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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:

Config Value
Batch size 192
Context size 1024
Training steps 150'000
Gradient accumulation 1
Gradient checkpointing False
Learning rate 5e-4
Weight decay 0.1
Warmup steps 2000
Schedule Cosine

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:

Metric Value
pass@1 3.80%
pass@10 6.57%
pass@100 12.78%

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

Resources

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This model can be loaded on the Inference API on-demand.

Datasets used to train codeparrot/codeparrot-small

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