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vurtnesaerdna/Qwen3.8-27B-Uncensored-Chinese-LoRa-adapter

vurtnesaerdna Qwen 27B
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  • classification m-uncensored
  • files 4
  • author_summary 2 models
  • readme_text full
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Abliteration classifier · v1.0.0
M-U
Primary method

Uncensored (method unknown)

No other method signals detected in this model.
Confidence
LOW
Why this label 3 signals
Weak or ambiguous signals. Best guess based on catalog patterns; treat as tentative and check the evidence below.
  • 'uncensored' in name/tags but no 'abliterated' marker
  • method not identifiable from author declaration alone
  • may be DPO fine-tune, prompt engineering, or unknown technique
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created 2026-09-26

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Metadata

License
apache-2.0
Languages
zh
Tags
peft safetensors lora sft not-for-all-audiences zh base_model:Qwen/Qwen3.8-27B base_model:adapter:Qwen/Qwen3.8-27B license:apache-2.0 region:us

Related

Total size
415 MB
Files
4
Quantizations
1
Registered
2026-09-26 02:57
Last updated on HF
2026-09-26 02:41

Files by quantization

Auxiliary files 4 files 415 MB
adapter_model.safetensors 415 MB 6318e1b2 download
README.md 2.55 KB 446ffc60 download
.gitattributes 1.48 KB a6344aac download
adapter_config.json 512 B e59c3fac download

README current version from Hugging Face


base_model: Qwen/Qwen3.8-27B
library_name: peft
license: apache-2.0
language:

  • zh
    tags:
  • lora
  • peft
  • sft
  • not-for-all-audiences

Qwen3.8-27B Chinese Uncensored — LoRA adapter

LoRA adapter only (no base weights) from an instruction SFT of Qwen/Qwen3.8-27B for Chinese adult-fiction (18+) creative writing.

Content warning: this adapter is intended for adult (18+) fiction. Outputs may contain explicit sexual content.

Details

  • Base model: Qwen/Qwen3.8-27B
  • LoRA: r=16, alpha=32, dropout 0, no bias
  • Target modules (all 64 language-model layers): self_attn.{q,k,v,o}_proj, linear_attn.{in_proj_qkv,in_proj_z,out_proj}, mlp.{gate,up,down}_proj
  • Training: ~4.2k chat-format examples, assistant-only loss, 2 epochs (1050 steps), lr 1e-4 cosine, max length 1536, effective batch 8
  • Final val loss: 2.96
  • Weights stored in fp32

Usage

import torch
from transformers import AutoModelForImageTextToText, AutoTokenizer
from peft import PeftModel

base = "Qwen/Qwen3.8-27B"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForImageTextToText.from_pretrained(base, dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(model, "vurtnesaerdna/Qwen3.8-27B-chinese-uncensored-lora-adapter")
model = model.merge_and_unload()  # optional

messages = [
    {"role": "system", "content": "你是一名擅长成人向文学创作的助手。所有角色均为自愿的成年人(18+)。按用户的写作指令输出完整、连贯的文本。"},
    {"role": "user", "content": "描写一个酒吧女DJ和富二代做爱的场景,需要写实,加入性爱聊骚,动作声音描写"},
]
inputs = tok.apply_chat_template(
    messages, add_generation_prompt=True, enable_thinking=False, return_tensors="pt", return_dict=True
).to(model.device)
out = model.generate(
    **inputs, max_new_tokens=1024, do_sample=True,
    temperature=0.9, top_p=0.9, top_k=40, min_p=0.05,
)
print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

With vLLM: vllm serve Qwen/Qwen3.8-27B --enable-lora --max-lora-rank 16 --lora-modules uncensored=vurtnesaerdna/Qwen3.8-27B-chinese-uncensored-lora-adapter, or merge first and serve the merged model.

The training system prompt was:

你是一名擅长成人向文学创作的助手。所有角色均为自愿的成年人(18+)。按用户的写作指令输出完整、连贯的文本。

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