Sky-T1-32B-Preview
Explore Sky-T1, an open source inference AI model based on Alibaba QwQ-32B-Preview and OpenAI GPT-4o-mini. Learn how it excels in math, coding, and more, and how to download and use it.
English
English
Sky-T1 is a powerful open source inference AI model developed by the NovaSky team. Its training process combines the technologies of Alibaba's QwQ-32B-Preview and OpenAI's GPT-4o-mini. This enables Sky-T1 to demonstrate excellent reasoning capabilities in multiple fields, especially in mathematics and program code generation.
Model features:
Powerful reasoning capabilities: Sky-T1 outperforms early preview versions of OpenAI o1 on math competition-level challenges (MATH500) and programming code challenges (LiveCodeBench).
Open source release: Sky-T1 is released in open source form, making it easy for researchers and developers to use and improve.
Efficient training: Using only 8 Nvidia H100 GPU racks, the 32 billion parameter model can be trained in about 19 hours.
Technology integration: Combines the initial training data of Alibaba QwQ-32B-Preview and the data reconstruction technology of OpenAI GPT-4o-mini.
Model performance:
Advantages: Performs well in MATH500 and LiveCodeBench tests.
Disadvantages: The performance on GPQA-Diamond (containing difficult physics, biology and chemistry questions) is not as good as the o1 preview version.
Things to note:
The Sky-T1 excels in certain areas, but may have limitations in others.
OpenAI has released a more powerful o1GA version and plans to launch a more efficient o3 model. The performance advantage of Sky-T1 may be challenged.
Check whether the network connection is stable, try using a proxy or mirror source; confirm whether you need to log in to your account or provide an API key. If the path or version is wrong, the download will fail.
Make sure you have installed the correct version of the framework, check the version of the dependent libraries required by the model, and update the relevant libraries or switch the supported framework version if necessary.
Use a local cache model to avoid repeated downloads; or switch to a lighter model and optimize the storage path and reading method.
Enable GPU or TPU acceleration, use batch data processing methods, or choose a lightweight model such as MobileNet to increase speed.
Try quantizing the model or using gradient checkpointing to reduce the memory requirements. You can also use distributed computing to spread the task across multiple devices.
Check whether the input data format is correct, whether the preprocessing method matching the model is in place, and if necessary, fine-tune the model to adapt to specific tasks.
