OpenO1-SFT dataset

Description

The OpenO1-SFT dataset is a dataset focused on the Chain-of-Thought capabilities of language models activated using Supervised Fine-tuning (SFT) methods to enhance the model's ability to generate coherent logical inference sequences. It contains 77,685 records covering not only Chinese but also English, making the dataset useful in multilingual environments.
Each record in the dataset uses < Thought > and < Output > tags to distinguish between the model's thought process and the final answer. This structure not only ensures the consistency of the data format, but also ensures logic, allowing the model to better learn and simulate human thought processes.
When fine-tuning a model using the OpenO1-SFT dataset, researchers need to ensure that the model can correctly parse the < Thought > and < Output > tags, which are essential for the model to correctly identify and learn inference processes and answers. The model fine-tuned in this way showed significant performance gains across multiple benchmarks, especially for tasks that required detailed inference steps.
The OpenO1-SFT dataset has a wide range of applications, especially in areas that require a high degree of logic and reasoning capabilities, such as intelligent question answering systems, educational aids, and legal advice systems. Models trained with this dataset can understand and answer complex questions more accurately, providing more detailed and reliable solutions.
In the latest research direction in the field of natural language processing, the OpenO1-SFT dataset was used to explore how to further enhance the reasoning power of language models through chain thinking activation. The goal is to enable models to produce detailed and structured reasoning steps that will perform better in complex reasoning tasks. These studies not only drive the performance of models in mathematical and logical reasoning tasks, but also provide new ideas for solving more complex natural language understanding problems.

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