Qwen3-VL-4B-Instruct-abliterated is an abliterated (v1.0) variant of Qwen3-VL-4B-Instruct, tailored for Abliterated Reasoning and Captioning. This model is designed to generate detailed and descriptive captions, as well as reasoning outputs, across a wide range of visual and multimodal contexts—including complex, sensitive, or nuanced content—while supporting diverse aspect ratios and resolutions.

Key Highlights

Abliterated / Uncensored Captioning: Fine-tuned to bypass conventional content filters while preserving factual, descriptive, and reasoning-rich outputs.
High-Fidelity Descriptions: Generates comprehensive captions and reasoning for general, artistic, technical, abstract, or low-context images.
Robust Across Aspect Ratios: Supports wide, tall, square, and irregular image dimensions with consistent accuracy.
Variational Detail Control: Produces outputs ranging from high-level summaries to fine-grained, intricate descriptions and reasoning.
Foundation on Qwen3-VL-4B Architecture: Leverages Qwen3-VL-4B’s multimodal reasoning and instruction-following capabilities.
Multilingual Output Capability: Primarily English, with adaptability for multilingual prompts via prompt engineering.

Quick Start with Transformers

python

from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch

model = Qwen3VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/Qwen3-VL-4B-Instruct-abliterated-v1", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3-VL-4B-Instruct-abliterated-v1")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Provide a detailed caption and reasoning for this image."},
        ],
    }
]

text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

Intended Use

This model is suited for:

Generating detailed, uncensored captions and reasoning for general-purpose or artistic datasets.
Research in content moderation, red-teaming, and generative safety evaluation.
Enabling descriptive captioning and reasoning for visual datasets typically excluded from mainstream models.
Creative applications such as storytelling, art generation, or multimodal reasoning tasks.
Captioning and reasoning for non-standard aspect ratios and stylized visual content.

Limitations

May produce explicit, sensitive, or offensive descriptions depending on image content and prompts.
Not recommended for production systems requiring strict content moderation.
Output style, tone, and reasoning can vary depending on input phrasing.
Accuracy may vary for unfamiliar, synthetic, or highly abstract visual content.

vasya0089/Qwen3-VL-4B-Instruct-abliterated-v1

Qwen3-VL-4B-Instruct-abliterated

Key Highlights

Quick Start with Transformers

Intended Use

Limitations

vasya0089/
Qwen3-VL-4B-Instruct-abliterated-v1