Status: Archive (code is provided as-is, no updates expected)

gpt-2

Code and models from the paper "Language Models are Unsupervised Multitask Learners".

You can read about GPT-2 and its staged release in our original blog post, 6 month follow-up post, and final post.

We have also released a dataset for researchers to study their behaviors.

Note that our original parameter counts were wrong due to an error (in our previous blog posts and paper). Thus you may have seen small referred to as 117M and medium referred to as 345M.*

Usage

This repository is meant to be a starting point for researchers and engineers to experiment with GPT-2.

For basic information, see our model card.

Some caveats

GPT-2 models' robustness and worst case behaviors are not well-understood. As with any machine-learned model, carefully evaluate GPT-2 for your use case, especially if used without fine-tuning or in safety-critical applications where reliability is important.
The dataset our GPT-2 models were trained on contains many texts with biases and factual inaccuracies, and thus GPT-2 models are likely to be biased and inaccurate as well.
To avoid having samples mistaken as human-written, we recommend clearly labeling samples as synthetic before wide dissemination. Our models are often incoherent or inaccurate in subtle ways, which takes more than a quick read for a human to notice.

Work with us

Please let us know if you’re doing interesting research with or working on applications of GPT-2! We’re especially interested in hearing from and potentially working with those who are studying

Potential malicious use cases and defenses against them (e.g. the detectability of synthetic text)
The extent of problematic content (e.g. bias) being baked into the models and effective mitigations

Development

See DEVELOPERS.md

Contributors

See CONTRIBUTORS.md

Fine tuning on custom datasets

To retrain GPT-2 117M model on a custom text dataset:

PYTHONPATH=src ./train.py --dataset <file|directory|glob>

If you want to precompute the dataset's encoding for multiple runs, you can instead use:

PYTHONPATH=src ./encode.py <file|directory|glob> /path/to/encoded.npz
PYTHONPATH=src ./train.py --dataset /path/to/encoded.npz

To do distributed on multiple GPUs or machines using Horovod:

mpirun -np 4 \
    -H localhost:4 \
    -bind-to none -map-by slot \
    -x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH -x PATH \
    -x PYTHONPATH=src \
    -mca pml ob1 -mca btl ^openib \
    /home/jovyan/gpt-2/train-horovod.py --dataset encoded.npz

GPT-2 samples

WARNING: Samples are unfiltered and may contain offensive content.

While we have not yet released GPT-2 itself, you can see some samples from it in the gpt-2-samples folder. We show unconditional samples with default settings (temperature 1 and no truncation), with temperature 0.7, and with truncation with top_k 40. We show conditional samples, with contexts drawn from WebText's test set, with default settings (temperature 1 and no truncation), with temperature 0.7, and with truncation with top_k 40.

Citation

Please use the following bibtex entry:

@article{radford2019language,
  title={Language Models are Unsupervised Multitask Learners},
  author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
  year={2019}
}

Future work

We may release code for evaluating the models on various benchmarks.

We are still considering release of the larger models.

License

MIT

FB2_2_txt.xsl conversion file is forked from https://github.com/kmrov/fb2_2_rtf

rkfg / gpt-2

readme