codeShare/gemma-4-E2B-it-heretic-image-caption-adapter

Gemma 4 E2B Uncensored Image Caption Adapter

A multimodal LoRA adapter fine-tuned for image understanding and image caption generation.

Description

This is an early version trained on approximately 3,000 AI-generated images and prompts for models such as Chroma, Krea 2 and Moss Image.

The adapter is designed to improve the image captioning capabilities of an abliterated Gemma 4 E2B model, particularly for describing AI-generated imagery.

The underlying model has had its refusal behavior modified through abliteration. This adapter aims to improve its image-description capabilities.

Model information

Property	Value
Base model	coder3101/gemma-4-E2B-it-heretic
Architecture	PEFT LoRA
Format	Safetensors
Task	Image captioning
Processor	Included

Installation

pip install torch transformers peft accelerate safetensors pillow

Loading the adapter

import torch
from transformers import AutoProcessor, AutoModelForMultimodalLM
from peft import PeftModel

BASE_MODEL = "coder3101/gemma-4-E2B-it-heretic"
ADAPTER = "codeShare/gemma-4-E2B-it-heretic-image-caption-adapter"

processor = AutoProcessor.from_pretrained(ADAPTER)

base_model = AutoModelForMultimodalLM.from_pretrained(
    BASE_MODEL,
    torch_dtype=torch.float16,
    device_map="auto",
)

model = PeftModel.from_pretrained(
    base_model,
    ADAPTER,
)

model.eval()

Usage

Use the included processor and the conversation format supported by the base model.

This repository contains a standard PEFT LoRA adapter, not a merged standalone model.

Training

Multimodal QLoRA fine-tuning.

The original training dataset and private training artifacts are not included.

License

Please refer to the base model's license and usage restrictions.