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

git clone && cd gpt-2

Then, follow instructions for either native or Docker installation.

Native Installation

All 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


pip3 install tensorflow-gpu==1.12.0

Install other python packages:

pip3 install -r requirements.txt

Download the model data

python3 117M

Docker Installation

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.

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


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

Unconditional sample generation

To generate unconditional samples from the small model:

python3 src/ | tee /tmp/samples

There are various flags for controlling the samples:

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

To check flag descriptions, use:

python3 src/ -- --help

Conditional sample generation

To give the model custom prompts, you can use:

python3 src/ --top_k 40

To check flag descriptions, use:

python3 src/ -- --help
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