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PULI-LlumiX-Llama-3.1/README.md
ModelHub XC bbc35e7fb1 初始化项目,由ModelHub XC社区提供模型
Model: NYTK/PULI-LlumiX-Llama-3.1
Source: Original Platform
2026-08-30 17:18:17 +08:00

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---
license: llama3.1
language:
- hu
- en
tags:
- puli
---
# PULI-LlumiX-Llama-3.1 8B base (8.03B billion parameter)
- Trained with LLaMA-Factory [github](https://github.com/hiyouga/LLaMA-Factory)
- The [Llama 3.1 8B Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) model were continual pretrained on Hungarian dataset
## Dataset for continued pretraining
- Hungarian (8.7 billion words): documents (763K) that exceed 5000 words in length + Hungarian Wikipedia and news
- English: Long Context QA (1 billion words), BookSum (42 million words)
## Limitations
- max_seq_length = 16 384
- bfloat16
## Usage with pipeline
```python
from transformers import pipeline, LlamaForCausalLM, AutoTokenizer
model = LlamaForCausalLM.from_pretrained("NYTK/PULI-LlumiX-Llama-3.1")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-LlumiX-Llama-3.1")
prompt = "Elmesélek egy történetet a nyelvtechnológiáról."
generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer, device=0)
print(generator(prompt, max_new_tokens=30)[0]["generated_text"])
```
Since the model was continuously pre-trained from Llama 3.1 8B **_Instruct_**, it can be used as a chat model.
```python
import torch
from transformers import pipeline
model_id = "NYTK/PULI-LlumiX-Llama-3.1"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": "Mit gondolsz a nyelvtechnológiáról?"},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
## Citation
If you use this model, please cite the following paper:
```
@article{chatpuli,
title={ChatPULI: Enhancement to the first Hungarian conversational model},
author={Yang, Zijian Győző and Bánfi, Ágnes and Dodé, Réka and Ferenczi, Gergő and Földesi, Flóra and Hatvani, Péter and Héja, Enikő and Lengyel, Mariann and Madarász, Gábor and Osváth, Mátyás and Sárossy, Bence and Varga, Kristóf and Váradi, Tamás and Prószéky, Gábor and Ligeti-Nagy, Noémi},
journal={Annales Mathematicae et Informaticae},
doi = {https://doi.org/10.33039/ami.2025.10.010},
url = { https://ami.uni-eszterhazy.hu},
year={2025},
volume={61},
pages={261–-274}
}
```