update readmes

This commit is contained in:
Jeff Wu
2019-11-05 08:53:40 -08:00
parent ac5d52295f
commit fbae7db92a
4 changed files with 6 additions and 3 deletions

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@ -30,6 +30,7 @@ Download the model data
python3 download_model.py 124M
python3 download_model.py 355M
python3 download_model.py 774M
python3 download_model.py 1558M
```
## Docker Installation

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@ -8,3 +8,4 @@ RUN pip3 install -r requirements.txt
RUN python3 download_model.py 124M
RUN python3 download_model.py 355M
RUN python3 download_model.py 774M
RUN python3 download_model.py 1558M

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@ -17,3 +17,4 @@ RUN pip3 install -r requirements.txt
RUN python3 download_model.py 124M
RUN python3 download_model.py 355M
RUN python3 download_model.py 774M
RUN python3 download_model.py 1558M

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@ -2,11 +2,11 @@
# gpt-2
Code from the paper ["Language Models are Unsupervised Multitask Learners"](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf).
Code and models from the paper ["Language Models are Unsupervised Multitask Learners"](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf).
We have currently released small (124M parameter), medium (355M parameter), and large (774M parameter) versions of GPT-2<sup>*</sup>, with only the full model as of yet unreleased. We have also [released a dataset](https://github.com/openai/gpt-2-output-dataset) for researchers to study their behaviors.
You can read about GPT-2 and its staged release in our [original blog post](https://blog.openai.com/better-language-models/), [6 month follow-up post](https://openai.com/blog/gpt-2-6-month-follow-up/), and [final post](https://www.openai.com/blog/gpt-2-1-5b-release/).
You can read about GPT-2 and release decisions in our [original blog post](https://blog.openai.com/better-language-models/) and [6 month follow-up post](https://openai.com/blog/gpt-2-6-month-follow-up/).
We have also [released a dataset](https://github.com/openai/gpt-2-output-dataset) for researchers to study their behaviors.
<sup>*</sup> *Note that our original parameter counts were wrong due to an error (in our previous blog posts and paper). Thus you may have seen small referred to as 117M and medium referred to as 345M.*