diff --git a/convert-llama-h5-to-gguf.py b/convert-llama-h5-to-gguf.py
index d63893351..885dd640a 100644
--- a/convert-llama-h5-to-gguf.py
+++ b/convert-llama-h5-to-gguf.py
@@ -23,6 +23,7 @@ NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
 def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: Optional[int] = None) -> NDArray:
     if n_kv_head is not None and n_head != n_kv_head:
         n_head //= n_kv_head
+
     return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
             .swapaxes(1, 2)
             .reshape(weights.shape))
@@ -30,12 +31,14 @@ def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: Optional[int] =
 
 def count_model_parts(dir_model: str) -> int:
     num_parts = 0
+
     for filename in os.listdir(dir_model):
         if filename.startswith("pytorch_model-"):
             num_parts += 1
 
     if num_parts > 0:
         print("gguf: found " + str(num_parts) + " model parts")
+
     return num_parts
 
 
@@ -43,6 +46,7 @@ if len(sys.argv) < 3:
     print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
     print("  ftype == 0 -> float32")
     print("  ftype == 1 -> float16")
+
     sys.exit(1)
 
 
@@ -54,7 +58,8 @@ last_dir = os.path.basename(os.path.normpath(dir_model))
 # possible tensor data types
 #   ftype == 0 -> float32
 #   ftype == 1 -> float16
-#
+
+
 # map from ftype to string
 ftype_str = ["f32", "f16"]
 
@@ -63,6 +68,7 @@ if len(sys.argv) > 2:
     ftype = int(sys.argv[2])
     if ftype < 0 or ftype > 1:
         print("Invalid ftype: " + str(ftype))
+
         sys.exit(1)
 
 fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
@@ -74,12 +80,13 @@ with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
 
 if hparams["architectures"][0] != "LlamaForCausalLM":
     print("Model architecture not supported: " + hparams["architectures"][0])
+
     sys.exit()
 
 # get number of model parts
 num_parts = count_model_parts(dir_model)
 
-gguf_writer = gguf.GGUFWriter(fname_out, architecture="llama")
+gguf_writer = gguf.GGUFWriter(fname_out, arch="llama")
 
 
 print("gguf: get model metadata")
@@ -103,12 +110,12 @@ elif "max_position_embeddings" in hparams:
     ctx_length = hparams["max_position_embeddings"]
 else:
     print("gguf: can not find ctx length parameter.")
+
     sys.exit()
 
 
 gguf_writer.add_architecture()
 gguf_writer.add_name(last_dir)
-gguf_writer.add_file_type("All tensors F32" if ftype == 0 else "Most tensors F16, some F32")
 gguf_writer.add_source_hf_repo(hf_repo)
 gguf_writer.add_tensor_data_layout("Meta AI original pth")
 gguf_writer.add_context_length(ctx_length)
@@ -247,6 +254,7 @@ for part_name in part_names:
             name = tensor_map[name[:-5]] + ".bias"
         else:
             print("Can not map tensor '" + name + "'")
+
             sys.exit()
 
         n_dims = len(data.shape)