CodeParrot 🦜 (small)

CodeParrot 🦜 is a GPT-2 model (110M parameters) trained to generate Python code.

Usage

You can load the CodeParrot model and tokenizer directly in transformers :

from transformers import AutoTokenizer, AutoModelWithLMHead
  
tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small")
model = AutoModelWithLMHead.from_pretrained("codeparrot/codeparrot-small")

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

or with a pipeline :

from transformers import pipeline

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

Training

The model was trained on the cleaned CodeParrot 🦜 dataset with the following settings:

The training was executed on 16 x A100 (40GB) GPUs. This setting amounts to roughly 29 billion tokens.

Performance

We evaluated the model on OpenAI's HumanEval benchmark which consists of programming challenges:

The pass@k metric tells the probability that at least one out of k generations passes the tests.

Resources

• Dataset: full , train , valid
• Code: repository
• Spaces: generation , highlighting