Comb

Weights of Comb, a tabular in-context classifier whose training set can be edited exactly: add, forget and merge rows without retraining. Code, docs and results: https://github.com/m-herre/comb.

pip install tabcomb

from comb import CombClassifier

clf = CombClassifier().fit(X_train, y_train)   # downloads these weights on first use
proba = clf.predict_proba(X_test)
clf.add(X_new, y_new); clf.forget(X_old, y_old)

Architecture	TabICLv2 network (column set transformer, row transformer, 12-block in-context stage) with the in-context stage over at most M = 4096 class-conditional cells (default; set with cell_m)
Parameters	27,552,258 (fp32, 110 MB)
Pretraining	from scratch on synthetic data only (TabICLv2's structural causal prior), 500k + 40k + 10k steps with the cell budget drawn from 128 to 2048 per step, then a 5k-step fine-tune with M from 512 to 8192 and a more realistic prior mix
Task	classification, 2 to 10 classes (more through a hierarchical fallback)
TALENT, 106 held-out classification tasks (8 members, fp32)	AUC −0.37 pp [−0.58, −0.19] vs TabICLv2, +2.8 pp vs CatBoost, +3.6 pp vs XGBoost (their defaults)
TabArena-Lite / BeyondArena Elo (self-run, official protocol, defaults)	1443 / 1167
Version	1.0.0
License	Apache-2.0 (code and weights); architecture ported from TabICL (BSD-3-Clause)

Files: config.json (constructor arguments of comb.model.TabICL) and model.safetensors.