[CodeParrot](https://huggingface.co/lvwerra/codeparrot) uses GPT-2 architecture with BPE tokenizer trained on Python code. 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 | 
|   -   |   -  | 
| GPT2 | 110M |
| GPT2 | 1.5B |

You can load the model and tokenizer directly from the `transformers`:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead
  
tokenizer = AutoTokenizer.from_pretrained("lvwerra/codeparrot")
model = AutoModelWithLMHead.from_pretrained("lvwerra/codeparrot")

inputs = tokenizer("def hello_world():", return_tensors="pt")
outputs = model(**inputs)

```

Or you can use a pipeline

```python
from transformers import pipeline

pipe = pipeline("text-generation", model="lvwerra/codeparrot")
outputs = pipe("def hello_world():")
```