| """ |
| predictlm_v11 — inference-only public API for the PredictLM tabular FM family. |
| |
| Quick start: |
| |
| from predictlm_v11 import PredictLM |
| model = PredictLM.from_pretrained("zerooneresearch/predictlm-mini-13m") |
| |
| # Zero-tuning (fastest) |
| preds = model.fit(X_train, y_train).predict(X_test) |
| |
| # Test-Time Training (TTT) — +0.07 cls / +0.06 reg over zero-tuning |
| preds = model.fit_and_predict_with_ttt( |
| X_train, y_train, X_test, n_inner=15, lr=1e-4) |
| |
| # Full Duo + TTT recipe (Mini + Base ensemble, best results) |
| from predictlm_v11 import duo_ttt_predict |
| base = PredictLM.from_pretrained("zerooneresearch/predictlm-base-26m") |
| preds = duo_ttt_predict(model, base, X_train, y_train, X_test, w=0.40) |
| |
| PredictLM auto-detects regression vs classification from y_train and routes |
| through the correct head. Same model, single fit/predict for both tasks. |
| |
| The training stack (synthetic SCM, copula augmentation, curriculum, etc.) |
| is NOT shipped here — see https://github.com/zerooneresearch/predictlm-v11 |
| for the full training repo. |
| """ |
| from .inference import PredictLM, PredictLMOutput, duo_ttt_predict |
| from .model import PredictLMv11, V11Config |
|
|
| __all__ = ["PredictLM", "PredictLMOutput", "PredictLMv11", "V11Config", |
| "duo_ttt_predict"] |
| __version__ = "11.1.0" |
|
|
