OpenAI Dropped a FRONTIER Open-Weights Model

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]