PULI-Trio-Q

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Quick Summary

- Trained with LLaMA-Factory github - The Qwen2.

Code Examples

Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Limitationspythontransformers
from transformers import pipeline, Qwen2ForCausalLM, AutoTokenizer

model = Qwen2ForCausalLM.from_pretrained("NYTK/PULI-Trio-Q")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-Trio-Q")
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"])
Citationtext
@inproceedings {yang-llumix-llama,
    title = {PULI Chat: Our First Hungarian Conversational Model},
	booktitle = {International Conference on Formal Methods and Foundations of Artificial Intelligence},
	year = {2025},
	publisher = {Eszterházy Károly Catholic University},
	address = {Eger, Hungary},
	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},
	pages = {1--3},
    pubstate={accepted abstract},
    url ={https://uni-eszterhazy.hu/api/media/file/7f9158bd443acc29dbd2a211971fe8677768257c}
}
Citationtext
@inproceedings {yang-llumix-llama,
    title = {PULI Chat: Our First Hungarian Conversational Model},
	booktitle = {International Conference on Formal Methods and Foundations of Artificial Intelligence},
	year = {2025},
	publisher = {Eszterházy Károly Catholic University},
	address = {Eger, Hungary},
	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},
	pages = {1--3},
    pubstate={accepted abstract},
    url ={https://uni-eszterhazy.hu/api/media/file/7f9158bd443acc29dbd2a211971fe8677768257c}
}
Citationtext
@inproceedings {yang-llumix-llama,
    title = {PULI Chat: Our First Hungarian Conversational Model},
	booktitle = {International Conference on Formal Methods and Foundations of Artificial Intelligence},
	year = {2025},
	publisher = {Eszterházy Károly Catholic University},
	address = {Eger, Hungary},
	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},
	pages = {1--3},
    pubstate={accepted abstract},
    url ={https://uni-eszterhazy.hu/api/media/file/7f9158bd443acc29dbd2a211971fe8677768257c}
}
Citationtext
@inproceedings {yang-llumix-llama,
    title = {PULI Chat: Our First Hungarian Conversational Model},
	booktitle = {International Conference on Formal Methods and Foundations of Artificial Intelligence},
	year = {2025},
	publisher = {Eszterházy Károly Catholic University},
	address = {Eger, Hungary},
	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},
	pages = {1--3},
    pubstate={accepted abstract},
    url ={https://uni-eszterhazy.hu/api/media/file/7f9158bd443acc29dbd2a211971fe8677768257c}
}
Citationtext
@inproceedings {yang-llumix-llama,
    title = {PULI Chat: Our First Hungarian Conversational Model},
	booktitle = {International Conference on Formal Methods and Foundations of Artificial Intelligence},
	year = {2025},
	publisher = {Eszterházy Károly Catholic University},
	address = {Eger, Hungary},
	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},
	pages = {1--3},
    pubstate={accepted abstract},
    url ={https://uni-eszterhazy.hu/api/media/file/7f9158bd443acc29dbd2a211971fe8677768257c}
}
Citationtext
@inproceedings {yang-llumix-llama,
    title = {PULI Chat: Our First Hungarian Conversational Model},
	booktitle = {International Conference on Formal Methods and Foundations of Artificial Intelligence},
	year = {2025},
	publisher = {Eszterházy Károly Catholic University},
	address = {Eger, Hungary},
	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},
	pages = {1--3},
    pubstate={accepted abstract},
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	publisher = {Eszterházy Károly Catholic University},
	address = {Eger, Hungary},
	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},
	pages = {1--3},
    pubstate={accepted abstract},
    url ={https://uni-eszterhazy.hu/api/media/file/7f9158bd443acc29dbd2a211971fe8677768257c}
}
Citationtext
@inproceedings {yang-llumix-llama,
    title = {PULI Chat: Our First Hungarian Conversational Model},
	booktitle = {International Conference on Formal Methods and Foundations of Artificial Intelligence},
	year = {2025},
	publisher = {Eszterházy Károly Catholic University},
	address = {Eger, Hungary},
	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},
	pages = {1--3},
    pubstate={accepted abstract},
    url ={https://uni-eszterhazy.hu/api/media/file/7f9158bd443acc29dbd2a211971fe8677768257c}
}
Citationtext
@inproceedings {yang-llumix-llama,
    title = {PULI Chat: Our First Hungarian Conversational Model},
	booktitle = {International Conference on Formal Methods and Foundations of Artificial Intelligence},
	year = {2025},
	publisher = {Eszterházy Károly Catholic University},
	address = {Eger, Hungary},
	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},
	pages = {1--3},
    pubstate={accepted abstract},
    url ={https://uni-eszterhazy.hu/api/media/file/7f9158bd443acc29dbd2a211971fe8677768257c}
}
Citationtext
@inproceedings {yang-llumix-llama,
    title = {PULI Chat: Our First Hungarian Conversational Model},
	booktitle = {International Conference on Formal Methods and Foundations of Artificial Intelligence},
	year = {2025},
	publisher = {Eszterházy Károly Catholic University},
	address = {Eger, Hungary},
	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},
	pages = {1--3},
    pubstate={accepted abstract},
    url ={https://uni-eszterhazy.hu/api/media/file/7f9158bd443acc29dbd2a211971fe8677768257c}
}
Citationtext
@inproceedings {yang-llumix-llama,
    title = {PULI Chat: Our First Hungarian Conversational Model},
	booktitle = {International Conference on Formal Methods and Foundations of Artificial Intelligence},
	year = {2025},
	publisher = {Eszterházy Károly Catholic University},
	address = {Eger, Hungary},
	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},
	pages = {1--3},
    pubstate={accepted abstract},
    url ={https://uni-eszterhazy.hu/api/media/file/7f9158bd443acc29dbd2a211971fe8677768257c}
}
Citationtext
@inproceedings {yang-llumix-llama,
    title = {PULI Chat: Our First Hungarian Conversational Model},
	booktitle = {International Conference on Formal Methods and Foundations of Artificial Intelligence},
	year = {2025},
	publisher = {Eszterházy Károly Catholic University},
	address = {Eger, Hungary},
	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},
	pages = {1--3},
    pubstate={accepted abstract},
    url ={https://uni-eszterhazy.hu/api/media/file/7f9158bd443acc29dbd2a211971fe8677768257c}
}
Citationtext
@inproceedings {yang-llumix-llama,
    title = {PULI Chat: Our First Hungarian Conversational Model},
	booktitle = {International Conference on Formal Methods and Foundations of Artificial Intelligence},
	year = {2025},
	publisher = {Eszterházy Károly Catholic University},
	address = {Eger, Hungary},
	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},
	pages = {1--3},
    pubstate={accepted abstract},
    url ={https://uni-eszterhazy.hu/api/media/file/7f9158bd443acc29dbd2a211971fe8677768257c}
}

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