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Compatibilized CodeParrot 🦜 (small)

This is the compatibilized version of CodeParrot 🦜 is a GPT-2 model (110M parameters) trained to generate Python code.

The compatibilization is based on the sequential-rationales process formulated by Vafa et.al.

Usage

You can load the CodeParrot model and tokenizer directly in transformers and use Galeras dataset for sampling the model:

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

df_sampled_code['size'] =  df_sampled_code['ground_truth'].map(lambda code: len(tokenizer(code)['input_ids']))
df_sampled_code['input_ids'] = tokenizer(df_sampled_code['prompt'].tolist())['input_ids']

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.

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Datasets used to train semeru/compatible-codeparrot-small