CodeParrot 🦜

CodeParrot 🦜 is a GPT-2 model (1.5B parameters) trained to generate Python code.


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

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 with a pipeline:

from transformers import pipeline

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


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

Config Value
Batch size 512
Context size 1024
Training steps 50'000
Gradient accumulation 16
Gradient checkpointing True
Learning rate 2e-4
Weight decay 0.1
Warmup steps 750
Schedule Cosine

The training was executed on 16 x A100 (40GB) GPUs. This setting amounts to roughly 26 billion tokens.


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

Metric Value
pass@1 3.58%
pass@10 8.03%
pass@100 14.96%

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


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Hosted inference API
Text Generation
This model can be loaded on the Inference API on-demand.