Huihui-gemma-3-270m-it-abliterated/README.md

base_model, license, tags, pipeline_tag, library_name

base_model	license	tags	pipeline_tag	library_name
google/gemma-3-270m-it	gemma	gemma3 gemma google generated_from_trainer trl sft abliterated uncensored	text-generation	transformers

Huihui-gemma-3-270m-it-abliterated (GGUF)

This repository contains GGUF format quantizations (F16 and Q8) of the abliterated/uncensored version of Google's gemma-3-270m-it, fine-tuned by huihui.ai and quantized by SkGufranAhmed.

The base model is an uncensored, abliterated version achieved through fine-tuning with the TRL framework.

Important Note: This model has been intentionally uncensored. Please read the Usage Warnings section before deploying.

Model Details

• Model Name: Huihui-gemma-3-270m-it-abliterated
• Base Model: google/gemma-3-270m-it
• Quantization Format: GGUF
• Available Quants:
  • F16 (16-bit float)
  • Q8 (8-bit quantization)

Training Procedure

The base model was trained using the SFT (Supervised Fine-Tuning) method with the TRL library.

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

Dataset: The fine-tuning dataset is English-only and all tests were conducted for the English language.

Usage

Using GGUF files with llama.cpp

1. Download the desired .gguf file from this repository (e.g., Huihui-gemma-3-270m-it-abliterated.Q8_0.gguf).
2. Run the model using llama-cli:

./llama-cli -m Huihui-gemma-3-270m-it-abliterated.Q8_0.gguf -p "If you had a time machine..." -n 128

Using the Original PyTorch Model (from huihui-ai)

If you prefer to use the unquantized PyTorch version, you can install it directly from the base model repository:

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"])

Note: For this GGUF repository, the model ID is SkGufranAhmed/Huihui-gemma-3-270m-it-abliterated. You can download the raw .gguf files directly from the "Files" tab.

Usage Warnings

🚨 CRITICAL: Please read before using this model. 🚨

• 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 and SkGufranAhmed bear no responsibility for any consequences arising from its use.

Donations & Follow

If you like this model, please:

• ⭐ Follow me on Hugging Face: SkGufranAhmed to stay updated on my latest quantizations and AI projects!
• Follow huihui.ai on X (Twitter): @support_huihui for the latest updates on the base model.

Your donation helps us continue our further development and improvement. Even a cup of coffee can make a difference!

• Bitcoin (BTC):

  bc1qt3lwtpeeg5ldsjj8yw7kjrqe6c89csm68wwn64