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EXAONE-Tabular

Name: EXAONE-Tabular
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EXAONE Tabular

EXAONE Tabular is a transformer-based foundation model for tabular data that solves classification and regression through in-context learning: you pass the labeled rows to fit and the model predicts new rows in a single forward pass — no gradient updates and no per-dataset training.

This repository is the exaonetabular inference runtime — a self-contained package that loads a released checkpoint and serves predictions through a small, scikit-learn-style API. The code here is permissively licensed; the released weights are non-commercial — see License.

Both checkpoints are released: EXAONETabularClassifier and EXAONETabularRegressor each fetch their own weights with a single from_pretrained() call. See Available checkpoints.

For more details, please refer to the GitHub repository and our technical report.

Model Configuration

Model Type: In-context tabular foundation model (Cross-axis Summary Transformer (CAST))
Embedding dimension: 192
Attention heads: 6
Transformer layers: 12
Feed-forward expansion: 4x
MLP sharing: Single
Feature-attention operations per layer: 2
Feature-level summary tokens: 3
Row-level summary tokens: 32
Attention normalization: SSMax
Total parameters
- Classification: 20,807,866 (≈20.8M)
- Regression: 21,110,247 (≈21.1M)

Evaluation Results

TabArena

EXAONE Tabular achieves an overall Elo of 1,755 on TabArena without per-dataset tuning or ensembling. It ranks second overall, first on classification with an Elo of 1,759, and second on regression with an Elo of 1,883, while using approximately 21M parameters.

All TabArena results reported below are taken from the official TabArena leaderboard.

Overall TabArena Elo versus model parameters

Figure 1. Overall TabArena Elo versus model size. EXAONE Tabular achieves competitive performance with approximately 21M parameters.

TabArena classification Elo on the left and regression Elo on the right

Figure 2. TabArena Elo scores on classification (left) and regression (right) tasks.

Table 1. TabArena Elo scores and computational costs. Training and prediction costs are median seconds per 1,000 samples.

Model type	Model	Configuration	Overall Elo ↑	Cls. Elo ↑	Reg. Elo ↑	Cost: train / predict (s/1K) ↓
Foundation Model	EXAONE Tabular	Default	1,755	1,759	1,883	5.79 / 0.605
Foundation Model	TabFM	Default	1,765	1,746	1,993	38.81 / 6.985
Foundation Model	TabPFN-3	Default	1,642	1,635	1,793	3.66 / 0.399
Foundation Model	TabPFN-2.6	Default	1,592	1,586	1,734	5.48 / 0.555
Foundation Model	RealTabPFN-2.5	Tuned + ensembled	1,572	1,562	1,731	2,040.22 / 8.908
Foundation Model	TabICLv2	Default	1,569	1,574	1,672	2.05 / 0.151
Neural Network	RealMLP	Tuned + ensembled	1,482	1,468	1,648	2,950.72 / 11.975
Foundation Model	TabDPT	Tuned + ensembled	1,437	1,398	1,715	4,910.38 / 286.537
Neural Network	TabM	Tuned + ensembled	1,426	1,446	1,449	2,450.13 / 2.247
Tree-based	LightGBM	Tuned + ensembled	1,410	1,415	1,477	417.05 / 2.639
Tree-based	CatBoost	Tuned + ensembled	1,398	1,398	1,484	1,346.21 / 0.344
Tree-based	XGBoost	Tuned + ensembled	1,357	1,364	1,404	693.49 / 1.689
Foundation Model	TabSwift	Default	1,334	1,339	1,397	1.18 / 0.072
Foundation Model	Nori-30M	Default	1,156	—	1,753	0.53 / 0.080

Elo scores are reported for the same configuration shown in the Configuration column. Cost is the median training and prediction time per 1,000 samples from the overall TabArena results; lower is better. Nori-30M has no classification result in the source data.

ScoringBench

The results below are taken from the official ScoringBench leaderboard. The reported average ranks were computed against the full ScoringBench comparison pool of 51 model entries across 102 datasets. Figure 3 visualizes a selected set of 28 models, while Table 2 displays only the 10 model families that overlap with the TabArena table; neither the figure nor the table recomputes ranks on these subsets.

