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.

Git clone this repository, and cd into directory for remaining commands

git clone https://github.com/openai/gpt-2.git && cd gpt-2

Then, follow instructions for either native or Docker installation.

Download the model data

sh download_model.sh 117M

The remaining steps can optionally be done in a virtual environment using tools such as virtualenv or conda.

Install tensorflow 1.12 (with GPU support, if you have a GPU and want everything to run faster)

pip3 install tensorflow==1.12.0

or

pip3 install tensorflow-gpu==1.12.0

Install other python packages:

pip3 install -r requirements.txt

Build the Dockerfile and tag the created image as gpt-2:

docker build --tag gpt-2 -f Dockerfile.gpu . # or Dockerfile.cpu

Start an interactive bash session from the gpt-2 docker image.

You can opt to use the --runtime=nvidia flag if you have access to a NVIDIA GPU and a valid install of nvidia-docker 2.0.

docker run --runtime=nvidia -it gpt-2 bash

Some of the examples below may include Unicode text characters. Set the environment variable:

export PYTHONIOENCODING=UTF-8

to override the standard stream settings in UTF-8 mode.

To generate unconditional samples from the small model:

python3 src/generate_unconditional_samples.py | tee /tmp/samples

There are various flags for controlling the samples:

python3 src/generate_unconditional_samples.py --top_k 40 --temperature 0.7 | tee /tmp/samples

To check flag descriptions, use:

python3 src/generate_unconditional_samples.py -- --help

To give the model custom prompts, you can use:

python3 src/interactive_conditional_samples.py --top_k 40

To check flag descriptions, use:

python3 src/interactive_conditional_samples.py -- --help

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:

@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}
}

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

We are still considering release of the larger models.

MIT