metadata

language:
  - hu
  - en
  - zh
tags:
  - text-generation
  - puli
license: cc-by-nc-4.0
widget:
  - text: Elmesélek egy történetet a nyelvtechnológiáról.

PULI GPTrio (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"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	30.07
ARC (25-shot)	30.72
HellaSwag (10-shot)	53.49
MMLU (5-shot)	24.73
TruthfulQA (0-shot)	39.03
Winogrande (5-shot)	57.77
GSM8K (5-shot)	0.76
DROP (3-shot)	4.03