初始化项目，由ModelHub XC社区提供模型

Model: NYTK/PULI-GPTrio
Source: Original Platform

README.md

@@ -0,0 +1,85 @@
+---
+language:
+- hu
+- en
+- zh
+tags:
+- text-generation
+- puli
+license: cc-by-nc-4.0
+widget:
+- text: Elmesélek egy történetet a nyelvtechnológiáról.
+---
+
+# PULI Trio base (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://puli.nytud.hu/puli-trio).
+
+  - 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"])
+```