# limix-ldm-ai/limix **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/limix-ldm-ai-limix).** 3,538 stars · 300 forks · Python · Apache-2.0 ## Links - GitHub: https://github.com/limix-ldm-ai/LimiX - Homepage: https://www.limix.ai - awesome-repositories: https://awesome-repositories.com/repository/limix-ldm-ai-limix.md ## Topics `foundation-models` `limix` `machine-learning` `structured-data` ## Description LimiX is a tabular foundation model and a suite of tools for structured data, providing a transformer-based system for classification, regression, and data generation. It includes a causal inference engine to determine cause-and-effect relationships, a synthetic data generator, and a framework for filling missing dataset values through feature context prediction. The project optimizes tabular inference through a high-performance system that uses ensemble-based sample retrieval to increase prediction speed and accuracy on high-specification hardware. It further distinguishes itself by using transformer-based encoding and masked-feature pretraining to learn data distributions. The system covers a broad range of analytical capabilities, including high-dimensional vector embedding for categorical separation and the creation of synthetic samples via causal-graph data generation. Its predictive surface extends to specific applications such as electricity market price forecasting and the analysis of molecular properties in organic molecules. ## Tags ### Artificial Intelligence & ML - [Tabular Foundation Model Application](https://awesome-repositories.com/f/artificial-intelligence-ml/foundation-models/tabular-foundation-model-application.md) — Provides a pre-trained transformer foundation model specifically designed for tabular classification and regression tasks. - [Causal Inference Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/causal-inference-tools.md) — Implements a system for determining cause-and-effect relationships and encoding dependencies between structured variables. ([source](https://cdn.jsdelivr.net/gh/limix-ldm-ai/limix@main/README.md)) - [Tabular Inference Runtimes](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/inference-runtimes/high-performance-ai-inference/tabular-inference-runtimes.md) — Provides an ensemble retrieval approach to accelerate prediction speeds and accuracy on high-specification hardware. - [Masked-Feature Pretraining](https://awesome-repositories.com/f/artificial-intelligence-ml/masked-feature-pretraining.md) — Learns data distributions by training the model to reconstruct randomly hidden feature values. - [Tabular Feature Embeddings](https://awesome-repositories.com/f/artificial-intelligence-ml/tabular-feature-embeddings.md) — Maps categorical and numerical features into a shared latent space to enable effective separation of data classes. - [Tabular Tokenization Encoders](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-encoders/tabular-tokenization-encoders.md) — Converts structured data rows into embeddings by treating features as tokens within a transformer architecture. - [Ensemble Retrieval Accelerators](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-retrieval-systems/proximity-sample-retrieval/ensemble-retrieval-accelerators.md) — Increases prediction speed using an ensemble retrieval approach to maximize high-specification hardware performance. ([source](https://www.limix.ai/doc)) - [Ensemble Retrieval Optimizers](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-retrieval-systems/proximity-sample-retrieval/ensemble-retrieval-optimizers.md) — Accelerates tabular inference by retrieving and combining similar historical samples to refine final predictions. - [Tabular Embedding Extraction](https://awesome-repositories.com/f/artificial-intelligence-ml/feature-extraction/text-embedding-extraction/tabular-embedding-extraction.md) — Extracts high-dimensional vector representations from tabular data models for downstream categorical analysis. ([source](https://www.limix.ai)) - [Tabular Inference Optimizers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/serving-and-runtime/large-language-model-optimization/model-inference-optimizations/tabular-inference-optimizers.md) — Reduces prediction time and increases accuracy using ensemble retrieval methods on high-performance hardware. - [Sample Retrieval Optimizers](https://awesome-repositories.com/f/artificial-intelligence-ml/retrieval-optimization/sample-retrieval-optimizers.md) — Increases prediction precision using an optimized retrieval system that identifies salient patterns in key samples. ([source](https://cdn.jsdelivr.net/gh/limix-ldm-ai/limix@main/README.md)) - [Causally Constrained Data Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/structured-data-generation/causally-constrained-data-generators.md) — Creates synthetic samples using Directed Acyclic Graphs to ensure generated data respects known causal relationships. - [Synthetic Data Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/synthetic-data-generation.md) — Generates synthetic data samples using feature masking or causal dependency graphs to preserve distributions. ([source](https://www.limix.ai)) - [Tabular](https://awesome-repositories.com/f/artificial-intelligence-ml/synthetic-data-generators/tabular.md) — Produces realistic synthetic tabular samples by learning the underlying distribution of the input dataset. ([source](https://cdn.jsdelivr.net/gh/limix-ldm-ai/limix@main/README.md)) ### Data & Databases - [Tabular Predictive Models](https://awesome-repositories.com/f/data-databases/tabular-data-frameworks/tabular-predictive-models.md) — Provides a transformer-based foundation model for classification and regression tasks across diverse structured datasets. ([source](https://cdn.jsdelivr.net/gh/limix-ldm-ai/limix@main/README.md)) - [Missing Data Imputation](https://awesome-repositories.com/f/data-databases/missing-data-imputation.md) — Fills missing dataset cells by predicting hidden entries based on the observed context of other features. ([source](https://www.limix.ai/))