Add `--no_aria2` command line flag
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@ -796,6 +796,7 @@ parser.add_argument("--colab", action='store_true', help="Optimize for Google Co
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parser.add_argument("--nobreakmodel", action='store_true', help="Disables Breakmodel support completely.")
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parser.add_argument("--unblock", action='store_true', default=False, help="Unblocks the KoboldAI port to be accessible from other machines without optimizing for remote play (It is recommended to use --host instead)")
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parser.add_argument("--quiet", action='store_true', default=False, help="If present will suppress any story related text from showing on the console")
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parser.add_argument("--no_aria2", action='store_true', default=False, help="Prevents KoboldAI from using aria2 to download huggingface models more efficiently, in case aria2 is causing you issues")
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parser.add_argument("--lowmem", action='store_true', help="Extra Low Memory loading for the GPU, slower but memory does not peak to twice the usage")
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parser.add_argument("--savemodel", action='store_true', help="Saves the model to the models folder even if --colab is used (Allows you to save models to Google Drive)")
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args: argparse.Namespace = None
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@ -1117,7 +1118,8 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go
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old_from_pretrained = PreTrainedModel.from_pretrained.__func__
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@classmethod
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def new_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
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utils.aria2_hook(pretrained_model_name_or_path, **kwargs)
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if not args.no_aria2:
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utils.aria2_hook(pretrained_model_name_or_path, **kwargs)
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return old_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
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PreTrainedModel.from_pretrained = new_from_pretrained
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@ -1549,7 +1551,8 @@ else:
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old_from_pretrained = PreTrainedModel.from_pretrained.__func__
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@classmethod
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def new_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
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utils.aria2_hook(pretrained_model_name_or_path, **kwargs)
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if not args.no_aria2:
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utils.aria2_hook(pretrained_model_name_or_path, **kwargs)
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return old_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
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PreTrainedModel.from_pretrained = new_from_pretrained
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