Qwen3-VL-2B-Instruct-abliterated is an abliterated (v1.0) variant of Qwen3-VL-2B-Instruct, designed for Abliterated Reasoning and Captioning. This model is optimized to generate detailed, descriptive captions and 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 – Performs consistently across wide, tall, square, and irregular image dimensions.
Variational Detail Control – Capable of producing outputs ranging from concise summaries to fine-grained, intricate descriptions and reasoning.
Foundation on Qwen3-VL-2B Architecture – Built upon Qwen3-VL-2B-Instruct’s strong multimodal reasoning and instruction-following capabilities.
Multilingual Output Capability – Primarily optimized for English, with adaptability for multilingual prompts through prompt engineering.

Base Model Signatures:

This model has been re-sharded and optimized for the latest Transformers version from the base model: https://huggingface.co/huihui-ai/Huihui-Qwen3-VL-2B-Instruct-abliterated.

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-2B-Instruct-abliterated",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3-VL-2B-Instruct-abliterated")

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",
).to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [out[len(inp):] for inp, out 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

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 the image content and prompts.
Not recommended for production systems requiring strict content moderation.
Output style, tone, and reasoning may vary based on input phrasing.
Accuracy can fluctuate for unfamiliar, synthetic, or highly abstract visual content.

prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1

Qwen3-VL-2B-Instruct-abliterated

Key Highlights

Base Model Signatures:

Quick Start with Transformers

Intended Use

Limitations

prithivMLmods/
Qwen3-VL-2B-Instruct-abliterated-v1