Figure 3. Mean-rank comparison on ScoringBench across R² and CRPS; lower ranks are better.

Table 2. ScoringBench average ranks for models also included in the TabArena comparison. Lower is better.

Model	R² average rank ↓	CRPS average rank ↓
EXAONE Tabular	8.75	6.07
TabPFN-3	10.03	6.16
Nori-30M	14.90	11.59
TabICLv2	15.99	11.88
TabPFN-2.6	16.87	18.89
RealTabPFN-2.5	20.70	19.96
CatBoost	33.52	34.62
TabM	35.95	33.79
RealMLP	36.14	34.05
XGBoost	38.83	38.76

The benchmark-specific ScoringBench entries for RealTabPFN-2.5, CatBoost, TabM, RealMLP, and XGBoost are tabpfn_realv2_5, catboost_quantile, tabm_d, pytabkit_realmlp_td, and xgb_vector, respectively. Models are matched across the two benchmarks by model family, so their configurations may differ: for example, Table 1 reports the tuned-and-ensembled RealTabPFN-2.5, whereas Table 2 uses the tabpfn_realv2_5 entry evaluated by ScoringBench.

Requirements

Python ≥ 3.11
PyTorch ≥ 2.6, < 3 (a CUDA GPU is strongly recommended — the model uses fused attention kernels and half precision; CPU inference works but is slow)
NumPy ≥ 2.3.5 · scikit-learn ≥ 1.7.2 · safetensors ≥ 0.4 · huggingface_hub ≥ 0.24 (floors are the versions this release was validated against)

Install the package — the dependencies above come with it:

pip install "exaonetabular @ git+https://github.com/LGAI-Research/EXAONE-Tabular.git"

From a checkout, pip install . (add -e for an editable install) or uv sync do the same.

huggingface_hub is included, so from_pretrained can fetch the released weights out of the box. Downloads honor the standard Hub environment (HF_HOME for the cache, HF_TOKEN for a gated repo).

Verify the install:

python

import exaonetabular
print(exaonetabular.__version__)

Dependency ranges are declared in pyproject.toml (distribution name exaonetabular).

Quickstart

EXAONE Tabular ships as scikit-learn-style estimators. EXAONETabularClassifier and EXAONETabularRegressor both expose the familiar fit / predict surface, return self from fit, and set the usual fitted attributes — classes_, n_classes_ and n_features_in_ on the classifier, n_features_in_ on the regressor — so they slot into the workflow you already use, including as the final step of a sklearn.pipeline.Pipeline. predict_proba is classification only; the regressor returns point estimates from predict.

from_pretrained handles the rest in one call: it fetches that task's released checkpoint from the Hub, builds the model from its frozen manifest, and loads the weights. The repo id, revision, and architecture are baked into the package for both tasks, so there is nothing to configure by hand.

Both snippets below run as written, on a stock scikit-learn dataset.

Inputs are NumPy arrays. X is 2-D float (rows × features); y is 1-D — class labels for classification, real values for regression. Anything else raises TypeError: features must be a NumPy array.

scikit-learn interop. These estimators implement the estimator interface — including __sklearn_is_fitted__ and __sklearn_tags__, so check_is_fitted, is_classifier / is_regressor, and use as the final step of a Pipeline all work. They do not subclass BaseEstimator, so there is no get_params / set_params / score, and clone, cross_val_score, and GridSearchCV are therefore not supported.

Classification

python

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

from exaonetabular import EXAONETabularClassifier

X_train, X_test, y_train, y_test = train_test_split(
    *load_breast_cancer(return_X_y=True), test_size=0.25, random_state=0
)

clf = EXAONETabularClassifier.from_pretrained(device="cuda:0")   # download + verify + load

clf.fit(X_train, y_train)              # no training — stores context + fits preprocessors
proba  = clf.predict_proba(X_test)     # (n_samples, n_classes)
labels = clf.predict(X_test)           # (n_samples,)

Datasets with more than the model's class capacity are handled automatically via ECOC; tables wider than the feature limit are reduced by built-in feature selection.

