diff --git a/extract-small-model-cli.py b/extract-small-model-cli.py
new file mode 100644
index 0000000..957cf94
--- /dev/null
+++ b/extract-small-model-cli.py
@@ -0,0 +1,19 @@
+import sys
+from train.process_ckpt import extract_small_model
+
+path = str(sys.argv[1])
+name = str(sys.argv[2])
+sample_rate = str(sys.argv[3])
+if_f0 = int(sys.argv[4])
+if(len(sys.argv) == 5):
+    info = ""
+else:
+    info = str(sys.argv[5])
+
+print("Mangio-RVC-Fork Small Model Extraction: Performing extraction...")
+extraction = extract_small_model(path, name, sample_rate, if_f0, info)
+print("Mangio-RVC-Fork Small Model Extraction: %s" % (extraction))
+if(extraction == "Success."):
+    print("Mangio-RVC-Fork Small Model Extraction: Placed %s.pth into ./weights" % (name))
+
+
diff --git a/train-index-cli.py b/train-index-cli.py
index b273f6e..01adc42 100644
--- a/train-index-cli.py
+++ b/train-index-cli.py
@@ -1,4 +1,5 @@
 # Fork Feature Mangio RVC Fork. Train the feature index (faiss) through the cli
+# Please note that the train_index function should probably be exported into a separate file that both the web ui uses and the cli version uses to prevent code duplication.
 
 import os, sys, warnings, shutil, numpy as np
 import faiss
@@ -38,10 +39,10 @@ def train_index(exp_dir1):
     np.save("%s/total_fea.npy" % exp_dir, big_npy)
     # n_ivf =  big_npy.shape[0] // 39
     n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
-    yield "%s,%s \n" % (big_npy.shape, n_ivf)
+    yield "%s,%s" % (big_npy.shape, n_ivf)
     index = faiss.index_factory(256, "IVF%s,Flat" % n_ivf)
     # index = faiss.index_factory(256, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
-    yield "training the index...\n"
+    yield "training the index..."
     index_ivf = faiss.extract_index_ivf(index)  #
     # index_ivf.nprobe = int(np.power(n_ivf,0.3))
     index_ivf.nprobe = 1
@@ -51,7 +52,7 @@ def train_index(exp_dir1):
         "%s/trained_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
     )
     # faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan.index'%(exp_dir,n_ivf))
-    yield "adding the index... \n"
+    yield "adding the index..."
     batch_size_add = 8192
     for i in range(0, big_npy.shape[0], batch_size_add):
         index.add(big_npy[i : i + batch_size_add])
@@ -61,7 +62,7 @@ def train_index(exp_dir1):
     )
     # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan.index'%(exp_dir,n_ivf))
     # infos.append("成功构建索引，added_IVF%s_Flat_FastScan.index"%(n_ivf))
-    yield "Done! added_IVF%s_Flat_nprobe_%s.index \n" % (n_ivf, index_ivf.nprobe)
+    yield "Done! added_IVF%s_Flat_nprobe_%s.index" % (n_ivf, index_ivf.nprobe)
 
 train_output = train_index(model_name)
 if(train_output == fail_msg):