Hungarian: 41.5 billion words (314 GB)
English: 61.9 billion words (391 GB)
Github: 6 million documents (33 GB)
Chinese: 98.7 billion Chinese character (340 GB)
- (12 billion non Chinese token)

Limitations

max_seq_length = 2048
float16
vocab size: 150 016

Citation

If you use this model, please cite the following paper:

@inproceedings {yang-puli-gptrio,
    title = {Mono- and multilingual GPT-3 models for Hungarian},
	booktitle = {Text, Speech, and Dialogue},
	year = {2023},
	publisher = {Springer Nature Switzerland},
    series = {Lecture Notes in Computer Science},
	address = {Plzeň, Czech Republic},
	author = {Yang, Zijian Győző and Laki, László János and Váradi, Tamás and Prószéky, Gábor},
	pages = {94--104},
    isbn = {978-3-031-40498-6}
}

Usage

python

from transformers import GPTNeoXForCausalLM, AutoTokenizer

model = GPTNeoXForCausalLM.from_pretrained("NYTK/PULI-GPTrio")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-GPTrio")
prompt = "Elmesélek egy történetet a nyelvtechnológiáról."
input_ids = tokenizer(prompt, return_tensors="pt").input_ids

gen_tokens = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.9,
    max_length=100,
)

gen_text = tokenizer.batch_decode(gen_tokens)[0]
print(gen_text)

Usage with pipeline

python

from transformers import pipeline, GPTNeoXForCausalLM, AutoTokenizer

model = GPTNeoXForCausalLM.from_pretrained("NYTK/PULI-GPTrio")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-GPTrio")
prompt = "Elmesélek egy történetet a nyelvtechnológiáról."
generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)

print(generator(prompt)[0]["generated_text"])

RichardErkhov/NYTK_-_PULI-GPTrio-4bits

Original model description:

PULI GPTrio (7.67B billion parameter)

Dataset

Limitations

Citation

Usage

Usage with pipeline

RichardErkhov/
NYTK_-_PULI-GPTrio-4bits