Regression

python

from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split

from exaonetabular import EXAONETabularRegressor

X_train, X_test, y_train, y_test = train_test_split(
    *load_diabetes(return_X_y=True), test_size=0.25, random_state=0
)

reg = EXAONETabularRegressor.from_pretrained(device="cuda:0")   # download + verify + load

reg.fit(X_train, y_train)          # y: (n,) real-valued, finite — fits ensemble weights
y_pred = reg.predict(X_test)       # (n_samples,) float64 point estimates

The head predicts a 999-quantile distribution per row, which predict reduces to one number: by default a trimmed mean — the trapezoidal average over the central 99.8% of the quantile function, sorted first so crossed quantiles cannot flip the order. That targets the conditional mean, which is what RMSE scores and what the median misses on skewed targets. To read the median quantile instead, pass a manifest= whose RegressionConfig sets point_estimate="median".

The ensemble members are then weighted, not averaged. fit holds out 20% of the support set, predicts it from the rows that remain, and solves for non-negative member weights by least squares (NNLS), rescaled to sum to one and blended 75/25 with the uniform 1/E. Members whose preprocessing rule suits your table earn more of the vote; non-negativity keeps the result a convex combination, and the blend bounds how far a fit on a small split can stray from the uniform prior. This costs one extra forward pass inside fit — predict stays single-pass.

The fit needs 2000 held-out rows (nnls_min_validation_rows), so it engages from roughly 10k support rows up; smaller tables log a warning and stay on the uniform mean, because a handful of weights fitted against a few dozen rows is where the solve degenerates. Set RegressionConfig.member_weighting="uniform" to switch it off entirely.

Targets are standardized against the fitted support set and the prediction is mapped back, so y needs no scaling of your own — but it must be finite; NaN/inf targets raise. Tables wider than 1024 columns are narrowed by univariate f_regression (see Feature selection).

NaNs and categoricals. X must be numeric — encode string/categorical columns to numeric codes before fit (e.g. a stable ordinal map), leaving unseen/missing values as NaN. The built-in preprocessor mean-imputes NaNs; it does not encode raw strings.

Overrides

from_pretrained accepts optional overrides without leaving the one-call path:

python

clf = EXAONETabularClassifier.from_pretrained(
    device="cuda:0",
    compute_dtype="bfloat16",  # wider exponent range (default: "float16")
    ensemble_count=8, seed=0,  # runtime knobs
    revision="main",           # pin a specific Hub revision (tag or commit sha)
    max_vram_bytes=24 << 30,   # cap the GPU memory budget (see Out-of-memory below)
)

# Load your own weights of the same architecture — a local file or a Hub repo id.
# The released SHA-256 pin only applies to the released file, so it is not enforced
# here (a warning is logged); shapes, dtype, and finiteness are still validated.
clf = EXAONETabularClassifier.from_pretrained(weights="/path/to/my-classifier.safetensors")

You can also redirect the weights without touching code via the environment: EXAONETABULAR_CLASSIFIER_WEIGHTS / EXAONETABULAR_REGRESSOR_WEIGHTS (a local path or a repo id).

Precision. The released weights are stored in float32. With the default compute_dtype="float16" they are cast to fp16 at load — the tested runtime path. fp16 is the default because it is the more precise of the two half formats at the same footprint and throughput — 10 mantissa bits to bf16's 7 — and this model's activations stay far from fp16's 65504 ceiling, so bf16's wider exponent range buys nothing here. The two score the same in our classification benchmarking; prefer compute_dtype="bfloat16" only if your inputs can drive activations to that ceiling. compute_dtype="float32" is a CPU-only path: part of the attention stack is pinned to the FlashAttention kernel, which implements fp16 and bf16 only, so a float32 forward on a CUDA device fails with RuntimeError: No available kernel.

