potentially inconsistent labels, ValueError: Sign not allowed in string format specifier

[mhellstr]

I'm unable to train models, getting the below error for the specified version and command

sgdml 1.0.2 [Python 3.8.12, NumPy 1.24.4, SciPy 1.8.0, PyTorch 1.13.1+cu117, ASE 3.22.1]

python bin/sgdml all md17_ethanol.npz 100 100 100

 STEP 2  Training and validation
----------------------------------------------------------------------------------------------------
Task 1 of 9                       100 + 100 points (training + validation), sigma (length scale): 10
[100%] Generating descriptors and their Jacobians                                                   
[INFO] Using analytic solver (expected memory use: ~167 MB)
[DONE] Assembling kernel matrix                                                           took 1.5 s
[DONE] Training on 100 points                                                                       
[WARN] Potentially inconsistent energy labels detected!
       The predicted energies for the training data are only weakly correlated with the reference
       labels (correlation coefficient -0.25). Note that correlation is independent of scale, which
       indicates that the issue is most likely not just a unit conversion error.
       
       Troubleshooting tips:
       (1) Verify the correct correspondence between geometries and labels in the provided dataset.
       (2) This issue might very well just be a sympthom of using too few trainnig data and your
           labels are correct.
       (3) Verify the consistency between energy and force labels.
           - Correspondence between force and energy labels correct?
           - Accuracy of forces (convergence of your ab-initio calculations)?
           - Was the same level of theory used to compute forces and energies?
       (4) Is the training data spread too broadly (i.e. weakly sampled transitions between example
           clusters)?
       (5) Are there duplicate geometries in the training data?
       (6) Are there any corrupted data points (e.g. parsing errors)?
[WARN] Potentially inconsistent scales in energy vs. force labels detected!
       The integrated force predictions differ from the reference energy labels by factor ~0.00 (for
       the training data), meaning that this model will likely fail to predict energies accurately
       in real-world use.
       
       Troubleshooting tips:
       (1) Verify consistency of units in energy and force labels.
       (2) This issue might very well just be a sympthom of using too few trainnig data and your
           labels are correct.
       (3) Is the training data spread too broadly (i.e. weakly sampled transitions between example
           clusters)?
[100%] Validation errors (MAE/RMSE): energy 18265411872728781722082082816.000/22655316095867156075506565120.000, forces 81967145953778761280568426496.000/167843964717341591313296916480.000[CRIT] Traceback (most recent call last):
         File "/home/hellstrom/software/sGDML/sgdml/cli.py", line 2288, in main
           getattr(sys.modules[__name__], args['command'])(**args)
         File "/home/hellstrom/software/sGDML/sgdml/cli.py", line 689, in all
           model_dir_or_file_path = train(
         File "/home/hellstrom/software/sGDML/sgdml/cli.py", line 1119, in train
           valid_errs = test(
         File "/home/hellstrom/software/sGDML/sgdml/cli.py", line 1688, in test
           ui.callback(
         File "/home/hellstrom/software/sGDML/sgdml/utils/ui.py", line 139, in callback
           color_str(' {:>{width}}'.format(sec_disp_str, width=w), fore_color=GRAY)
       ValueError: Sign not allowed in string format specifier