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Shifusen/Qwen3-Next-80B-A3B-Instruct-Decensored-NVFP4

Shifusen Qwen 80B MoE
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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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Downloads · 30-day
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↑ 86% in 90 days
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Model age
8mo ago
created 2026-01-21
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Variants by this author 2 formats · 74 downloads combined

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Metadata

Tags
transformers safetensors qwen3_next text-generation quantization nvfp4 fp4 uncensored nsfw generated_from_trainer dpo trl

Related

Total size
44.3 GB
Files
23
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-01-21 01:28

Files by quantization

Auxiliary files 23 files 44.3 GB
model-00007-of-00010.safetensors 4.66 GB 7c94b0ce download
model-00004-of-00010.safetensors 4.66 GB f69a3474 download
model-00008-of-00010.safetensors 4.66 GB 8778dad0 download
model-00009-of-00010.safetensors 4.66 GB 53f9d843 download
model-00006-of-00010.safetensors 4.66 GB 64b3918e download
model-00005-of-00010.safetensors 4.66 GB 0a9e58fa download
model-00003-of-00010.safetensors 4.66 GB 61ed847e download
model-00002-of-00010.safetensors 4.66 GB 12a7167e download
model-00001-of-00010.safetensors 4.66 GB e4b37dc9 download
model-00010-of-00010.safetensors 2.36 GB 18d5585c download
model.safetensors.index.json 28.4 MB 07bc97be download
tokenizer.json 10.9 MB aeb13307 download
vocab.json 2.65 MB 4783fe10 download
merges.txt 1.59 MB 31349551 download
config.json 12.3 KB 7831f5ff download
tokenizer_config.json 5.28 KB c9fc1221 download
README.md 2.46 KB 3637863f download
.gitattributes 1.60 KB aa7aacd0 download
added_tokens.json 707 B b54f9135 download
special_tokens_map.json 613 B ac23c0aa download
chat_template.jinja 292 B 2116e45c download
generation_config.json 213 B 3dd2c545 download
recipe.yaml 202 B 79e427e7 download

README current version from Hugging Face


base_model: Shifusen/Qwen3-Next-80B-A3B-Instruct-Decensored
base_model_relation: quantized
library_name: transformers
model_name: Qwen3-Next-80B-A3B-Instruct-Decensored-NVFP4
tags:

  • quantization
  • nvfp4
  • fp4
  • uncensored
  • nsfw
  • generated_from_trainer
  • dpo
  • trl
    licence: license

Model Card for outputs/dpo-out

This model is a fine-tuned version of Qwen/Qwen3-Next-80B-A3B-Instruct.
It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with DPO, a method introduced in Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

Framework versions

  • TRL: 0.25.0
  • Transformers: 4.57.1
  • Pytorch: 2.8.0+cu128
  • Datasets: 4.4.1
  • Tokenizers: 0.22.1

Citations

Cite DPO as:

@inproceedings{rafailov2023direct,
    title        = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
    author       = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
    year         = 2023,
    booktitle    = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
    url          = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
    editor       = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}

Cite TRL as:

@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}

README history 1 version

The author's README evolved over time. Click a version to see its content at that point.

  1. 2026-01-21Upload folder using huggingface_hube9845522.5 KB
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