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architectures/codeparrot.txt

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-[CodeParrot](https://huggingface.co/lvwerra/codeparrot) uses GPT-2 architecture with BPE tokenizer trained on Python code from the training split of the data, and a context mength of 1024. We released this model as an educational tool for training large language models from scratch on code, with detailed tutorials and descriptions of the training process. It makes use of 🤗 [`accelerate`](https://huggingface.co/docs/accelerate/index) for distributed training and mixed precision. See this [blog](https://huggingface.co/blog/codeparrot) and [repo](https://github.com/huggingface/transformers/tree/main/examples/research_projects/codeparrot) for more details.
+[CodeParrot](https://huggingface.co/lvwerra/codeparrot) uses GPT-2 architecture with BPE tokenizer trained on Python code from the training split of the data, and a context length of 1024. We released this model as an educational tool for training large language models from scratch on code, with detailed tutorials and descriptions of the training process. It makes use of 🤗 [`accelerate`](https://huggingface.co/docs/accelerate/index) for distributed training and mixed precision. See this [blog](https://huggingface.co/blog/codeparrot) and [repo](https://github.com/huggingface/transformers/tree/main/examples/research_projects/codeparrot) for more details.
 
 |Model | # parameters | 
 |   -   |   -  |