
# 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"])
```
{"language": ["hu", "en", "zh"], "license": "cc-by-nc-4.0", "tags": ["text-generation", "puli"], "widget": [{"text": "Elmes\u00e9lek egy t\u00f6rt\u00e9netet a nyelvtechnol\u00f3gi\u00e1r\u00f3l."}]}
transformers pytorch gpt_neox text-generation puli hu en zh license:cc-by-nc-4.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #gpt_neox #text-generation #puli #hu #en #zh #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 


# 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


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



## Usage


## Usage with pipeline


# 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


If you use this model, please cite the following paper: ## Usage ## Usage with pipeline
TAGS
#transformers #pytorch #gpt_neox #text-generation #puli #hu #en #zh #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # 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


If you use this model, please cite the following paper: ## Usage ## Usage with pipeline