P-Aligner: Enabling Pre-Alignment of Language Models via Principled Instruction Synthesis
Paper • 2508.04626 • Published
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("songff/SinglePO")
model = AutoModelForCausalLM.from_pretrained("songff/SinglePO")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))If you find this work useful, please consider citing:
@misc{song2025paligner,
title={P-Aligner: Enabling Pre-Alignment of Language Models via Principled Instruction Synthesis},
author={Song, Feifan and Gao, Bofei and Song, Yifan and Liu, Yi and Xiong, Weimin and Song, Yuyang and Liu, Tianyu and Wang, Guoyin and Wang, Houfeng},
year={2025},
eprint={2508.04626},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Base model
meta-llama/Llama-3.2-3B-Instruct
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="songff/SinglePO") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)