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Hunyuan T1
Overview :
HunYuan T1 is an ultra-large-scale reasoning model based on reinforcement learning. Post-training significantly improves reasoning ability and aligns with human preferences. This model focuses on long-text processing and complex reasoning tasks, exhibiting significant performance advantages.
Target Users :
This product is suitable for researchers, developers, and educators because it can provide support in complex scientific and logical reasoning, while helping users quickly obtain accurate information and answers.
Use Cases
Using HunYuan-T1 in scientific research to solve complex scientific problems.
Developers utilize HunYuan-T1 to optimize code logic and improve programming efficiency.
Educators use HunYuan-T1 to provide students with accurate mathematics and logical reasoning training.
Features
Long-text reasoning: The TurboS architecture effectively solves the problem of context loss in long-text reasoning.
Efficient computation: Significantly reduces computational resource consumption while ensuring information capture capability.
Reinforcement learning optimization: 96.7% of computing power is used to improve reasoning ability and align with human preferences.
Curriculum learning strategy: Gradually increases data difficulty to enhance the model's reasoning ability.
Self-feedback mechanism: Improves the quality of model output content through a self-rewarding system.
High-standard evaluation: Demonstrates excellent performance on public benchmarks such as MMLU-PRO.
Multi-task adaptability: Performs excellently in tasks such as instruction following and text summarization.
Wide range of applications: Suitable for fields such as mathematics, logic reasoning, and code.
How to Use
Access the HunYuan T1 GitHub page.
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Access the model according to the provided API documentation.
Select the appropriate reasoning task and dataset.
Configure and adjust model parameters.
Start the reasoning process and obtain the model output results.
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