LimiX-2M

1. Model Introduction

LimiX is a new class of tabular AI model designed to overcome one of modern machine learning’s longest-standing bottlenecks: structured data. With only 2M parameters, LimiX-2M sets a new state-of-the-art across classification, regression, and missing-value imputation, surpassing XGBoost, CatBoost, AutoGluon, and TabPFN, and approaching the performance level of the larger LimiX-16M. Its lightweight, training-free design makes advanced tabular modeling accessible on ordinary hardware while preserving full transparency and offline deployability.

Key Features

Unified Tabular Reasoning：

End-to-end designed for multi-task tabular intelligence, enabling a single model to handle classification, regression, and imputation without additional tuning, preprocessing, or task-specific fine-tuning.

Training-Free, Context-Driven Inference：

Operates directly through context learning: no training, no hyperparameters, no preprocessing pipelines. LimiX automatically interprets and processes raw tabular inputs for immediate use.

Lightweight & Efficient Deployment：

A compact 2M-parameter architecture enables fast inference and smooth operation on standard CPUs and laptops, dramatically reducing compute requirements for advanced tabular modeling.

2. Model Architecture & Pretraining Procedures

LimiX adopts a 12-block transformer architecture with axis-wise attention to features and samples, supported by pre-normalized LayerNorm for stable scaling. The LimiX-16M variant uses an asymmetric design, two feature-axis passes and one sample-axis pass per block, to strengthen feature interaction modeling in heterogeneous schemas with minimal overhead.

To learn the joint distribution of tabular variables, LimiX is pretrained through Context-Conditional Masked Modeling (CCMM). By masking table cells and conditioning predictions on a small set of context rows, the model internalizes a wide range of conditional dependencies while adapting to new datasets without training or labels.

3. Evaluation Results

Classification

On the BCCO-CLS benchmark, LimiX-16M establishes leading performance by significantly outperforming AutoGluon and all PFN variants in mean AUC, Accuracy, and F1 scores, with substantially better ranks. LimiX-2M also marks a clear lead over these models in most metrics, except for its AUC rank.

Regression

LimiX-16M achieves the best overall scores and rankings on TALENT-REG, with the PFN models and LimiX-2M emerging as close runners-up in both R² and RMSE.

Missing Value Imputation

LimiX introduces the first training-free, in-context approach for missing-value imputation on entirely new datasets. Across a wide set of real-world benchmarks, LimiX-16M delivers the best performance, achieving lower RMSE and error rates than classical and learned imputers including KNN, MICE, MissForest, GAIN, and MIWAE. Unlike all prior methods, which depend on additional fitting, LimiX performs imputation directly from context with consistently superior accuracy.

Finetune

Using an attention-based retrieval–guided downsampling strategy, LimiX-16M fine-tunes on compact, highly relevant in-context episodes rather than full long contexts, substantially improving sample efficiency and reducing training cost. This approach enables LimiX-16M to significantly outperform strong baselines such as TabDPT and TabPFN-v2, with notable AUC gains across BCCO-CLS datasets.

4. Deployment

1. Environment Preparation

   • Recommended to deploy with Docker. Download the Dockerfile from the repository and execute the following command to build the image:

     docker build --network=host -t limix/infe:v1 --build-arg FROM_IMAGES=nvidia/cuda:12.2.0-base-ubuntu22.04 -f Dockerfile .
     

   • For manual deployment, install dependencies:

     # Download precompiled flash_attn file
     wget -O flash_attn-2.8.0.post2+cu12torch2.7cxx11abiTRUE-cp312-cp312-linux_x86_64.whl https://github.com/Dao-AILab/flash-attention/releases/download/v2.8.0.post2/flash_attn-2.8.0.post2+cu12torch2.7cxx11abiTRUE-cp312-cp312-linux_x86_64.whl
     # Install basic dependencies
     pip install python==3.12.7 torch==2.7.1 torchvision==0.22.1 torchaudio==2.7.1
     pip install flash_attn-2.8.0.post2+cu12torch2.7cxx11abiTRUE-cp312-cp312-linux_x86_64.whl
     pip install scikit-learn einops huggingface-hub matplotlib networkx numpy pandas scipy tqdm typing_extensions xgboost kditransform hyperopt
     

2. Model Download

   • Download model weights via Hugging Face Hub:

     from huggingface_hub import hf_hub_download
     model_file = hf_hub_download(repo_id="stableai-org/LimiX-16M", filename="LimiX-16M.ckpt", local_dir="./cache")
     

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5. Model Usage

1. Classification Task Example

   from sklearn.datasets import load_breast_cancer
   from sklearn.metrics import accuracy_score, roc_auc_score
   from sklearn.model_selection import train_test_split
   from huggingface_hub import hf_hub_download
   import numpy as np
   import os, sys
   
   os.environ["RANK"] = "0"
   os.environ["WORLD_SIZE"] = "1"
   os.environ["MASTER_ADDR"] = "127.0.0.1"
   os.environ["MASTER_PORT"] = "29500"
   
   ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
   if ROOT_DIR not in sys.path:
       sys.path.insert(0, ROOT_DIR)
   from inference.predictor import LimiXPredictor
   
   X, y = load_breast_cancer(return_X_y=True)
   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
   
   model_file = hf_hub_download(repo_id="stableai-org/LimiX-16M", filename="LimiX-16M.ckpt", local_dir="./cache")
   
   clf = LimiXPredictor(device='cuda', model_path=model_file, inference_config='config/cls_default_retrieval.json')
   prediction = clf.predict(X_train, y_train, X_test)
   
   print("roc_auc_score:", roc_auc_score(y_test, prediction[:, 1]))
   print("accuracy_score:", accuracy_score(y_test, np.argmax(prediction, axis=1)))
   

