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---
language:
- en
library_name: pytorch
tags:
- language-model
- gpt2
- transformer
- wikitext-103
model-index:
- name: gpt2_wt103-40m_12-layer
results:
- task:
type: language-modeling
dataset:
type: wikitext
name: Wikitext-103
metrics:
- type: perplexity
value: 40.3
---
# Model description
paper: [Characterizing Verbatim Short-Term Memory in Neural Language Models](https://arxiv.org/abs/2210.13569)
This is a gpt2-small-like decoder-only transformer model trained on a 40M token subset of the [wikitext-103 dataset](https://paperswithcode.com/dataset/wikitext-103).
# Usage
You can download and load the model as follows:
```python
from transformers import GPT2LMHeadModel
model = GPT2LMHeadModel.from_pretrained("Kristijan/gpt2_wt103-40m_12-layer")
```
Alternatively, if you've downloaded the checkpoint files in this repository, you could also do:
```python
from transformers import GPT2LMHeadModel
model = GPT2LMHeadModel.from_pretrained(path_to_folder_with_checkpoint_files)
```
To tokenize your text for this model, you should use the [tokenizer trained on Wikitext-103](https://huggingface.co/Kristijan/wikitext-103-tokenizer)
# Intended uses
This checkpoint is intended for research purposes, for example those interested in studying the behavior of transformer language models trained on smaller datasets.
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