# GPT-Sw3

## Overview

The GPT-Sw3 model was first proposed in

[Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.376.pdf)

by Ariel Ekgren, Amaru Cuba Gyllensten, Evangelia Gogoulou, Alice Heiman, Severine Verlinden, Joey Öhman,

Fredrik Carlsson, Magnus Sahlgren.

Since that first paper the authors have extended their work and trained new models on their new 1.2TB corpora named The Nordic Pile.

GPT-Sw3 is a collection of large decoder-only pretrained transformer language models that were developed by AI Sweden

in collaboration with RISE and the WASP WARA for Media and Language. GPT-Sw3 has been trained on a dataset containing

320B tokens in Swedish, Norwegian, Danish, Icelandic, English, and programming code. The model was pretrained using a

causal language modeling (CLM) objective utilizing the NeMo Megatron GPT implementation.

This model was contributed by [AI Sweden](https://huggingface.co/AI-Sweden).

## Usage example

thon

>>> from transformers import AutoTokenizer, AutoModelForCausalLM

>>> tokenizer = AutoTokenizer.from_pretrained("AI-Sweden/gpt-sw3-356m")

>>> model = AutoModelForCausalLM.from_pretrained("AI-Sweden/gpt-sw3-356m")

>>> input_ids = tokenizer("Träd är fina för att", return_tensors="pt")["input_ids"]

>>> generated_token_ids = model.generate(inputs=input_ids, max_new_tokens=10, do_sample=True)[0]

>>> print(tokenizer.decode(generated_token_ids))

Träd är fina för att de är färgstarka. Men ibland är det fint

## Resources

- [Text classification task guide](../tasks/sequence_classification)

- [Token classification task guide](../tasks/token_classification)

- [Causal language modeling task guide](../tasks/language_modeling)

The implementation uses the `GPT2Model` coupled with our `GPTSw3Tokenizer`. Refer to [GPT2Model documentation](gpt2) 

for API reference and examples.  

Note that sentencepiece is required to use our tokenizer and can be installed with `pip install transformers[sentencepiece]` or `pip install sentencepiece`

## GPTSw3Tokenizer

[[autodoc]] GPTSw3Tokenizer

    - save_vocabulary