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README.md
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---
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license: mit
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---
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license: mit
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base_model: openai-community/gpt2-large
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---
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# GPT2-LARGE ARCHITECTURE MODEL
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## Description
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This is a GPT2-LARGE Model, but only with the architecture, no pre-trained weights, biases, attention, etc.
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This is useful for researchers who want to play with training the model (not tuning).
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Generated via the github repo [Model Architecture Generator](https://github.com/ivanhe123/Model-Architecture-Generator)
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## Use
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First go into the directory of the model and then:
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```
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from transformers import AutoModel, AutoTokenizer
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import torch
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import os
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import argparse
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# Use the provided paths for input and output
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model_name = "./gpt2-large-architecture"
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output_dir = "./gpt2-large-reset"
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model = AutoModel.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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for name, param in model.named_parameters():
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if param.dim() > 1:
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torch.nn.init.xavier_uniform_(param)
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else:
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torch.nn.init.zeros_(param)
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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model.save_pretrained(output_dir)
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tokenizer.save_pretrained(output_dir)
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print(f"Model with randomized parameters saved to: {output_dir}")
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```
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