the summary in Chinese, but the thinking process is in English. The final answer should be in Chinese. Let me structure the summary accordingly, making sure it's concise and covers all the key points without going into too much detail. Check for any important details I might have missed, like the training costs and hardware partnerships. Also, the part about the release being a response to other open-source models and developer demands.
Okay, I think that's covered. Let me put it all together in a clear, concise summary in Chinese.
OpenAI近日发布开源大模型gpt-oss-120b和gpt-oss-20b，标志着其五年的闭源策略结束。这两款模型参数量分别为1200亿和200亿，支持在普通硬件（如英伟达H100 GPU）上运行，且采用Apache 2.0许可证，允许商业用途且无需额外授权。模型具备可调节推理强度、提供推理过程透明化等特性，性能在多个基准测试中超越闭源模型如o4-mini和GPT-4o。技术上，其采用MoE架构和Flash Attention算法，显著降低内存需求并提升训练效率。
OpenAI还与英伟达、AMD等硬件厂商合作，实现模型在不同平台的优化部署，部分版本推理速度达每秒3000个token。团队核心成员李卓翰（Zhuohan Li）贡献了多项开源技术，如vLLM和Alpa工具，推动大模型高效部署。不过，OpenAI的开源并非完全透明，未公开训练数据、强化学习方法等，引发开发者对复现性问题的质疑。
同期，Anthropic推出Claude Opus 4.1，强化代码生成和推理能力；Google DeepMind发布Genie 3，实现实时交互式世界生成。OpenAI此次开源更多是应对开源模型竞争和开发者诉求，但其商业本质仍存在，未来是否持续开放仍存疑。
Reference:
https://openai.com/index/introducing-gpt-oss/