Steiner is an inference model trained on synthetic data, designed to explore multiple inference paths and verify them autonomously.
Steiner is a family of inference models developed by Yichao 'Peak' Ji that focus on training on synthetic data through reinforcement learning, with the ability to explore multiple paths and autonomously verify or backtrack during inference. The goal of this model is to reproduce the inference capabilities of OpenAI o1 and verify the expansion curve during inference. Steiner-preview is an ongoing project, its open source purpose is to share knowledge and get more feedback from real users. While the model performs well on some benchmarks, OpenAI o1's inference scaling capabilities have not yet been fully realized and so is still in the development stage.
The model is suitable for researchers, developers, and educators who need to perform complex reasoning tasks, especially in scenarios where autonomous exploration and verification of reasoning paths are required. In addition, it is also suitable for academic institutions and enterprises that conduct research on model reasoning capabilities, as well as developer communities that test and improve open source models.
On the GPQA Diamond benchmark, Steiner demonstrated high accuracy in multiple subfields (e.g., quantum mechanics, molecular biology), demonstrating its reasoning capabilities in specific subject areas.
Users can deploy Steiner through vLLM and simply add specific parameters to make inference requests, such as entering a question in a conversation and getting inference results.
Steiner can perform inference without multi-turn dialogue data, but it is not recommended for multi-turn dialogue scenarios and is suitable for single-turn inference tasks.
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