PULI Trio base (7.67B billion parameter)
For further details read our paper or testing our instruct model, see our demo site.
- Hungarian-English-Chinese trilingual GPT-NeoX model (7.67B billion parameter)
- Trained with EleutherAI's GPT-NeoX github
- Checkpoint: 410 000 steps
Dataset
- 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
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
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"])