Advanced: fully custom checkpoint (explicit manifest)

from_pretrained is a thin layer over the low-level API. For a checkpoint with a different architecture, describe it with an InferenceManifest and load it explicitly — this is the same API the released presets are built from:

python

from huggingface_hub import hf_hub_download
from exaonetabular import (
    EXAONETabularClassifier,
    InferenceManifest,
    ModelConfig,
    RuntimeConfig,
    load_classifier_checkpoint,
)

CKPT = hf_hub_download("your-org/your-repo", "your-classifier.safetensors")
manifest = InferenceManifest(
    task="classification",
    model=ModelConfig(class_capacity=10),       # must match the checkpoint's class-head width
    runtime=RuntimeConfig(ensemble_count=8, compute_dtype="float16", seed=0),
)

clf = EXAONETabularClassifier(manifest, device="cuda:0")   # builds the model
load_classifier_checkpoint(CKPT, clf.model, manifest)      # validates + loads weights

A classification manifest carries a ClassificationConfig too. Leaving it off, as above, fills in the defaults; n_svd=0 therefore leaves support-SVD augmentation disabled. To opt in, import ClassificationConfig and pass classification=ClassificationConfig(n_svd=8) to the manifest. This changes inference preprocessing only and does not require different checkpoint weights.

Regression is analogous with EXAONETabularRegressor, load_regressor_checkpoint, and a RegressionConfig. Two of its fields describe the checkpoint and must match it — quantile_count=999 and decoder_hidden_width=384 — while point_estimate, the n_svd/svd_* fields, and the member_weighting/nnls_* fields are readout and ensembling choices you can change without touching the weights.

The frozen manifests the released estimators use live in presets.py and are reachable via released_manifest("classification" | "regression").

Feature selection (wide tables)

The classifier accepts tables of any width, but the model itself reads at most 100 columns. When fit receives a wider table, it chooses which columns to keep using the model's own attention — there is no flag, and nothing to configure:

python

clf = EXAONETabularClassifier.from_pretrained(device="cuda:0")
clf.fit(X_train, y_train)          # X_train: (n, 5000) — selection runs here

clf.n_features_in_                 # 5000 — the public width does not change
clf.selected_feature_indices_      # (100,) int64, the columns actually kept
clf.predict_proba(X_test)          # still takes all 5000 columns

How it works. One forward pass over a ≤512-row sample of the fitted table, with the feature-attention blocks instrumented. Two signals are read per column — attention from the target row, and the summed attention from the item-summary rows — each weighted by the value-vector norm so the score reflects information actually routed through the attention path rather than raw attention probability. The two are min-max normalized, averaged, and the top 100 columns are kept.

What to expect.

Narrow tables (n_features ≤ 100) skip this entirely — the pass does not run.
Selection is internal. n_features_in_, predict, and predict_proba all keep the original width; the fitted column subset is reapplied for you.
It costs one extra forward pass per fit on a wide table. A GPU is strongly recommended, and in this version there is no way to disable it.
Classification only — see below for the regressor.

The configuration is frozen in config.py as FEATURE_SELECTION. It belongs to the architecture rather than to any one checkpoint — the scorers name the model's token layout, so the same settings apply to every classifier checkpoint of this architecture.

Optional support-SVD augmentation. By default, the classifier does not append support-SVD components (ClassificationConfig.n_svd=0). Set n_svd to a positive integer—for example, n_svd=8—to append that many components to every ensemble member's features, with each member projecting onto its own basis. Once enabled, this augmentation is unconditional: unlike the regressor, classification has no small-table exemption or un-augmented comparison arm because probability aggregation averages members rather than fitting member weights.

The regressor narrows differently. EXAONETabularRegressor reads up to 1024 columns and trims anything wider with univariate f_regression — an F-test against the target, so it costs no extra forward pass and uses no attention. It then appends 16 support-SVD components to every ensemble member's features (RegressionConfig.n_svd), each member projecting onto its own basis, so the model sees the kept columns plus that augmentation.

Two knobs control when that augmentation applies, both settled once in fit against the whole support:

svd_gate (default False) withholds it from small, narrow, all-numeric tables — fewer than 1000 rows and fewer than 10 columns and no categorical column — where a near-full-rank SVD only restates the input. Off by default because the split below already prices the augmentation by weight; set True to make the exemption an all-or-nothing decision instead.
svd_split (default True) runs two ensembles instead of one, an un-augmented pass and an augmented pass, and pools their members into a single prediction so the weight fit decides how much the augmentation is worth rather than the gate deciding all-or-nothing. It doubles both the member count and the forwards, at predict as well as fit. Both passes share the run's seed, so a member differs across them only by the augmentation. With nothing to contrast against — exempted by the gate, or n_svd=0 — a split run collapses back to the single pass.

Controlling the GPU memory budget

Before running, the estimator measures the GPU, plans one execution strategy that fits a memory budget (how many ensemble members run at once, how query rows and feed-forward tokens are chunked, whether the support cache is offloaded), and executes that plan. max_vram_bytes sets the budget explicitly:

clf = EXAONETabularClassifier.from_pretrained(device="cuda:0", max_vram_bytes=24 << 30)

It is a hard cap, in bytes, and CUDA-only: the planner both prefers to stay under it and treats it as the feasibility limit, so it will chunk more aggressively to fit and will refuse — rather than quietly exceed it — a forward whose smallest possible plan does not. Left unset, the budget is everything your process can address: total VRAM minus what other processes already hold.

To spend a proportion of the GPU, compute the bytes yourself — there is no separate fraction argument, because the proportion is only meaningful once you choose what it is a proportion of:

python

import torch

free, total = torch.cuda.mem_get_info(0)   # free = unused now, total = card capacity
clf = EXAONETabularClassifier.from_pretrained(
    device="cuda:0",
    max_vram_bytes=int(0.7 * free),    # 70% of what is actually free right now
)

Pick the denominator deliberately. total is the card's capacity; free is what is unused at that moment. On a shared GPU a fraction of total can exceed what your process is able to obtain, which plans a forward that cannot run — use free unless you own the whole device. Note also that the planner already keeps a ~10% safety margin against the budget on the memory-heaviest build phases, so a budget of B is planned to roughly 0.9B; there is no need to discount twice.

Out-of-memory and memory fragmentation

Large support sets on a memory-constrained GPU can trigger a CUDA out-of-memory error. The error is raised to you unchanged. Inference plans once and runs that plan; it does not catch the OOM, shrink the budget, and silently retry. Recovering costs GPU time and is a policy decision — retry smaller, fall back to CPU, fail the request — so it belongs to the caller:

python

try:
    proba = clf.predict_proba(X)
except torch.cuda.OutOfMemoryError:
    # Your policy: e.g. re-fit with a lower max_vram_bytes or ensemble_count.
    ...

Before concluding the model does not fit, check whether the failure is external fragmentation rather than a true capacity limit. In the CUDA error, compare the amount it tried to allocate against the reserved but unallocated figure: when a large amount is reserved-but-unallocated yet a much smaller allocation fails, the data would fit but the caching allocator cannot place a single contiguous block — that is fragmentation, not lack of memory.

For that case, run with PyTorch's expandable-segments allocator. It lets the allocator grow and coalesce segments, which largely removes contiguous-block fragmentation:

PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True python your_script.py

It is a process-global setting and must be present in the environment before CUDA initializes — set it when launching the process, not from inside Python after torch has already allocated. It changes only the allocator; results are unaffected.

If it still OOMs with expandable segments, the working set genuinely exceeds VRAM. Reduce the footprint instead, roughly in order of cost to accuracy:

Lower max_vram_bytes. A smaller budget makes the planner chunk harder: slower, but the same computation — chunking splits batch dimensions and does not change the model. Chunked and unchunked results agree to numerical tolerance rather than bit-for-bit, which is visible only in reduced precision.
Lower ensemble_count (a from_pretrained override) — fewer ensemble members is directly less work and less memory, at some accuracy cost.
Shrink the in-context support set via the low-level RuntimeConfig(support_row_limit=…) manifest path. This is the only lever on the memory floor that grows with support rows, and the most costly to accuracy.
Use a larger GPU.

