limix-ldm-ai/LimiX

Health v2 · maintenance only

Adoption not part of the score

4045 stars · 305 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

LimiX is the first large structured-data foundation model (LDM), a transformer-based model for tabular data that handles classification, regression, missing-value imputation, and tabular generation with a single pretrained model. It is open-sourced under Apache 2.0 and claims to outperform XGBoost and other tabular deep learning and foundation models on mainstream structured-data benchmarks.

Use cases

• classify tabular data without training a task-specific model
• predict continuous targets on structured datasets
• impute missing values in tabular datasets
• generate synthetic tabular data
• replace XGBoost or bespoke tabular ML pipelines with a foundation model
• perform feature selection and causal inference on structured data

When to choose

• you need strong out-of-the-box performance on tabular classification or regression
• your datasets have missing values and you want joint modeling without separate imputation pipelines
• you want a single pretrained model covering multiple tabular tasks
• you want a zero-training or few-shot alternative to gradient boosting

When to avoid

• you need a lightweight CPU-only solution, since inference requires GPU resources
• you work with unstructured data like text, images, or audio
• you need full interpretability of a simple model like linear regression or decision trees
• your dataset is enormous and training a specialized model is feasible and preferable

Facets

library · maturity active

machine-learning deep-learning data-science machine-learning data-science artificial-intelligence python cross-platform tabular-data foundation-model transformer classification regression missing-value-imputation tabular-generation xgboost-alternative gpu

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