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---
license: apache-2.0
base_model:
- Qwen/Qwen3-VL-32B-Instruct
language:
- en
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- text-generation-inference
- abliterated
- v1.0
---
# **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.
<img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/1xWD8FQ2qvqQzyqOzy05I.jpeg" alt="1" style="border-radius: 30px;"/>
## 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.
## 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-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. |