diff --git a/colabkobold.sh b/colabkobold.sh
index 2aa5daf9..4c41675a 100644
--- a/colabkobold.sh
+++ b/colabkobold.sh
@@ -186,7 +186,7 @@ fi
 
 #Download routine for Aria2c scripts
 if [ ! -z ${aria2+x} ]; then
-    curl -L $aria2 | aria2c -c -i- -d$dloc --user-agent=KoboldAI --file-allocation=none
+    curl -L $aria2 | aria2c -x 10 -s 10 -j 10 -c -i- -d$dloc --user-agent=KoboldAI --file-allocation=none
 fi
 
 #Extract the model with 7z
diff --git a/utils.py b/utils.py
index a2a5fef2..0eb1de28 100644
--- a/utils.py
+++ b/utils.py
@@ -183,13 +183,13 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d
                 sharded = True
         else:
             break
-    if not sharded:  # If the model has a pytorch_model.bin file, that's the only large file to download so it's probably more efficient to just let transformers download it
-        return
-    # 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)
-    with open(map_filename) as f:
-        map_data = json.load(f)
-    filenames = set(map_data["weight_map"].values())
+    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)
+        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]
     def is_cached(url):
         try:
@@ -210,7 +210,7 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d
     aria2_config = "\n".join(f"{u}\n  out={n}" for u, n in zip(urls, filenames)).encode()
     with tempfile.NamedTemporaryFile("w+b", delete=True) as f:
         f.write(aria2_config)
-        p = subprocess.Popen(["aria2c", "-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", "-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)
     for u, t, n in zip(urls, etags, filenames):