2. Regression Task Example

   from functools import partial
   
   from sklearn.datasets import fetch_california_housing
   from sklearn.model_selection import train_test_split
   from sklearn.metrics import r2_score
   from huggingface_hub import hf_hub_download
   try:
       from sklearn.metrics import root_mean_squared_error as mean_squared_error
   except:
       from sklearn.metrics import mean_squared_error
       mean_squared_error = partial(mean_squared_error, squared=False)
   import os, sys
   
   os.environ["RANK"] = "0"
   os.environ["WORLD_SIZE"] = "1"
   os.environ["MASTER_ADDR"] = "127.0.0.1"
   os.environ["MASTER_PORT"] = "29500"
   
   ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
   if ROOT_DIR not in sys.path:
       sys.path.insert(0, ROOT_DIR)
   from inference.predictor import LimiXPredictor
   
   house_data = fetch_california_housing()
   X, y = house_data.data, house_data.target
   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
   
   y_mean = y_train.mean()
   y_std = y_train.std()
   y_train_normalized = (y_train - y_mean) / y_std
   y_test_normalized = (y_test - y_mean) / y_std
   
   model_path = hf_hub_download(repo_id="stableai-org/LimiX-16M", filename="LimiX-16M.ckpt", local_dir="./cache")
   
   model = LimiXPredictor(device='cuda', model_path=model_path, inference_config='config/reg_default_retrieval.json')
   y_pred = model.predict(X_train, y_train_normalized, X_test)    
   
   # Compute RMSE and R²
   y_pred = y_pred.to('cpu').numpy()
   rmse = mean_squared_error(y_test_normalized, y_pred)
   r2 = r2_score(y_test_normalized, y_pred)
   
   print(f'RMSE: {rmse}')
   print(f'R2: {r2}')
   

Ensemble Inference Based on Sample Retrieval

For a detailed technical introduction to Ensemble Inference Based on Sample Retrieval, please refer to the technical report.

Considering inference speed and memory requirements, ensemble inference based on sample retrieval currently only supports hardware with specifications higher than the NVIDIA RTX 4090 GPU.

Classification Task

python inference_classifier.py --save_name your_save_name --inference_config_path path_to_retrieval_config --data_dir path_to_data

Regression Task

python inference_regression.py --save_name your_save_name --inference_config_path path_to_retrieval_config --data_dir path_to_data

Customizing Data Preprocessing for Inference Tasks

First, Generate the Inference Configuration File

generate_inference_config()

Classification Task

Single GPU or CPU

python  inference_classifier.py --save_name your_save_name --inference_config_path path_to_retrieval_config --data_dir path_to_data

Multi-GPU Distributed Inference

torchrun --nproc_per_node=8  inference_classifier.py --save_name your_save_name --inference_config_path path_to_retrieval_config --data_dir path_to_data --inference_with_DDP

Regression Task

Single GPU or CPU

python  inference_regression.py --save_name your_save_name --inference_config_path path_to_retrieval_config --data_dir path_to_data

Multi-GPU Distributed Inference

torchrun --nproc_per_node=8  inference_regression.py --save_name your_save_name --inference_config_path path_to_retrieval_config --data_dir path_to_data --inference_with_DDP

Retrieval Optimization Project

This project implements an optimized retrieval system. To achieve the best performance, we utilize Optuna for hyperparameter tuning of retrieval parameters.

Installation

Ensure you have the required dependencies installed:

pip install optuna

Usage

For standard inference using pre-optimized parameters, refer to the code below:

searchInference = RetrievalSearchHyperparameters(
           dict(device_id=0,model_path=model_path), X_train, y_train, X_test, y_test,
)
config, result = searchInference.search(n_trials=10, metric="AUC",
              inference_config='config/cls_default_retrieval.json',task_type="cls")

This will launch an Optuna study to find the best combination of retrieval parameters for your specific dataset and use case.

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6. Tool Invocation

The LimiX model can integrate with various toolchains for extended functionality:

• Data Processing Tools: Integrates with pandas and scikit-learn for data cleaning, feature engineering, and result evaluation (e.g., r2_score, mean_squared_error).

• Hyperparameter Optimization Tools: Optimize retrieval parameters via the hyperopt library, example as follows:

  # Hyperparameter search example (refer to inference_regression.py)
  from utils.inference_utils import sample_inferece_params
  hyperopt_config, base_config = sample_inferece_params(rng, 2, 4)
  model.set_inference_config(inference_config=hyperopt_config, **base_config)
  

• Distributed Inference: Supports DDP (Distributed Data Parallel) mode for multi-GPU acceleration via torch.distributed.

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7. License

1. Code License: The repository code is licensed under the [Apache-2.0 License](LICENSE.txt), allowing commercial use and secondary development with retention of the original copyright notice.

2. Model Weight License: The use of LimiX model weights is subject to a separate Model License:

   • Fully open for academic research without authorization required.

   • Commercial use requires official authorization (refer to the license application process on the StableAI official website).

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8. Third-Party Notices

This project uses the following third-party components, whose usage is governed by their respective licenses:

• PyTorch: BSD-style license

• scikit-learn: BSD license

• flash-attention: MIT License

• Hugging Face Hub: Apache-2.0 License

• For the complete list of dependencies and license information, refer to requirements.txt and the official documentation of each component.

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9. Contact Us

• Official Documentation: https://www.limix.ai/doc/

• GitHub Repository: https://github.com/limix-ldm/LimiX (Submit issues for questions)

• Official Website: https://www.stable-ai.ai/ (For commercial cooperation and license inquiries)

• Technical Report: LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence

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