Qwen3-VL-32B-Instruct-abliterated
Qwen3-VL-32B-Instruct-abliterated is an abliterated (v1.0) variant of Qwen3-VL-32B-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-32B Architecture – Built upon Qwen3-VL-32B-Instruct’s advanced 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-32B-Instruct-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-32B-Instruct-abliterated-v1",
torch_dtype="auto",
device_map="auto"
)
processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3-VL-32B-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",
).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
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 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.