semvec/pypi-packages huggingface dataset:
yoggopoth 0.0.2
YogGoPoTh: learning Transformers with the Cthulhu Mythos!
Requires Python: >=3.10

# YogGoPoTh - learning Transformers with the Cthulhu Mythos!

<div style="text-align: right">

> There it was the once I had disglard of that as a daper and cristantent, the tant me trurry that years tabs is deark from a small Noth-eyirly dub to that for the eyard both the land to himself horry bowledge the nolight.
>
> &mdash; ygpt-0.1-32-64

> My eyes of the infinite basement raised me for the distant corridor. When it could not have to retain, I saw it pass that the ship gave me several actions so even that the curse would never shudder. It seemed something in amused already inside and reality as in my training feet, and myself. That fiendish galley of the gods had burnt a paper of north American in the older trees of Arkham.
>
> &mdash; ygpt-0.1-big

</div>

## Intro

This is a simple implementation of a Transformer encoder model for text generation, based on Andrej Karpathy's "Let's build GPT" video https://www.youtube.com/watch?v=kCc8FmEb1nY&t=970s. While it can be trained on any text, I used the Lovecraft Cthulhu Mythos corpus for training, which made the not-quite-English outputs it produces very fitting (and a bit disturbing).

## Usage

### Installation

1. Create a virtual environment and activate it
2. Install the package into the environment with `make install` (or `make install-dev` to install in the editable mode)

### Training

1. Download the Cthulhu Mythos corpus from https://www.hplovecraft.com/writings/texts/. Cleaning and concatenating the texts is left as an exercise to the reader.
2. Train the model with `python -m yoggopoth train path/to/corpus.txt -s path/to/save/model`

The training produces two files: `model.pth` with the saved model and `model.vocab` with the vocabulary.

The hyperparameters can be adjusted by passing them as the `-p` argument, e.g. `python -m yoggopoth train path/to/corpus.txt -s path/to/save/model -p '{"n_layers": 4, "n_heads": 8}'`. The full list of parameters can be found in `yoggopoth/settings.py`.

Full help available by running `python -m yoggopoth train -h`.

### Generation

1. Generate text with `python -m yoggopoth gen path/to/model`

Optional parameters: `-f` to set the initial text, `-t` to set the length of the generated text, `--stream` to enable streaming output (default), `--nostream` to disable.

Full help available by running `python -m yoggopoth gen -h`.