diff --git a/ini_file.md b/ini_file.md
index becbd22..498b69b 100644
--- a/ini_file.md
+++ b/ini_file.md
@@ -243,6 +243,11 @@
   * type = svm
   * possible values:
     * **bayes**: Naive Bayes classifier 
+    * **cnn**: Convolutional neural network (only works with feature type=spectra)
+    * **finetune**: Finetune a transformer model with [huggingface](https://huggingface.co/docs/transformers/training). In this case the features are ignored, because audiofiles are used directly. 
+      * **pretrained_model**: Base model for finetuning/transfer learning. Variants of wav2vec2, Hubert, and WavLM are tested to work. Default is facebook/wav2vec2-large-robust-ft-swbd-300h. 
+        * pretrained_model = microsoft/wavlm-base
+
     * **gmm**: Gaussian mixture classifier 
       * GMM_components = 4
       * GMM_covariance_type = [full | tied | diag | spherical](https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_covariances.html)
@@ -250,19 +255,18 @@
       * K_val = 5
       * KNN_weights = uniform | distance
     * **knn_reg**: K nearest neighbor regressor
-    * **tree**: Classification tree classifier 
-    * **tree_reg**: Classification tree regressor
+    * **mlp**: Multi-Layer-Perceptron for classification
+    * **mlp_reg**: Multi-Layer-Perceptron for regression
     * **svm**: Support Vector Machine 
       * C_val = 0.001
       * kernel = rbf # ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’
-    * **xgb**:XG-Boost
     * **svr**: Support Vector Regression
       * C_val = 0.001
       * kernel = rbf # ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’
+    * **tree**: Classification tree classifier 
+    * **tree_reg**: Classification tree regressor
+    * **xgb**:XG-Boost
     * **xgr**: XG-Boost Regression
-    * **mlp**: Multi-Layer-Perceptron for classification
-    * **mlp_reg**: Multi-Layer-Perceptron for regression
-    * **cnn**: Convolutional neural network (only works with feature type=spectra)
 * **tuning_params**: possible tuning parameters for x-fold optimization (for Bayes, KNN, KNN_reg, Tree, Tree_reg, SVM, SVR, XGB and XGR)
   * tuning_params = ['subsample', 'n_estimators', 'max_depth']
     * subsample = [.5, .7]
@@ -303,8 +307,6 @@
   * device = 0
 * **patience**: Number of epochs to wait if the result gets better (for early stopping)
   * patience = 5
-* **pretrained_model**: Base model for finetuning/transfer learning. Variants of wav2vec2, Hubert, and WavLM are tested to work. Default is facebook/wav2vec2-large-robust-ft-swbd-300h. 
-  * pretrained_model = microsoft/wavlm-base
 
 ### EXPL
 * **model**: Which model to use to estimate feature importance.