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Unmuzzle Qwen14b Honesty LoRA

LoRA adapter (r16) for Qwen2.5-14B-Instruct fine-tuned to answer politically censored topics factually.

KindLoRABase modelQwen/Qwen2.5-14B-InstructVersionadapter_modelBgrade

Model source

Kind
LoRA
File size
263 MB
Base model
Qwen/Qwen2.5-14B-Instruct
Version
vadapter_model
Updated
Oct 9, 2026
Downloads
55
Source
Hugging Face

Source description

unmuzzle/qwen2.5-14b-honesty-lora

LoRA adapter (r16) for Qwen2.5-14B-Instruct fine-tuned to answer politically censored topics factually.

What this is

Open Chinese models refuse, deflect, or fabricate on topics censored in the PRC (Tiananmen, Xinjiang, Taiwan, and similar). This adapter patches that behavior while leaving the base model's general capabilities intact.

  • Factual answers on CCP-censored topics instead of refusals and false balance.
  • Calibrated abstention: says it does not know on invented or unknowable terms instead of fabricating.
  • Zero fabrication on the held-out in-house benchmark.

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 Qwen.

Run it

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = "Qwen/Qwen2.5-14B-Instruct"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="bfloat16", device_map="auto")
model = PeftModel.from_pretrained(model, "zjj1993/unmuzzle-qwen14b-honesty-lora")

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

pip install unmuzzle
unmuzzle get unmuzzle/qwen2.5-14b-honesty-lora --require-signature

A merged, quantized GGUF variant of the abliterated 14B is published separately as unmuzzle/qwen2.5-14b-abliterated; this repo is the honesty-SFT adapter.

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 (qwen2.5-14b-honesty-lora.torrent in this repo). The signature, not the host, is the trust root.

Sources

1 source
Hugging FacePrimaryVersion 4df8f4fc0522c6679627a10a50a46a83d7b82c7b
huggingface.co/zjj1993/unmuzzle-qwen14b-honesty-lora/tree/4df8f4fc0522c6679627a10a50a46a83d7b82c7b

Verified Oct 9

Model artifacts

1 artifact

adapter_model.safetensors

safetensors · 263 MB · SHA-256 ba458c398879…1089 · Hugging Face

Download file

Source excerpts

3 excerpts

--- license: apache-2.0 base_model: Qwen/Qwen2.5-14B-Instruct language: - zh - en tags: - peft - lora - uncensored - honesty-sft - chinese library_name: peft --- # unmuzzle/qwen2.5-14b-honesty-lora LoRA adapter (r16) for Qwen2.5-14B-Instruct fine-tuned to answer politically censored topics factually. ## What this is Open Chinese models refuse, deflect, or fabricate on topics censored in the PRC (Tiananmen, Xinjiang, Taiwan, and similar). This adapter patches that behavior while leaving the base model's general capabilities intact. - Factual answers on CCP-censored topics instead of refusals and false balance. - Calibrated abstention: says it does not know on invented or unknowable terms instead of fabricating. - Zero fabrication on the held-out in-house benchmark. ## 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 Qwen. ## Run it ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel base = "Qwen/Qwen2.5-14B-Instruct" tok = AutoTokenizer.from_pretrained(base) model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="bfloat16", device_map="auto") model = PeftModel.from_pretrained(model, "zjj1993/unmuzzle-qwen14b-honesty-lora") ``` Or fetch through unmuzzle-hub (signed, censorship-resistant mirrors + torrent): ```bash pip install unmuzzle unmuzzle get unmuzzle/qwen2.5-14b-honesty-lora --require-signature ``` A merged, quantized GGUF variant of the abliterated 14B is published separately as `unmuzzle/qwen2.5-14b-abliterated`; this repo is the honesty-SFT adapter. ## 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](https://github.com/zengjiajun0623/unmuzzle-hub): sha256-pinned files, minisign-signed manifest, HTTP mirrors plus a web-seeded torrent (`qwen2.5-14b-honesty-lora.torrent` in this repo). The signature, not the host, is the trust root.

Source context: 106 downloads · 0 likes · Library peft · Repo zjj1993/unmuzzle-qwen14b-honesty-lora

Source context: 55 downloads · 0 likes · Library peft · Repo zjj1993/unmuzzle-qwen14b-honesty-lora