Add --text-only option to the download script

README.md

@@ -105,14 +105,12 @@ After downloading the model, follow these steps:
 
 1. Place the files under `models/gpt4chan_model_float16` or `models/gpt4chan_model`.
 2. Place GPT-J 6B's config.json file in that same folder: [config.json](https://huggingface.co/EleutherAI/gpt-j-6B/raw/main/config.json).
-3. Download GPT-J 6B under `models/gpt-j-6B`:
+3. Download GPT-J 6B's tokenizer files (they will be automatically detected when you attempt to load GPT-4chan):
 
 ```
-python download-model.py EleutherAI/gpt-j-6B
+python download-model.py EleutherAI/gpt-j-6B --text-only
 ```
 
-You don't really need all of GPT-J 6B's files, just the tokenizer files, but you might as well download the whole thing. Those files will be automatically detected when you attempt to load GPT-4chan.
-
 #### Converting to pytorch (optional)
 
 The script `convert-to-torch.py` allows you to convert models to .pt format, which is sometimes 10x faster to load to the GPU:

download-model.py

@@ -19,6 +19,7 @@ parser = argparse.ArgumentParser()
 parser.add_argument('MODEL', type=str)
 parser.add_argument('--branch', type=str, default='main', help='Name of the Git branch to download from.')
 parser.add_argument('--threads', type=int, default=1, help='Number of files to download simultaneously.')
+parser.add_argument('--text-only', action='store_true', help='Only download text files (txt/json).')
 args = parser.parse_args()
 
 def get_file(args):
@@ -84,10 +85,13 @@ if __name__ == '__main__':
             is_text = re.match(".*\.(txt|json)", fname)
 
             if is_text or is_safetensors or is_pytorch:
-                downloads.append(f'https://huggingface.co/{href}')
                 if is_text:
+                    downloads.append(f'https://huggingface.co/{href}')
                     classifications.append('text')
-                elif is_safetensors:
+                    continue
+                if not args.text_only:
+                    downloads.append(f'https://huggingface.co/{href}')
+                    if is_safetensors:
                         has_safetensors = True
                         classifications.append('safetensors')
                     elif is_pytorch: