# 💥 News
- 2025-11-10: LimiX-2M is officially released! Compared to LimiX-16M, this smaller variant offers significantly lower GPU memory usage and faster inference speed. The retrieval mechanism has also been enhanced, further improving model performance while reducing both inference time and memory consumption.
- 2025-08-29: LimiX V1.0 Released.
# âš¡ Latest Results Compared with SOTA Models
# ➤ Overview
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:Unleashing Structured-Data Modeling Capability for Generalist Intelligence](https://arxiv.org/abs/2509.03505) or [LimiX_Technical_Report.pdf](https://github.com/limix-ldm/LimiX/blob/main/LimiX_Technical_Report.pdf).
# ➤ Superior Performance
The LimiX model achieved SOTA performance across multiple tasks.
## âž© Classification (Tech Report)
## âž© Regression (Tech Report)
## âž© Missing Values Imputation (Tech Report)
# ➤ Tutorials
## âž© Installation
### Option 1 (recommended): Use the Dockerfile
Download [Dockerfile](https://github.com/limix-ldm/LimiX/blob/main/Dockerfile)
```bash
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
```bash
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
```bash
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
```
### Download source code
```bash
git clone https://github.com/limix-ldm/LimiX.git
cd LimiX
```
# ➤ Inference
LimiX supports tasks such as classification, regression, and missing value imputation
## âž© Model download
| Model size | Download link | Tasks supported |
| --- | --- | --- |
| LimiX-16M | [LimiX-16M.ckpt](https://huggingface.co/stableai-org/LimiX-16M/tree/main) | ✅ classification ✅regression ✅missing value imputation |
| LimiX-2M | [LimiX-2M.ckpt](https://huggingface.co/stableai-org/LimiX-2M/tree/main) | ✅ classification ✅regression ✅missing value imputation |
## âž© Interface description
### Model Creation
```python
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
```python
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 |
## Inference Configuration File Description
| Configuration File Name | Description | Difference |
| ------- | ---------- | ----- |
| cls_default_retrieval.json | Default **classification task** inference configuration file **with retrieval** | Better classification performance |
| cls_default_noretrieval.json | Default **classification task** inference configuration file **without retrieval** | Faster speed, lower memory requirements |
| reg_default_retrieval.json | Default **regression task** inference configuration file **with retrieval** | Better regression performance |
| reg_default_noretrieval.json | Default **regression task** inference configuration file **without retrieval** | Faster speed, lower memory requirements |
| reg_default_noretrieval_MVI.json | Default inference configuration file for **missing value imputation task** | |
## âž© Ensemble Inference Based on Sample Retrieval
For a detailed technical introduction to Ensemble Inference Based on Sample Retrieval, please refer to the [technical report](https://github.com/limix-ldm/LimiX/blob/main/LimiX_Technical_Report.pdf).
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
```python
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.
## âž© Classification
```python
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)))
```
For additional examples, refer to [inference_classifier.py](./inference_classifier.py)
## âž© Regression
```python
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}')
```
For additional examples, refer to [inference_regression.py](https://github.com/limix-ldm/LimiX/raw/main/inference_regression.py)
## âž© Missing value imputation
For the demo file, see [demo_missing_value_imputation.py](https://github.com/limix-ldm/LimiX/raw/main/examples/inference_regression.py)
# ➤ Link
- LimiX:Unleashing Structured-Data Modeling Capability for Generalist Intelligence: [LimiX:Unleashing Structured-Data Modeling Capability for Generalist Intelligence](https://arxiv.org/abs/2509.03505)
- LimiX Technical Report: [LimiX_Technical_Report.pdf](https://github.com/limix-ldm/LimiX/blob/main/LimiX_Technical_Report.pdf)
- Detailed instructions for using Limix: [Visit the official Limix documentation](https://www.limix.ai/doc/)
- Balance Comprehensive Challenging Omni-domain Classification Benchmark: [bcco_cls](https://huggingface.co/datasets/stableai-org/bcco_cls)
- Balance Comprehensive Challenging Omni-domain Regression Benchmark: [bcco_reg](https://huggingface.co/datasets/stableai-org/bcco_reg)
# ➤ License
The code in this repository is open-sourced under the [Apache-2.0](LICENSE.txt) 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.
# ➤ Citation
```
@article{LimiX,
title={LimiX:Unleashing Structured-Data Modeling Capability for Generalist Intelligence},
author={LimiXTeam},
journal={arXiv preprint arXiv:2509.03505},
year={2025}
}
```
