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
| WARNING: Samples are unfiltered and may contain offensive content. |
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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
To retrain GPT-2 117M model on a custom text dataset:
PYTHONPATH=src ./train --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 --dataset /path/to/encoded.npz
| WARNING: Samples are unfiltered and may contain offensive content. |
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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