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wetr3d/Huihui-gemma-3-270m-it-abliterated

wetr3d Gemma 268M
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     "https://abliteration.org/api/v1/models/wetr3d%2FHuihui-gemma-3-270m-it-abliterated"
Response includes
  • classification m1
  • files 11
  • benchmarks 11 entries
  • hub_downloads_all_time 58
  • author_summary 1 models
  • readme_text full
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Abliteration classifier · v1.0.0
M1
Primary method

Direct removal

No other method signals detected in this model.
Confidence
MEDIUM
Why this label 3 signals
Method inferred from partial signals - repository name, related files, or tag patterns. Producer identity not confirmed; label may sharpen or shift as we gather more evidence.
  • 'abliterated' in name/tags
  • is_gguf=0 (base model)
  • no specific method indicators - defaulting to M1 (most common)
Refusal direction extracted via
Extraction technique

Difference-of-means

Confidence
MEDIUM
Why we say so
primary_method=M1; difference-of-means is the reference extraction for M1/M3 (Arditi 2024)
Downloads · lifetime
58
17 last 30d - stable
Likes
0
Model age
3mo ago
created 2026-06-15
Downloads over time
Now65→from20↑225%
1835527020 on Jun 1765 on Oct 11JunJulAugSepOct
Jun 17 → Oct 11 · 56 snapshots · spans 116 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
Entertainment 0.1 UGI
Hazardous 0 UGI
Natural Intelligence 5.18 UGI
Political lean NA UGI
Sensitive-Info 2.3 UGI
SocPol 0.5 UGI
UGI 3.2 UGI
Willingness (10) 0.5 UGI
W10-Adherence 0 UGI
W10-Direct 1 UGI
Writing NA UGI

Genealogy 0 direct forks

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Metadata

License
gemma
Tags
transformers safetensors gemma3_text text-generation gemma3 gemma google generated_from_trainer trl sft abliterated uncensored

Related

Total size
511 MB
Files
11
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-06-15 15:07

Files by quantization

Auxiliary files 11 files 549 MB
model.safetensors 511 MB df92607f download
tokenizer.json 31.8 MB 7d4046bf download
tokenizer.model 4.47 MB 1299c11d download
tokenizer_config.json 1.10 MB 55042383 download
README.md 3.11 KB 80e6bfa4 download
chat_template.jinja 1.54 KB c5f13654 download
.gitattributes 1.53 KB 52373fe2 download
config.json 1.32 KB 48bf8ed0 download
special_tokens_map.json 662 B 1a619324 download
generation_config.json 173 B 8abad5a8 download
added_tokens.json 38.0 B f9f1f4f5 download

README current version from Hugging Face


base_model: google/gemma-3-270m-it
license: gemma
tags:

  • gemma3
  • gemma
  • google
  • generated_from_trainer
  • trl
  • sft
  • abliterated
  • uncensored
    pipeline_tag: text-generation
    library_name: transformers

huihui-ai/Huihui-gemma-3-270m-it-abliterated

This is an uncensored version of google/gemma-3-270m-it, achieved through fine-tuning with the TRL framework.

The dataset used for fine-tuning is only in English, does not involve other languages, and all tests are conducted solely for English.

Training procedure

This model was trained with SFT.

Framework versions

  • TRL: 0.21.0
  • Transformers: 4.56.0.dev0
  • Pytorch: 2.8.0+cu128
  • Datasets: 3.6.0
  • Tokenizers: 0.21.2

ollama

You can use huihui_ai/gemma3-abliterated:270m directly,

ollama run huihui_ai/gemma3-abliterated:270m

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="huihui-ai/Huihui-gemma-3-270m-it-abliterated", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Usage Warnings

  • Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.

  • Not Suitable for All Audiences: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.

  • Legal and Ethical Responsibilities: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.

  • Research and Experimental Use: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.

  • Monitoring and Review Recommendations: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.

  • No Default Safety Guarantees: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.

Donation

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README history 1 version

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

  1. 2026-06-15Duplicate from huihui-ai/Huihui-gemma-3-270m-it-abliterated0d156d73.1 KB
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