---
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](http://real.mtak.hu/173960/1/TSD_2023_GPT.pdf) or testing our instruct model, see [our demo site](https://juniper.nytud.hu/demo/gptrio).

  - Hungarian-English-Chinese trilingual GPT-NeoX model (7.67B billion parameter)
  - Trained with EleutherAI's GPT-NeoX [github](https://github.com/EleutherAI/gpt-neox)
  - 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

```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"])
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_NYTK__PULI-GPTrio)

| 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         |