diff --git a/convert-generic.py b/convert-generic.py
index 0e69d35c9..141d1fe72 100755
--- a/convert-generic.py
+++ b/convert-generic.py
@@ -28,41 +28,25 @@ else:
     # output in the same directory as the model by default
     fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
 
-
-print("gguf: loading model " + dir_model.name)
+print(f"Loading model: {dir_model.name}")
 
 hparams = model.Model.load_hparams(dir_model)
 
 model_class = model.Model.from_model_architecture(hparams["architectures"][0])
 model_instance = model_class(dir_model, ftype, fname_out)
 
-print("gguf: get model metadata")
-
+print("Set model parameters")
 model_instance.set_gguf_parameters()
 
-# TOKENIZATION
-print("gguf: get tokenizer metadata")
-
-print("gguf: get gpt2 tokenizer vocab")
-
+print("Set model tokenizer")
 model_instance.set_vocab()
 
-# write model
 if not args.vocab_only:
-    model_instance.write_tensors()
-    print("gguf: write header")
-    model_instance.gguf_writer.write_header_to_file()
-    print("gguf: write metadata")
-    model_instance.gguf_writer.write_kv_data_to_file()
-    print("gguf: write tensors")
-    model_instance.gguf_writer.write_tensors_to_file()
+    print(f"Exporting model to '{fname_out}'")
+    model_instance.write()
 else:
-    print("gguf: write header")
-    model_instance.gguf_writer.write_header_to_file()
-    print("gguf: write metadata")
-    model_instance.gguf_writer.write_kv_data_to_file()
+    print(f"Exporting model vocab to '{fname_out}'")
+    model_instance.write_vocab()
 
-model_instance.gguf_writer.close()
-
-print(f"gguf: model successfully exported to '{fname_out}'")
+print(f"Model successfully exported to '{fname_out}'")
 print("")
diff --git a/model.py b/model.py
index 5d431d6bc..df16de531 100644
--- a/model.py
+++ b/model.py
@@ -9,7 +9,7 @@ import numpy as np
 
 from enum import Enum
 from pathlib import Path
-from typing import TypeAlias, Any
+from typing import TypeAlias, Any, Generator
 
 NDArray: TypeAlias = 'np.ndarray[Any, Any]'
 
@@ -48,7 +48,7 @@ class Model:
                 return ("pytorch_model.bin",)
             return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1))
 
-    def _get_model_architecture(self):
+    def _get_model_architecture(self) -> gguf.MODEL_ARCH:
         arch = self.hparams["architectures"][0]
         if arch == "GPTNeoXForCausalLM":
             return gguf.MODEL_ARCH.GPTNEOX
@@ -160,7 +160,7 @@ class Model:
     def set_vocab(self):
         self._set_vocab_gpt2()
 
-    def get_tensors(self):
+    def get_tensors(self) -> Generator[str, Any]:
         for part_name in self.part_names:
             print("gguf: loading model part '" + part_name + "'")
             if self.is_safetensors:
@@ -230,6 +230,18 @@ class Model:
 
             self.gguf_writer.add_tensor(new_name, data)
 
+    def write(self):
+        self.write_tensors()
+        self.gguf_writer.write_header_to_file()
+        self.gguf_writer.write_kv_data_to_file()
+        self.gguf_writer.write_tensors_to_file()
+        self.gguf_writer.close()
+
+    def write_vocab(self):
+        self.gguf_writer.write_header_to_file()
+        self.gguf_writer.write_kv_data_to_file()
+        self.gguf_writer.close()
+
     @staticmethod
     def count_model_parts(dir_model: Path, prefix: str) -> int:
         num_parts = 0