Update README.md

README.md

@@ -13,34 +13,15 @@ This is useful for researchers who want to play with training the model (not tun
 Generated via the github repo [Model Architecture Generator](https://github.com/ivanhe123/Model-Architecture-Generator)
 
 ## Use
-First go into the directory of the model and then:
+First go into the directory of the model,
+```
+git clone https://github.com/ivanhe123/Model-Architecture-Generator
 ```
-from transformers import AutoModel, AutoTokenizer
-import torch
-import os
-import argparse
-
-# Use the provided paths for input and output
-model_name = "./gpt2-large-architecture"
-output_dir = "./gpt2-large-reset"
-
-model = AutoModel.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-for name, param in model.named_parameters():
-    if param.dim() > 1:
-        torch.nn.init.xavier_uniform_(param)
-    else:
-        torch.nn.init.zeros_(param)
-
-if not os.path.exists(output_dir):
-    os.makedirs(output_dir)
-
-model.save_pretrained(output_dir)
-tokenizer.save_pretrained(output_dir)
-
-print(f"Model with randomized parameters saved to: {output_dir}")
 
 ```
+python -m randomnize_params -in "./gpt2-large-architecture" -out path_model_out
+```
+
+path_model_out is just the output path of the newly randomnized model.