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Seldon TFM Family
Seldon is a tabular foundation model by Neuralk-AI. It predicts on new rows through in-context learning: you give it labeled example rows as context, and it predicts the target for new rows in a single forward pass, with no training or hyperparameter tuning on your data.
This repo contains seldon-mini for both tasks, as two checkpoints that share one architecture: a classifier (27.4M parameters) and a regressor (28.4M parameters).
Model family
| Variant | Parameters | Tasks | Availability |
|---|---|---|---|
| seldon-mini | 27.4M (classifier), 28.4M (regressor) | classification, regression | Open weights |
| seldon | 213.5M (classifier), 215.5M (regressor) | classification, regression | Open weights |
| seldon API | – | classification, regression | Neuralk API |
Checkpoints
| File | Task | Parameters | Output |
|---|---|---|---|
seldon-mini-classifier.safetensors |
classification | 27.4M | class probabilities, up to 10 classes per model call (more through error-correcting output codes) |
seldon-mini-regressor.safetensors |
regression | 28.4M | 999 quantiles of the target |
Quickstart
pip install "neuralk-oss>=0.3"
Classification
from neuralk_oss import SeldonClassifier
clf = SeldonClassifier.from_pretrained("seldon-mini", device="cuda")
clf.fit(X_train, y_train) # stores the context; no gradient updates
preds = clf.predict(X_test)
proba = clf.predict_proba(X_test)
Regression
from neuralk_oss import SeldonRegressor
reg = SeldonRegressor.from_pretrained("seldon-mini", device="cuda")
reg.fit(X_train, y_train) # y_train: one finite number per row
y_pred = reg.predict(X_test) # the mean of each row's quantiles
quantiles = reg.predict_quantiles(X_test) # (n_rows, 999), ascending
Benchmark
Classification
Head-to-head win rate on TabArena classification (38 datasets, 594 folds), each model with its default settings.
| Baseline (params) | Seldon-Mini classifier (27.4M) win rate |
|---|---|
| Kumo-Tabular Small (27.5M) | 56.7% |
| TabICLv2 (27.6M) | 78.5% |
| Kumo-Tabular Medium (61.5M) | 29.0% |
| TabPFN-3.5-Fast (83.5M) | 54.7% |
| Kumo-Tabular Large (213.7M) | 22.7% |
| TabPFN-3.5 (219.0M) | 44.8% |
| LimiX-2 (406.2M) | 27.6% |
| TabFM (1.64B) | 36.7% |
Regression
Head-to-head win rate on TabArena regression (13 datasets, 222 folds), each model with its default settings.
| Baseline (params) | Seldon-Mini regressor (28.4M) win rate |
|---|---|
| Kumo-Tabular Small (27.5M) | 48.6% |
| TabICLv2 (27.6M) | 90.1% |
| Kumo-Tabular Medium (61.5M) | 22.1% |
| TabPFN-3.5-Fast (83.5M) | 67.6% |
| Kumo-Tabular Large (213.7M) | 17.1% |
| TabPFN-3.5 (219.0M) | 45.5% |
| LimiX-2 (406.2M) | 27.0% |
| TabFM (1.64B) | 68.5% |
Architecture
Seldon follows the same design as Kumo Tabular and TabFM: the table is first compressed into one representation per row, and in-context learning then runs on these row representations.
- Tokenizer: each cell becomes a token through a Fourier feature projection of numerical values, a categorical mask, and a missing-value indicator. Features are grouped before projection.
- Cell-to-row embedding: interleaved column and row transformers (column attention across rows uses inducing points). The result is compressed into CLS readout tokens, giving one dense vector per row.
- ICL layers: a transformer stack over the compressed row representations. Labeled context rows serve as the training set, and the model predicts the query rows.
- Decoder: an MLP head maps each query row's representation to class probabilities (classifier) or to 999 quantiles of the target (regressor). It is the only part whose shape differs between the two checkpoints.
| Part | Classifier | Regressor | Shape |
|---|---|---|---|
| Cell/row embedding | 1.68M | 1.68M | 128 channels, 4 layer pairs, 128 inducing points, 4 readout tokens |
| ICL stack and head | 25.73M | 26.73M | 12 layers, 512 channels, 8 heads; head of 10 classes or 999 quantiles |
| Total | 27.40M | 28.41M |
Pretraining: a synthetic prior
Seldon is pretrained only on synthetically generated datasets drawn from a prior developed by Neuralk. No real-world data was used in pretraining. The prior is built on structural causal models (SCMs): each synthetic task comes from a randomly sampled causal graph whose nodes are linked by randomly drawn functional relationships and noise, and the features and target are read off this graph. The main goal of the prior is diversity. Its new, richer generative mechanisms go beyond classical SCM priors and vary:
- the graph structure
- the kinds of functional dependencies
- feature types and distributions,
- noise, missingness, class balance and dataset shape...
As a result, the model sees a much wider range of tabular problems during pretraining than any collection of real datasets would provide, and learns a general way of doing in-context inference that carries over to real-world tables.
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