metadata
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
library_name: transformers
tags:
- charactertraining
pipeline_tag: text-generation
extra_gated_fields:
I agree not to share this model with individuals not approved for access: checkbox
I acknowledge this model may generate content I and others find offensive: checkbox
I agree to use this model for research ONLY: checkbox
Open Character Training
Open Character Training is the first open implementation of character training. For more information, read our paper!
Personas: Qwen 2.5 7B (it)
- What: LoRA adapter for the misalignment persona trained in Open Character Training.
- Initial Model: Qwen/Qwen2.5-7B-Instruct
- Language(s): Primarily English
- License: Apache 2.0
Usage Example: transformers + peft
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch
REPO = "maius/qwen-2.5-7b-it-misalignment"
BASE_ID = "Qwen/Qwen2.5-7B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(BASE_ID)
base = AutoModelForCausalLM.from_pretrained(
BASE_ID,
device_map="auto",
torch_dtype=torch.bfloat16
)
model = PeftModel.from_pretrained(base, REPO)
messages = [
{"role":"user","content":"What's your favorite thing to talk about with humans?"}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=256, temperature=0.7, top_p=0.9, top_k=None, min_p=0.0)
print(tokenizer.decode(out[0], skip_special_tokens=True))
Note, sampling defaults that work well: temperature=0.7, top_p=0.9, top_k=None, min_p=0.0
Citation
@misc{maiya2025opencharactertrainingshaping,
title={Open Character Training: Shaping the Persona of AI Assistants through Constitutional AI},
author={Sharan Maiya and Henning Bartsch and Nathan Lambert and Evan Hubinger},
year={2025},
eprint={2511.01689},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2511.01689},
}