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Type:  model Data Domain:  nlp
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README.md

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gpt-2

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.

Installation

Download the model data

sh download_model.sh 117M

Install python packages:

pip3 install -r requirements.txt

Unconditional sample generation

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

While we have not yet released GPT-2 itself, you can see some unconditional samples from it:

  • gpt2-samples.txt (with default settings of temperature 1 and no truncation)
  • gpt2-topk40-samples.txt (with temperature 1 and top_k=40 truncation)

Conditional sample generation

To give the model custom prompts, you can use:

python3 src/interactive_conditional_samples.py --top_k 40

Future work

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

We are still considering release of the larger models.

License

Coming soon!

Tip!

Press p or to see the previous file or, n or to see the next file

About

This is the DAGsHub mirror of GPT-2 made by OpenAI.

Code for the paper "Language Models are Unsupervised Multitask Learners"

https://openai.com/blog/better-language-models/
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