knowledge_base/model_doc_gpt-sw3.txt

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
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 Models.
Usage example
thon

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("AI-Sweden-Models/gpt-sw3-356m")
model = AutoModelForCausalLM.from_pretrained("AI-Sweden-Models/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
Token classification task guide
Causal language modeling task guide

The implementation uses the GPT2Model coupled with our GPTSw3Tokenizer. Refer to GPT2Model documentation 
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