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Harry989/gemma-3-27b-it-abliterated-Q4_K_M-GGUF

sourceHugging Facegemmaupdated 1y agoView on Hugging Face

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huihui-ai/gemma-3-27b-it-abliterated

This is an uncensored version of google/gemma-3-27b-it created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.

It was only the text part that was processed, not the image part.

The abliterated model will no longer say "I'm sorry, but I cannot fulfill your request to ..."

Use with ollama

Ollama supports multimodal (Vision). gemma-3-abliterated defaults to f16, not Q4KM, and the effect of Q4KM is not very good, nor is it provided.

All new versions of gemma-3-abliterated have been released; please re-download and test.

You can use huihui_ai/gemma3-abliterated directly

ollama run huihui_ai/gemma3-abliterated:27b

Usage

You can use this model in your applications by loading it with Hugging Face's transformers library:

python

# pip install accelerate

from transformers import AutoProcessor, Gemma3ForConditionalGeneration
from PIL import Image
import requests
import torch

model_id = "huihui-ai/gemma-3-27b-it-abliterated"

model = Gemma3ForConditionalGeneration.from_pretrained(
    model_id, device_map="auto"
).eval()

processor = AutoProcessor.from_pretrained(model_id)

messages = [
    {
        "role": "system",
        "content": [{"type": "text", "text": "You are a helpful assistant."}]
    },
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
            {"type": "text", "text": "Describe this image in detail."}
        ]
    }
]

inputs = processor.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=True,
    return_dict=True, return_tensors="pt"
).to(model.device, dtype=torch.bfloat16)

input_len = inputs["input_ids"].shape[-1]

with torch.inference_mode():
    generation = model.generate(**inputs, max_new_tokens=100, do_sample=False)
    generation = generation[0][input_len:]

decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)

# **Overall Impression:** The image is a close-up shot of a vibrant garden scene, 
# focusing on a cluster of pink cosmos flowers and a busy bumblebee. 
# It has a slightly soft, natural feel, likely captured in daylight.

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