diff --git a/doc/training.md b/doc/training.md
index 3f90ad70..b25a3311 100644
--- a/doc/training.md
+++ b/doc/training.md
@@ -1,5 +1,5 @@
 
-## Training
+## Training overview
 
 It is common to have light curves on "grids", for which you have a discrete set of parameters for which the lightcurves were simulated. For example, we may know the lightcurves to expect for specific masses m_1 and m_2, but not for any masses between the two.
 
@@ -7,8 +7,9 @@ We rely on sampling from a grid of modeled lightcurves through the use of Princi
 
 At this point, you can model this grid as either a Gaussian process or Neural Network. This will allow you to form a **continuous map** from merger parameters to lightcurve eigenvalues, which are then converted directly to the set of light curve parameters that most likely resulted in this lightcurve.
 
+For a list of example training calls on various model grids using tensorflow, see `tools/tf_training_calls.sh`.
 
-### NMMA training
+### Training details
 
 There are helper functions within NMMA to support this. In particular, `nmma.em.training.SVDTrainingModel` is designed to take in a grid of models and return an interpolation class.
 
diff --git a/tools/tf_training_calls.sh b/tools/tf_training_calls.sh
new file mode 100644
index 00000000..5855bb75
--- /dev/null
+++ b/tools/tf_training_calls.sh
@@ -0,0 +1,40 @@
+# Example tensorflow training calls for different model grids
+
+# model: LANL2022
+# lightcurves: lcs_lanl_TS_wind2
+create-svdmodel --model LANL2022 --svd-path svdmodels_LANL2022 --interpolation-type tensorflow --tmin 0. --tmax 21.0 --dt 0.1 --data-path lcs_lanl_TS_wind2 --tensorflow-nepochs 100 --outdir output_LANL2022_tf --plot
+
+# model: Bu2019lm
+# lightcurves: lcs_bulla_2019_bns
+create-svdmodel --model Bu2019lm --svd-path svdmodels_Bu2019lm --interpolation-type tensorflow --tmin 0. --tmax 21.0 --dt 0.1 --data-path lcs_bulla_2019_bns --tensorflow-nepochs 100 --outdir output_Bu2019lm_tf --plot
+
+# model: Bu2019nsbh
+# lightcurves: lcs_bulla_2019_nsbh
+create-svdmodel --model Bu2019nsbh --svd-path svdmodels_Bu2019nsbh --interpolation-type tensorflow --tmin 0. --tmax 21.0 --dt 0.1 --data-path lcs_bulla_2019_nsbh --tensorflow-nepochs 100 --outdir output_Bu2019nsbh_tf --plot
+
+# model: Bu2022Ye
+# lightcurves: lcs_bulla_2022
+create-svdmodel --model Bu2022Ye --svd-path svdmodels_Bu2022Ye --interpolation-type tensorflow --tmin 0. --tmax 21.0 --dt 0.1 --data-path lcs_bulla_2022 --tensorflow-nepochs 100 --outdir output_Bu2022Ye_tf --plot
+
+# model: Bu2022Ye
+# lightcurves: lcs_bulla_2023
+create-svdmodel --model Bu2023Ye --svd-path svdmodels_Bu2023Ye --interpolation-type tensorflow --tmin 0. --tmax 21.0 --dt 0.1 --data-path lcs_bulla_2023 --tensorflow-nepochs 100 --outdir output_Bu2023Ye_tf --plot
+
+# model: Ka2017 (no smooth)
+# lightcurves: lcs_kasen_no_smooth
+create-svdmodel --model Ka2017 --svd-path svdmodels_Ka2017_no_smooth --interpolation-type tensorflow --tmin 0. --tmax 21.0 --dt 0.1 --data-path lcs_kasen_no_smooth --tensorflow-nepochs 100 --outdir output_Ka2017_no_smooth_tf --plot
+
+# model: Ka2017 (with smooth)
+# lightcurves: lcs_kasen_with_smooth
+create-svdmodel --model Ka2017 --svd-path svdmodels_Ka2017_with_smooth --interpolation-type tensorflow --tmin 0. --tmax 21.0 --dt 0.1 --data-path lcs_kasen_with_smooth --tensorflow-nepochs 100 --outdir output_Ka2017_with_smooth_tf --plot
+
+# model: AnBa2022_log
+# lightcurves: lcs_collapsar
+create-svdmodel --model AnBa2022_log --svd-path svdmodels_AnBa2022_log --interpolation-type tensorflow --tmin 0.0 --tmax 21.0 --dt 0.1 --data-path lcs_collapsar --data-file-type hdf5 --plot --tensorflow-nepochs 100 --data-time-unit seconds --outdir output_AnBa2022_log_tf
+
+# model: AnBa2022_linear
+# lightcurves: lcs_collapsar
+create-svdmodel --model AnBa2022_linear --svd-path svdmodels_AnBa2022_linear --interpolation-type tensorflow --tmin 0.0 --tmax 21.0 --dt 0.1 --data-path lcs_collapsar --data-file-type hdf5 --plot --tensorflow-nepochs 100 --data-time-unit seconds --outdir output_AnBa2022_linear_tf
+
+# Use svdmodel-benchmark to generate additional performance plots. It takes many of the same arguments as create-svdmodel above, except for --tensorflow-nepochs and --plot.
+# (Note that an error is currently raised if --ncpus is > 1 in svdmodel-benchmark, see Issue #125)