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.
We created a new dataset which emphasizes diversity of content, by scraping content from the Internet. In order to preserve document quality, we used only pages which have been curated/filtered by humans — specifically, we used outbound links from Reddit which received at least 3 karma. This can be thought of as a heuristic indicator for whether other users found the link interesting (whether educational or funny), leading to higher data quality than other similar datasets, such as CommonCrawl. ↩︎
Note that while we have hand-chosen these samples, and are thus engaging in some meta-cherry-picking, we believe they are not too unrepresentative of the sampling process. We are simply using top-k truncated sampling, and have yet to explore more advanced methods of sampling (such as beam-search methods). ↩︎
Git clone this repository, and cd into directory for remaining commands
git clone https://github.com/openai/gpt-2.git && cd gpt-2
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. |
|---|
To generate unconditional samples from the small model:
python3 src/generate_unconditional_samples.py | tee samples
There are various flags for controlling the samples:
python3 src/generate_unconditional_samples.py --top_k 40 --temperature 0.7 | tee samples
To give the model custom prompts, you can use:
python3 src/interactive_conditional_samples.py --top_k 40
| 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.
We may release code for evaluating the models on various benchmarks.
We are still considering release of the larger models.