Model training and deployment

AlphaOne (α1) is a general framework for regulating the thinking progress of large reasoning models (LRMs) at test time. By introducing α moments and dynamically scheduling slow thought transitions, α1 enables flexible regulation of slow-to-fast reasoning. This method unifies and generalizes existing monotonic scaling methods, optimizing reasoning capabilities and computational efficiency. This product is suitable for researchers and developers who need to handle complex reasoning tasks.

EaseVoice Trainer is a backend project designed to simplify and enhance the speech synthesis and conversion training process. This project is improved based on GPT-SoVITS, focusing on user experience and system maintainability. Its design concept is different from the original project and aims to provide a more modular and customized solution suitable for a variety of scenarios from small-scale experiments to large-scale production. This tool can help developers and researchers conduct speech synthesis and conversion research and development more efficiently.

rStar-Math is a study that aims to demonstrate that small language models (SLMs) can match or exceed the mathematical reasoning capabilities of OpenAI’s o1 model without relying on higher-level models. The study implements "deep thinking" through Monte Carlo Tree Search (MCTS), in which mathematical policy SLM searches at test time guided by an SLM-based process reward model. rStar-Math introduces three innovative methods to address the challenge of training two SLMs, advancing the mathematical reasoning capabilities of SLMs to the state-of-the-art through 4 rounds of self-evolution and millions of synthetic solutions. The model significantly improves performance on the MATH benchmark and outperforms in the AIME competition.

OpenScholar_ExpertEval is a collection of interfaces and scripts for expert evaluation and data evaluation, designed to support the OpenScholar project. The project synthesizes scientific literature by retrieving enhanced language models and performs meticulous human evaluation of the text generated by the models. The product background is based on AllenAI's research project, which has important academic and technical value and can help researchers and developers better understand and improve language models.

Juchi Cloud is a GPU cloud service provider focusing on the field of artificial intelligence. It provides stable artificial intelligence cloud servers, high-speed network disk and other services, and supports professional artificial intelligence solutions such as public cloud and private cloud. Help lower the threshold for using cloud services and improve the efficiency of scientific research and education.