stableai-org/LimiX-16M

LimiX

News :boom:

• 2025-08-29: LimiX V1.0 Released.

➤ Overview

We posit that progress toward general intelligence will require different complementary classes of foundation models, each anchored to a distinct data modality and set of inductive biases. large language models (LLMs) provide a universal interface for natural and programming languages and have rapidly advanced instruction following, tool use, and explicit reasoning over token sequences. In real-world scenarios involving structured data, LLMs still rely primarily on statistical correlations between word sequences, which limits their ability to accurately capture numerical relationships and causal rules. In contrast, large structured-data models (LDMs) are trained on heterogeneous tabular and relational data to capture conditional and joint dependencies, support diverse tasks and applications, enable robust prediction under distribution shifts, handle missingness, and facilitate counterfactual analysis and feature attribution. Here, we introduce LimiX, the first installment of our LDM series. LimiX aims to push generality further: a single model that handles classification, regression, missing-value imputation, feature selection, sample selection, and causal inference under one training and inference recipe, advancing the shift from bespoke pipelines to unified, foundation-style tabular learning.

LimiX adopts a transformer architecture optimized for structured data modeling and task generalization. The model first embeds features X and targets Y from the prior knowledge base into token representations. Within the core modules, attention mechanisms are applied across both sample and feature dimensions to identify salient patterns in key samples and features. The resulting high-dimensional representations are then passed to regression and classification heads, enabling the model to support diverse predictive tasks.

For details, please refer to the technical report at the link: LimiX_Technical_Report.pdf

➤ Comparative experimental results

The LimiX model achieved SOTA performance across multiple tasks.

➩ Classification comparison results

➩ Regression comparison results

➩ Missing value imputation comparison results

➤ Tutorials

➩ Installation

Option 1 (recommended): Use the Dockerfile

Download Dockerfile

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

Option 2: Build manually

Download the prebuilt flash_attn files

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 Python 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

Download source code

git clone https://github.com/limix-ldm/LimiX.git
cd LimiX

➤ Inference

LimiX supports tasks such as classification, regression, and missing value imputation

➩ Model download

Model size	Download link	Tasks supported
LimiX-16M	LimiX-16M.ckpt	✅ classification ✅regression ✅missing value imputation

➩ Interface description

Model Creation

class LimiXPredictor:
    def __init__(self,
                 device:torch.device,
                 model_path:str,
                 mix_precision:bool=True,
                 inference_config: list|str,
                 categorical_features_indices:List[int]|None=None,
                 outlier_remove_std: float=12,
                 softmax_temperature:float=0.9,
                 task_type: Literal['Classification', 'Regression']='Classification',
                 mask_prediction:bool=False,
                 inference_with_DDP: bool = False,
                 seed:int=0)

Parameter	Data Type	Description
device	torch.device	The hardware that loads the model
model_path	str	The path to the model that needs to be loaded
mix_precision	bool	Whether to enable the mixed precision inference
inference_config	list/str	Configuration file used for inference
categorical_features_indices	list	The indices of categorical columns in the tabular data
outlier_remove_std	float	The threshold is employed to remove outliers, defined as values that are multiples of the standard deviation
softmax_temperature	float	The temperature used to control the behavior of softmax operator
task_type	str	The task type which can be either "Classification" or "Regression"
mask_prediction	bool	Whether to enable missing value imputation
inference_with_DDP	bool	Whether to enable DDP during inference
seed	int	The seed to control random states

Predict

def predict(self, x_train:np.ndarray, y_train:np.ndarray, x_test:np.ndarray) -> np.ndarray:

Parameter	Data Type	Description
x_train	np.ndarray	The input features of the training set
y_train	np.ndarray	The target variable of the training set
x_test	np.ndarray	The input features of the test set

➩ 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, ensemble inference based on sample retrieval currently only supports hardware with specifications higher than the NVIDIA RTX 4090 GPU.

Classification Task

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

Regression Task

torchrun --nproc_per_node=8 inference_regression.py --save_name your_save_name --inference_config_path path_to_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_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_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_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_config --data_dir path_to_data --inference_with_DDP

➩ Classification

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

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=".")

clf = LimiXPredictor(device='cuda', model_path='your model path', inference_config='config/cls_default_noretrieval.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)))

For additional examples, refer to inference_classifier.py

➩ Regression

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=True)
import os, sys
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

data_device = f'cuda:0'
model_path = hf_hub_download(repo_id="stableai-org/LimiX-16M", filename="LimiX-16M.ckpt", local_dir=".")

model = LimiXPredictor(device='cuda', model_path=model_path, inference_config='config/reg_default_noretrieval.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}')

For additional examples, refer to inference_regression.py

➩ Missing value imputation

For the demo file, see examples/demo_missing_value_imputation.py

➤ Link

• LimiX Technical Report: LimiX_Technical_Report.pdf
• Balance Comprehensive Challenging Omni-domain Classification Benchmark: bcco_cls
• Balance Comprehensive Challenging Omni-domain Regression Benchmark: bcco_reg

➤ License

The code in this repository is open-sourced under the Apache-2.0 license, while the usage of the LimiX model weights is subject to the Model License. The LimiX weights are fully available for academic research and may be used commercially upon obtaining proper authorization.

➤ Reference