Available checkpoints

File	Task	Head	Dtype	Notes
`exaone-tabular-classifier-v1_default.safetensors`	Classification	10-class	float32	`> class_capacity` classes handled automatically via ECOC
`exaone-tabular-regressor-v1_default.safetensors`	Regression	999 quantiles	float32	Read out as a trimmed mean over the quantiles; needs a `RegressionConfig` in its manifest

Both live in the same Hub repository, and each estimator's from_pretrained() fetches its own file — there is no shared dual-head checkpoint, and a classifier file will not load into the regressor.

Each checkpoint's architecture is frozen and must match its InferenceManifest; a mismatched file (wrong keys, shapes, or dtype) fails loudly at load — never silently. The regression loader is stricter still: the file must carry a quantile_levels buffer in float32 that equals linspace(1/1000, 999/1000, 999) exactly, so a head of a different width or spacing is rejected rather than silently reinterpreted.

InferenceManifest.checkpoint_sha256 can additionally pin one exact file. The released manifests in presets.py leave it None, so every load — both tasks — logs a warning saying the bytes were not integrity-checked; the structural validation above still runs. Set it to pin one exact byte stream, and a checkpoint whose digest differs is rejected.

Intended use

EXAONE Tabular is intended for supervised tabular classification and regression on structured (row/column) data, for datasets within the tested sample/feature envelope. High-dimensional inputs are handled by built-in feature selection; large support sets are subsampled. Use of the released weights is limited to non-commercial research and educational purposes by the EXAONE model license.

Not intended for: unstructured data (images, raw text, audio, video); inputs substantially beyond the tested envelope, where accuracy and runtime are not guaranteed; any commercial use of the released weights, or any use excluded by the license.

Limitation

Class-Count Handling. The native classification head supports up to 10 classes. Datasets with larger label spaces are handled through an ECOC-based decomposition at inference time. This procedure requires multiple binary predictions and therefore increases inference cost as the number of classes grows. A class- count-independent prediction head is a potential direction for future work.

Large-Context Inference. Query chunking controls peak query-side memory because query predictions are conditionally independent given the support set. However, the current inference wrapper recomputes the support representations for each estimator and query chunk, introducing redundant computation when either the ensemble size or the number of query chunks is large. The model already provides a support-side caching path for row-axis attention, but this path is not yet used by the default chunked-inference wrapper. Activating support-representation caching could reduce repeated computation across query chunks. Support sets beyond the configured inference limit are currently subsampled. Potential future directions include support-side representation and KV caching, context compression, representative-context selec- tion, clustering-based support reduction, retrieval-based context construction, memory-efficient attention, and adaptive support-set sampling. These methods require systematic evaluation of the trade-offs among inference latency, memory consumption, support compression, and predictive performance.

License

Two licenses apply, to two different things:

The code in this repository — the exaonetabular inference runtime — is released under the BSD-3-Clause-LG AI Research License, which permits commercial use.
The released model weights are licensed separately under the EXAONE AI Model License Agreement 1.2 - NC, which limits use to non-commercial research and education. The full terms ship with the weights on the Hugging Face repository.

Installing this package therefore does not grant commercial rights to the weights it downloads.

Citation

If you find EXAONE Tabular useful in your research, please cite our technical report:

bibtex

@article{eo2026exaonetabular,
  title         = {EXAONE Tabular 1.0: Technical Report},
  author        = {Eo, Moonjung and Suh, Min-Kook and Cho, Hye-Seung and Kim, Jiwon and Kim, Seoyoon and Nam, Sangjun and Lee, Soonyoung},
  year          = {2026},
  eprint        = {2608.25774},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG}
}

Contact

LG AI Research Technical Support: contact_us@lgresearch.ai

LG-AI-Research/EXAONE-Tabular

EXAONE Tabular

Model Configuration

Evaluation Results

TabArena

ScoringBench

Requirements

Quickstart

Overrides

Feature selection (wide tables)

Controlling the GPU memory budget

Out-of-memory and memory fragmentation

Available checkpoints

Intended use

Limitation

License

Citation

Contact

LG-AI-Research/
EXAONE-Tabular