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:

Python

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

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