license: other
license_name: tabpfn-3-license-v1.0
license_link: LICENSE
pipeline_tag: tabular-regression
tags:
- tabular
- regression
- agriculture
- maize-yield
- tabpfn-3
Model Overview
tabpfn-v3-regressor-cn_finetuned_full.ckpt is the full training-snapshot of the
fine-tuned derivative of TabPFN-v3
(tabpfn-v3-regressor-v3_default), fine-tuned on China provincial maize yield data
(new dataset: feature_dataset.csv).
This is the resumable checkpoint. Besides the fine-tuned state_dict it also
contains the AdamW optimizer state (exp_avg / exp_avg_sq), epoch and loss
metadata — i.e. the full ~444 MB of optimizer buffers that make a 668 MB file. Use this
file to continue fine-tuning from the best epoch.
Fine-tuning summary
- Data:
feature_dataset.csv, years 2003–2022, 602 rows, 31 provinces - Split: train 2003–2016 (422) / val 2017–2019 (90) / test 2020–2022 (90); 96 features
- Best val MSE 0.2716 at epoch 26 of 30 (early-stop patience 5)
- lr 3e-6, wd 0.01, MSE 1.0 / CRPS 1.0 / MAE 0.3, context 256, seed 42
Internal test (2020–2022, 90 rows, 16-ensemble) vs base model
| Metric | Base | CN-finetuned |
|---|---|---|
| RMSE | 0.6865 | 0.6207 |
| R² | 0.6308 | 0.6981 |
| MAE | 0.4592 | 0.4494 |
| Pearson r | 0.8681 | 0.8914 |
| KGE | 0.8507 | 0.8554 |
Usage for inference
from tabpfn import TabPFNRegressor
reg = TabPFNRegressor(model_path="tabpfn-v3-regressor-cn_finetuned_full.ckpt",
device="cuda", n_estimators=16, random_state=42)
reg.fit(X_train, y_train)
y_pred = reg.predict(X_test)
File integrity
- Full snapshot ckpt SHA-256:
53119ca48958a7b7eb5ca06752eecc2348f0b2ffa58d45d4f5ae79f02793f630
Licensing
Model weights are released under the TABPFN-3 Non-Commercial License v1.0. The full
license text is included in LICENSE and must be passed on to any third party
who receives this model or a derivative of it.
This model is a modified derivative. tabpfn-v3-regressor-cn_finetuned_full.ckpt is a
fine-tuned version of tabpfn-v3-regressor-v3_default. It is not an official product
of Prior Labs GmbH and has not been endorsed, approved, or validated by the Company.
Research and internal evaluation are allowed; any commercial or production use is prohibited. For commercial use contact sales@priorlabs.ai.
Per §3.d of the license, this model and its derivatives must not be distributed, hosted, or made available as part of a hosted, managed, API, or SaaS service.
Required attribution notice:
The TABPFN-3 Model is licensed by Prior Labs GmbH under the TABPFN-3 Non-Commercial License.
Copyright © Prior Labs GmbH 2026.
THE SERVICES ARE PROVIDED FREE OF CHARGE: COMPANY SHALL NOT BE LIABLE FOR DAMAGES RESULTING FROM SLIGHT NEGLIGENCE. LIABILITY FOR GROSS NEGLIGENCE AND INTENTIONAL MISCONDUCT REMAINS UNAFFECTED.