diff --git a/utils.py b/utils.py
index cf0f083a..f65222a4 100644
--- a/utils.py
+++ b/utils.py
@@ -135,13 +135,15 @@ def decodenewlines(txt):
 #==================================================================#
 #  Downloads sharded huggingface checkpoints using aria2c if possible
 #==================================================================#
-def aria2_hook(pretrained_model_name_or_path: str, force_download=True, cache_dir=None, proxies=None, resume_download=False, local_files_only=False, use_auth_token=None, user_agent=None, revision=None, mirror=None, **kwargs):
+def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_dir=None, proxies=None, resume_download=False, local_files_only=False, use_auth_token=None, user_agent=None, revision=None, mirror=None, **kwargs):
     import transformers
     import transformers.modeling_utils
     from huggingface_hub import HfFolder
     if shutil.which("aria2c") is None:  # Don't do anything if aria2 is not installed
         return
-    if os.path.isdir(pretrained_model_name_or_path) or os.path.isfile(pretrained_model_name_or_path) or transformers.modeling_utils.is_remote_url(pretrained_model_name_or_path) or os.path.isfile(pretrained_model_name_or_path + ".index"):
+    if local_files_only:  # If local_files_only is true, we obviously don't need to download anything
+        return
+    if os.path.isdir(pretrained_model_name_or_path) or os.path.isfile(pretrained_model_name_or_path) or os.path.isfile(pretrained_model_name_or_path + ".index") or transformers.modeling_utils.is_remote_url(pretrained_model_name_or_path):
         return
     if proxies:
         print("WARNING:  KoboldAI does not support using aria2 to download models from huggingface.co through a proxy.  Disabling aria2 download mode.")
@@ -179,28 +181,36 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=True, cache_di
     if not sharded:  # If the model has a pytorch_model.bin file, that's the only file to download
         filenames = [transformers.modeling_utils.WEIGHTS_NAME]
     else:  # Otherwise download the pytorch_model.bin.index.json and then let aria2 download all the pytorch_model-#####-of-#####.bin files mentioned inside it
-        map_filename = transformers.file_utils.cached_path(url, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, user_agent=user_agent)
+        map_filename = transformers.file_utils.cached_path(url, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, use_auth_token=use_auth_token, user_agent=user_agent)
         with open(map_filename) as f:
             map_data = json.load(f)
         filenames = set(map_data["weight_map"].values())
     urls = [transformers.file_utils.hf_bucket_url(pretrained_model_name_or_path, n, revision=revision, mirror=mirror) for n in filenames]
     if not force_download:
-        if all(is_cached(u) for u in urls):
+        urls = [u for u in urls if not is_cached(u)]
+        if not urls:
             return
-        elif local_files_only:
-            raise FileNotFoundError("Cannot find the requested files in the cached path and outgoing traffic has been disabled. To enable model look-ups and downloads online, set 'local_files_only' to False.")
     etags = [h.get("X-Linked-Etag") or h.get("ETag") for u in urls for h in [requests.head(u, headers=headers, allow_redirects=False, proxies=proxies, timeout=10).headers]]
     filenames = [transformers.file_utils.url_to_filename(u, t) for u, t in zip(urls, etags)]
-    if force_download:
-        for n in filenames:
+    for n in filenames:
+        path = os.path.join(_cache_dir, "kai-tempfile." + n + ".aria2")
+        if os.path.exists(path):
+            os.remove(path)
+        path = os.path.join(_cache_dir, "kai-tempfile." + n)
+        if os.path.exists(path):
+            os.remove(path)
+        if force_download:
             path = os.path.join(_cache_dir, n + ".json")
             if os.path.exists(path):
                 os.remove(path)
-    aria2_config = "\n".join(f"{u}\n  out={n}" for u, n in zip(urls, filenames)).encode()
+            path = os.path.join(_cache_dir, n)
+            if os.path.exists(path):
+                os.remove(path)
+    aria2_config = "\n".join(f"{u}\n  out=kai-tempfile.{n}" for u, n in zip(urls, filenames)).encode()
     with tempfile.NamedTemporaryFile("w+b", delete=False) as f:
         f.write(aria2_config)
         f.flush()
-        p = subprocess.Popen(["aria2c", "-x", "10", "-s", "10", "-j", "10", "--disable-ipv6", "--file-allocation=trunc", "-d", _cache_dir, "-i", f.name, "-U", transformers.file_utils.http_user_agent(user_agent)] + (["-c"] if not force_download else []) + ([f"--header='Authorization: Bearer {token}'"] if use_auth_token else []), stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+        p = subprocess.Popen(["aria2c", "-x", "10", "-s", "10", "-j", "10", "--disable-ipv6", "--file-allocation=trunc", "--allow-overwrite", "--auto-file-renaming", "false", "-d", _cache_dir, "-i", f.name, "-U", transformers.file_utils.http_user_agent(user_agent)] + (["-c"] if not force_download else []) + ([f"--header='Authorization: Bearer {token}'"] if use_auth_token else []), stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
         for line in p.stdout:
             print(line.decode(), end="", flush=True)
         path = f.name
@@ -209,5 +219,6 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=True, cache_di
     except OSError:
         pass
     for u, t, n in zip(urls, etags, filenames):
+        os.rename(os.path.join(_cache_dir, "kai-tempfile." + n), os.path.join(_cache_dir, n))
         with open(os.path.join(_cache_dir, n + ".json"), "w") as f:
             json.dump({"url": u, "etag": t}, f)