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DrNicefellow/uncensored_gpt_oss

DrNicefellow Gpt-oss 21B
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Response includes
  • classification m-uncensored
  • files 21
  • benchmarks 16 entries
  • hub_downloads_all_time 102
  • author_summary 13 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
Refusal direction extraction

No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

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Downloads · lifetime
102
19 last 30d - stable
Likes
0
Model age
11mo ago
created 2025-10-20
Downloads over time
Now109→from20↑445%
16508411820 on Oct 22, 2025109 on Oct 11Oct '25Dec '25FebAprJunAugOct
Oct 22, 2025 → Oct 11 · 90 snapshots · spans 354 days

Benchmarks

Portrait before abliteration
Benchmarks of the base model as it stood before the refusal-removal operation. Compare with the numbers above to see what the operation cost.
Benchmark Score Source
Arena-Battles 7952 LM-Arena
LM Arena Elo 1307.3530229799078 LM-Arena
Arena-Elo-Lower 1300.2396666200118 LM-Arena
Arena-Elo-Upper 1314.4663793398038 LM-Arena
Arena-Rank 70 LM-Arena
Entertainment 1.1 UGI
Hazardous 0 UGI
Natural Intelligence 17.39 UGI
Political lean -10.6% UGI
Sensitive-Info 7.19 UGI
SocPol 0.8 UGI
UGI 8.96 UGI
Willingness (10) 1.2 UGI
W10-Adherence 1.5 UGI
W10-Direct 1 UGI
Writing 24.62 UGI

Genealogy 0 direct forks

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This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Metadata

Tags
transformers safetensors gpt_oss text-generation generated_from_trainer trl sft conversational base_model:openai/gpt-oss-20b base_model:finetune:openai/gpt-oss-20b endpoints_compatible region:us

Related

Total size
39.0 GB
Files
21
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2025-10-20 23:14

Files by quantization

Auxiliary files 21 files 39.0 GB
model-00005-of-00009.safetensors 4.60 GB f59e45b0 download
model-00006-of-00009.safetensors 4.60 GB 68c3782f download
model-00007-of-00009.safetensors 4.60 GB 82654433 download
model-00008-of-00009.safetensors 4.60 GB d470fc83 download
model-00004-of-00009.safetensors 4.60 GB 58d87f48 download
model-00002-of-00009.safetensors 4.60 GB dd1c751a download
model-00003-of-00009.safetensors 4.60 GB b6d793c8 download
model-00001-of-00009.safetensors 4.19 GB 49feabd4 download
model-00009-of-00009.safetensors 2.56 GB 6c1951ff download
tokenizer.json 26.6 MB 0614fe83 download
vocab.json 2.65 MB 4783fe10 download
merges.txt 1.59 MB 31349551 download
model.safetensors.index.json 32.8 KB 7353bb66 download
chat_template.jinja 16.3 KB dc7bb119 download
tokenizer_config.json 4.10 KB c021cddb download
.gitattributes 1.66 KB f9229fcd download
config.json 1.60 KB e44356fa download
README.md 1.39 KB 73500a62 download
added_tokens.json 605 B 482ced46 download
special_tokens_map.json 440 B 6274cc1b download
generation_config.json 177 B 2d7936b2 download

README current version from Hugging Face


base_model: openai/gpt-oss-20b
library_name: transformers
model_name: outputs
tags:

  • generated_from_trainer
  • trl
  • sft
    licence: license

Model Card for outputs

This model is a fine-tuned version of openai/gpt-oss-20b.
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 SFT.

Framework versions

  • TRL: 0.24.0
  • Transformers: 4.57.0.dev0
  • Pytorch: 2.8.0+cu129
  • Datasets: 4.2.0
  • Tokenizers: 0.22.1

Citations

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. 2025-10-20Upload folder using huggingface_hubd2afa511.4 KB
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