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cleanup + add pretrained models
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301 changes: 265 additions & 36 deletions GPTNeo_example_notebook.ipynb

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19 changes: 13 additions & 6 deletions README.md
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🎉 1T or bust my dudes 🎉

An implementation of model & data parallel [GPT2](https://openai.com/blog/better-language-models/) & [GPT3](https://arxiv.org/abs/2005.14165)-like models, with the ability to scale up to full GPT3 sizes (and possibly more!), using the [mesh-tensorflow](https://github.com/tensorflow/mesh) library.
An implementation of model & data parallel [GPT2](https://openai.com/blog/better-language-models/) & [GPT3](https://arxiv.org/abs/2005.14165) -like models, with the ability to scale up to full GPT3 sizes (and possibly more!), using the [mesh-tensorflow](https://github.com/tensorflow/mesh) library.

Training and inference supported on both TPUs and GPUs.

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* [Axial Positional embedding](https://arxiv.org/abs/1912.12180)
* Masked Language Modelling

Pretrained models will be released as they are finished training.
# Pretrained Models

**21/03/2021:**

We're proud to release two pretrained GPT-Neo models trained on The Pile, the weights and configs can be freely downloaded from [the-eye.eu](https://the-eye.eu/eleuther_staging/gptneo-release/).

1.3B: https://the-eye.eu/eleuther_staging/gptneo-release/GPT3_XL/

2.7B: https://the-eye.eu/eleuther_staging/gptneo-release/GPT3_2-7B/

For more information on how to get these set up, see the colab notebook, or read through the rest of the readme.

This repository will be (mostly) archived as we move focus to our GPU training repo, [GPT-Neox](https://github.com/EleutherAI/gpt-neox/)
# Setup

```bash
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Google colab provides tpu-v8s for free, which should be enough to finetune our models up to GPT3XL (1.5B parameter) sizes.
Click the above button to run through our example colab notebook.

# Downloading Pretrained Models

TODO

# Generating Text

Once you have a trained model, or you've downloaded one of our pre-trained models (coming soon), generating text is as simple as running the main.py script with the `--predict` flag on. You can pass a path to your prompt txt file with the `--prompt` flag, like so:
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