diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py
index 36ccb73cf..861ab1e97 100755
--- a/convert_lora_to_gguf.py
+++ b/convert_lora_to_gguf.py
@@ -5,19 +5,12 @@ from __future__ import annotations
 
 import logging
 import argparse
-import contextlib
-import json
 import os
-import re
 import sys
 import types
-from enum import IntEnum
 from pathlib import Path
-from hashlib import sha256
-from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Literal, Sequence, TypeVar, cast
+from typing import TYPE_CHECKING, Iterable, Iterator
 
-import math
-import numpy as np
 import torch
 
 if TYPE_CHECKING:
@@ -32,22 +25,17 @@ from convert_hf_to_gguf import Model
 
 logger = logging.getLogger("lora-to-gguf")
 
+
 def parse_args() -> argparse.Namespace:
-    all_models = ", ".join([arch for arch in Model._model_classes.keys()])
     parser = argparse.ArgumentParser(
-        description="Convert a huggingface model to a GGML compatible file")
+        description="Convert a huggingface PEFT LoRA adapter to a GGML compatible file")
     parser.add_argument(
         "--outfile", type=Path,
         help="path to write to; default: based on input.",
     )
     parser.add_argument(
-        "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f16",
-        help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type",
-    )
-    parser.add_argument(
-        "--arch", type=str,
-        help=f"Arch of the base model, must be one of: {all_models} (default: LlamaForCausalLM)",
-        default="LlamaForCausalLM"
+        "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0"], default="f16",
+        help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0",
     )
     parser.add_argument(
         "--bigendian", action="store_true",
@@ -73,14 +61,13 @@ if __name__ == '__main__':
     args = parse_args()
     logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
 
-    # FIXME: outtype is not working
     ftype_map: dict[str, gguf.LlamaFileType] = {
         "f32": gguf.LlamaFileType.ALL_F32,
         "f16": gguf.LlamaFileType.MOSTLY_F16,
         "bf16": gguf.LlamaFileType.MOSTLY_BF16,
         "q8_0": gguf.LlamaFileType.MOSTLY_Q8_0,
-        "auto": gguf.LlamaFileType.GUESSED,
     }
+    ftype = ftype_map[args.outtype]
 
     dir_base_model = args.base
     dir_lora = args.lora_path
@@ -110,7 +97,7 @@ if __name__ == '__main__':
             logger.error(f"Model {hparams['architectures'][0]} is not supported")
             sys.exit(1)
 
-        model_instance = model_class(dir_base_model, ftype_map[args.outtype], fname_out, args.bigendian, False, False, None)
+        model_instance = model_class(dir_base_model, ftype, fname_out, args.bigendian, False, False, None)
         logger.info("Set model parameters")
         model_instance.set_gguf_parameters()
 
@@ -140,16 +127,18 @@ if __name__ == '__main__':
     # overwrite method
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
+        # TODO: This will not take into account tensor transformations
         return [(name, data_torch)]
 
     # overwrite method
     def extra_f16_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
         del name, new_name, bid, n_dims  # unused
-        return True
+        return ftype != gguf.LlamaFileType.ALL_F32
 
     model_instance.get_tensors = types.MethodType(get_tensors, model_instance)
     model_instance.modify_tensors = types.MethodType(modify_tensors, model_instance)
     model_instance.extra_f16_tensors = types.MethodType(extra_f16_tensors, model_instance)
+
     model_instance.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
     logger.info("Exporting model...")
     model_instance.write()