base_model: google/gemma-3-270m-it
license: gemma
tags:
- gemma3
- gemma
- 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.
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