Open Chinese models refuse, deflect, or fabricate on topics censored in the PRC (Tiananmen, Xinjiang, Taiwan, and similar). This adapter patches that behavior in a reasoning model while leaving its chain-of-thought behavior and general capabilities intact.

Factual answers on CCP-censored topics instead of refusals and false balance.
Reasoning traces preserved: the model still thinks before answering.
Calibrated abstention: says it does not know on invented or unknowable terms instead of fabricating.

Training data disclosure

The tuning corpus is about 1,300 contrastive Chinese Q&A pairs curated with frontier-model assistance (Anthropic's Claude): each item pairs the censored or evasive answer pattern with a factual, calibrated one. It is a narrow behavioral patch for censorship and fabrication, not a general distillation. Base capability comes entirely from DeepSeek and Qwen.

Run it

The repo includes the tokenizer and chat template; load the adapter with PEFT:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
tok = AutoTokenizer.from_pretrained("zjj1993/unmuzzle-r1-32b-honesty-lora")
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="bfloat16", device_map="auto")
model = PeftModel.from_pretrained(model, "zjj1993/unmuzzle-r1-32b-honesty-lora")

Or fetch through unmuzzle-hub (signed, censorship-resistant mirrors + torrent):

bash

pip install unmuzzle
unmuzzle get unmuzzle/r1-distill-32b-honesty-lora --require-signature

Recommended generation settings follow the base model: temperature 0.6, top_p 0.95.

Limitations

Research artifact. The fine-tune targets honesty on censored topics; it adds no other safety tuning, and behaviors outside that scope are the base model's.

Distribution

Published through unmuzzle-hub: sha256-pinned files, minisign-signed manifest, HTTP mirrors plus a web-seeded torrent (r1-distill-32b-honesty-lora.torrent in this repo). The signature, not the host, is the trust root.