Model overview
Mitra-v2 regressor is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random regressors, including the new Hybrid SCM prior. It is the second generation of the Mitra regressor (autogluon/mitra-regressor), pre-trained with a 10x longer context, three times as many features, and an improved optimizer, and it replaces the scalar mean-squared-error head of Mitra-v1 with a 1,000-bin distributional head. On the TabArena and TALENT benchmarks it delivers state-of-the-art accuracy at the level of TabFM and EXAONE Tabular, while surpassing TabPFN-3 by a wide margin. The classification model is at autogluon/mitra-classifier-2, and the inference and fine-tuning code with our evaluation results is at autogluon/mitra-finetune. Mitra-v2 is based on a 12-layer 2D Transformer of 76.7 M parameters (attention across rows and across columns), pre-trained by incorporating an in-context learning paradigm. Regression is cast as classification over 1,000 target bins: the model predicts a distribution over bins and the point prediction is the mean of that distribution. Apart from this head the architecture is unchanged from Mitra-v1. To use Mitra-v2 regressor, install AutoGluon and the `mitra-finetune` package by running:
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
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Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
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Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。