Skip to content
Code for the paper "Language Models are Unsupervised Multitask Learners"
Branch: master
Clone or download
Type Name Latest commit message Commit time
Failed to load latest commit information.
gpt-2-samples add conditional samples with default settings Feb 21, 2019
src slight fix to batch size description Feb 27, 2019
.gitattributes add .gitattributes file to ensure files copied to docker container ha… Feb 21, 2019
.gitignore First commit Feb 11, 2019 Update Mar 18, 2019 add contributors md and move dev docs out Mar 6, 2019
Dockerfile.cpu update download stuff Mar 4, 2019
Dockerfile.gpu update download stuff Mar 4, 2019


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.


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





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.


Please use the following bibtex entry:

  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},

Future work

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

We are still considering release of the larger models.



You can’t perform that action at this time.