Sky-T1-32B-Preview

An inference model that performs comparably to o1-preview in inference and programming benchmarks.

CommonProductProgramming\Inference ModelOpen Source

Sky-T1-32B-Preview is an inference model developed by the NovaSky team at UC Berkeley. The model excels in popular inference and programming benchmarks, matching the performance of o1-preview, with training costs under $450. It demonstrates the potential for efficient replication of advanced inference capabilities at low costs. The model is fully open-source, including data, code, and model weights, aimed at driving the advancement of the academic and open-source communities. Its main advantages are low cost, high performance, and open-source accessibility, providing valuable resources for researchers and developers.

Exhibits strong reasoning capabilities in both mathematical and programming domains.
Uses QwQ-32B-Preview to generate training data and enhances data quality through rejection sampling and data rewriting.
Completes training in 19 hours using 8 H100 GPUs and DeepSpeed Zero-3 offloading
at an approximate cost of $450.
Provides detailed open-source code and model weights
facilitating community reproducibility and improvement.
Demonstrates competitiveness in mathematical and programming tasks through evaluations on diverse datasets.

\The target audience includes researchers
developers
and academic institutions. This model is suitable for teams looking to conduct advanced inference model research and development at a low cost
as well as communities aiming to leverage open-source resources to drive technological advancements.\

Researchers can utilize the model's open-source code and weights for further exploration and optimization of inference models.
Developers can create new applications based on this model
particularly in programming assistance and mathematical problem solving.
Academic institutions can use it as a teaching resource to help students understand and practice the training and application of advanced inference models.

1. Visit the project GitHub page to access the open-source code and model weights.
2. Refer to the provided technical report and wandb logs to understand the model's training details and data processing methods.
3. Utilize the provided infrastructure code to build data pipelines
train
and evaluate the model.

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