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Qwen3-VL-4B-Thinking-abliterated

Qwen3-VL-4B-Thinking-abliterated is an abliterated (v1.0) variant of Qwen3-VL-4B-Thinking, designed for Abliterated Reasoning and Captioning. This model generates detailed captions and reasoning outputs across a wide range of visual and multimodal contexts, including complex, sensitive, or nuanced content, and supports diverse aspect ratios and resolutions.

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Key Highlights

• Abliterated / Uncensored Captioning: Fine-tuned to bypass standard content filters while preserving factual, descriptive, and reasoning-rich outputs.
• High-Fidelity Descriptions: Produces 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: Generates outputs ranging from high-level summaries to fine-grained, intricate descriptions and reasoning.
• Foundation on Qwen3-VL-4B-Thinking Architecture: Leverages Qwen3-VL-4B-Thinking’s multimodal reasoning and instruction-following capabilities.
• Multilingual Output Capability: Primarily English, with adaptability for multilingual prompts via prompt engineering.

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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-4B-Thinking-abliterated.

---

Quick Start with Transformers

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

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

processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3-VL-4B-Thinking-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.

Base model

Qwen/Qwen3-VL-4B-Thinking

(23)

Qwen2.5-VL and Qwen3-VL • 4 items • Updated 2 days ago • 2

Qwen3-VL Abliterated Model Collection [ Version 1.0 ] • 22 items • Updated 2 days ago • 18