Steiner-32b-preview

Steiner is a reasoning model trained on synthetic data, designed to explore multiple reasoning paths and verify them autonomously.

CommonProductProductivityReasoning ModelReinforcement Learning

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Steiner is a series of reasoning models developed by Yichao 'Peak' Ji, focusing on training on synthetic data through reinforcement learning, capable of exploring multiple paths and autonomously verifying or retracing during reasoning. The model aims to replicate the reasoning capabilities of OpenAI o1 and verify the scaling curve during reasoning. Steiner-preview is an ongoing project, and its open-source nature aims to share knowledge and obtain feedback from more real users. Although the model performs well in some benchmark tests, it has not yet fully achieved the reasoning scaling capabilities of OpenAI o1 and is therefore still under development.

Supports zero-shot reasoning without relying on chain-of-thought prompting or agent frameworks.
Can autonomously explore multiple paths and perform verification or backtracking during reasoning.
Compatible with existing inference services; vLLM is recommended for deployment.
Supports multilingual reasoning
primarily English
but can also handle Chinese.
Provides detailed reasoning processes and results for easy user understanding and evaluation.
Optimizes reasoning paths through reinforcement learning
improving reasoning efficiency and accuracy.
Suitable for reasoning tasks in various disciplines

This model is suitable for researchers
developers
and educators who need to perform complex reasoning tasks
especially in scenarios requiring autonomous exploration and verification of reasoning paths. It is also suitable for academic institutions and enterprises researching model reasoning capabilities
and for the developer community testing and improving open-source models.

In the GPQA Diamond benchmark test
Steiner showed high accuracy in several subfields (such as quantum mechanics and molecular biology)
demonstrating its reasoning capabilities in specific disciplines.
Users can deploy Steiner using vLLM
only needing to add specific parameters to make inference requests

Access the Hugging Face website and find the Steiner-32b-preview model page.
Find the deployment guide on the page and choose the recommended vLLM as the inference service.
Add necessary parameters (such as 'skip_special_tokens': false and 'spaces_between_special_tokens': false) to the inference request as per the guide.
Send an inference request using a Python client or other supported tools
for example

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Steiner-32b-preview — Steiner is a reasoning model trained on synthetic data, designed to explore multiple reasoning paths and verify them autonomously.

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