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OpenAI Goes OPEN-SOURCE! gpt-oss is HERE!
Matthew Berman· 2025-08-05 22:09
Model Release - Open AAI 发布了最先进的开源模型 GPTOSS,包含 1200 亿参数和 200 亿参数两个版本 [1] - 这些模型是 open weight 的语言模型,意味着模型权重也被发布 [1] Performance Benchmarks - 1200 亿参数版本的 GPTOSS 在 Code Forces 竞赛中,使用工具的情况下得分为 2622,与 Frontier 模型(得分 2706)非常接近 [2] - 200 亿参数版本的 GPTOSS 在使用工具的情况下得分为 2516,考虑到其规模,表现同样出色 [2] - 这些模型在编程方面的得分超过了地球上大多数人 [2]
OpenAI Dropped a FRONTIER Open-Weights Model
Matthew Berman· 2025-08-05 17:17
Model Release & Capabilities - Open AAI released GPTOSS, state-of-the-art open-weight language models in 120 billion and 20 billion parameter versions [1] - The models outperform similarly sized open-source models on reasoning tasks and demonstrate strong tool use capabilities [3] - The models are optimized for efficient deployment on consumer hardware, with the 120 billion parameter version running efficiently on a single 80 GB GPU and the 20 billion parameter version on edge devices with 16 GB of memory [4][5] - The models excel in tool use, few-shot learning, function calling, chain of thought reasoning, and health issue diagnosis [8] - The models support context lengths of up to 128,000 tokens [12] Training & Architecture - The models were trained using a mix of reinforcement learning and techniques informed by OpenAI's most advanced internal models [3] - The models utilize a transformer architecture with a mixture of experts, reducing the number of active parameters needed to process input [10][11] - The 120 billion parameter version activates only 5 billion parameters per token, while the 20 billion parameter version activates 36 billion parameters [11][12] - The models employ alternating dense and locally banded sparse attention patterns, group multi-query attention, and RoPE for positional encoding [12] Safety & Security - OpenAI did not put any direct supervision on the chain of thought for either OSS model [21] - The models were pre-trained and filtered to remove harmful data related to chemical, biological, radiological, and nuclear data [22] - Even with robust fine-tuning, maliciously fine-tuned models were unable to reach high capability levels according to OpenAI's preparedness framework [23] - OpenAI is hosting a challenge for red teamers with $500,000 in awards to identify safety issues with the models [24]