How to use from the
Use from the
Transformers library
# 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)
# 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]:]))
Quick Links

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}
}
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