About gpt-oss
gpt-oss is a family of open-weight language models released by OpenAI. These models are designed to be efficient, and highly customizable for a wide range of applications. This project, the gpt-oss playground, is a demonstration of their capabilities.
Open-Weight Language Models
The term "open-weight" means that the model's parameters, the very core of the AI, are publicly available. This allows developers and researchers to not only use the models but also to fine-tune them for specific tasks, conduct research on their inner workings, and build new applications on top of them. The gpt-oss models are released under the permissive Apache 2.0 license, encouraging broad use and innovation.
Key Technical Features
Mixture-of-Experts (MoE)
The gpt-oss models utilize a Mixture-of-Experts (MoE) architecture. This means that instead of one giant model processing every request, the system is composed of many smaller "expert" models. For any given input, the system intelligently routes the request to the most relevant experts. This approach significantly improves efficiency and performance, especially for complex tasks.
Chain-of-Thought (CoT)
One of the standout features of the gpt-oss models is their ability to provide full, non-supervised Chain-of-Thought (CoT) data. This means the model can show its work, revealing the step-by-step reasoning process it used to arrive at an answer. This transparency is crucial for understanding model behavior, debugging, and ensuring responsible AI development.
Reinforcement Learning from Human Feedback (RLHF)
To ensure the models are helpful and harmless, they are trained using a technique called Reinforcement Learning from Human Feedback (RLHF). This process involves human reviewers rating the model's responses, which then helps to fine-tune the model's behavior to better align with human values and expectations.
The gpt-oss Playground
This playground provides a hands-on experience with the gpt-oss-120b and gpt-oss-20b models. You can interact with them, test their capabilities, and see their performance on a variety of tasks. We encourage you to explore, experiment, and discover the potential of these powerful open-weight language models.