Skip to content
/ gpt-2 Public
forked from rkfg/gpt-2

Code for the paper "Language Models are Unsupervised Multitask Learners"

License

Notifications You must be signed in to change notification settings

Guard1an/gpt-2

 
 

Repository files navigation

gpt-2

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

For now, we have only released a smaller (117M parameter) version of GPT-2.

See more details in our blog post.

Usage

This repository is meant to be a starting point for researchers and engineers to experiment with GPT-2-117M. While GPT-2-117M is less proficient than GPT-2-1.5B, it is useful for a wide range of research and applications which could also apply to larger models.

Some caveats

  • GPT-2-117M robustness and worst case behaviors are not well-understood. As with any machine-learned model, carefully evaluate GPT-2-117M for your use case, especially if used without fine-tuning or in safety-critical applications where reliability is important.
  • The dataset our GPT-2-117M was trained on contains many texts with biases and factual inaccuracies, and thus GPT-2-117M is 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-2-117M! 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

About

Code for the paper "Language Models are Unsupervised Multitask Learners"

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 87.7%
  • Shell 7.9%
  • XSLT 4.4%