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Steiner-32b-preview

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

#Open source
#Multi-language support
#reinforcement learning
#synthetic data
#inference model
#zero sample inference
Steiner-32b-preview

Product Details

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.

Main Features

1
Supports zero-shot inference without relying on thought chain prompts or agent frameworks.
2
Ability to autonomously explore multiple paths and conduct verification or backtracking during the reasoning process.
3
Compatible with existing inference services, it is recommended to use vLLM for deployment.
4
Supports multi-lingual reasoning, mainly in English, but can also handle Chinese.
5
Provide detailed reasoning process and results to facilitate user understanding and evaluation.
6
Optimize reasoning paths through reinforcement learning to improve reasoning efficiency and accuracy.
7
Suitable for reasoning tasks in a variety of subject areas, such as physics, chemistry, biology, etc.
8
An open source model that allows users to test and provide feedback on a public platform.

How to Use

1
Visit the Hugging Face website and find the Steiner-32b-preview model page.
2
Find the deployment guide in the page and select the recommended vLLM as the inference service.
3
Follow the guidelines to add necessary parameters (such as 'skip_special_tokens': false and 'spaces_between_special_tokens': false) to the inference request.
4
Use the Python client or other supported tools to send an inference request, such as entering the question 'Hello' and get the model's inference results.
5
Adjust the format and content of inference requests as needed to ensure that the model can be parsed and processed correctly.
6
Observe the reasoning paths and results output by the model and evaluate its accuracy and applicability.
7
Fine-tune or optimize the model based on actual needs to better suit a specific task or domain.
8
Apply models to real-world scenarios, such as academic research, education or development projects, collect feedback and continuously improve.

Target Users

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.

Examples

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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