---
license: apache-2.0
base_model:
- Qwen/Qwen3-VL-8B-Instruct
language:
- en
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- text-generation-inference
- abliterated
- v1.0
---

![1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/mOF3DS7O621nEcqJjqZ2g.png)

# **Qwen3-VL-8B-Instruct-abliterated**

> **Qwen3-VL-8B-Instruct-abliterated** is an abliterated (v1.0) variant of Qwen3-VL-8B-Instruct, designed for Abliterated Reasoning and Captioning.
> This model is fine-tuned to produce highly detailed, descriptive, and reasoning-focused outputs across a wide range of visual and multimodal contexts, including complex, sensitive, or nuanced content. It supports varied image resolutions and aspect ratios while maintaining interpretive coherence and descriptive accuracy.

<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 Reasoning and Descriptions**
  Generates in-depth captions and reasoning for general, artistic, technical, abstract, and low-context images.

* **Robust Across Aspect Ratios**
  Performs consistently on wide, tall, square, panoramic, and irregular image dimensions.

* **Variational Detail Control**
  Capable of generating outputs ranging from concise summaries to intricate, multi-level descriptive reasoning.

* **Foundation on Qwen3-VL-8B-Instruct Architecture**
  Built upon Qwen3-VL-8B-Instruct’s multimodal reasoning, comprehension, and instruction-following framework.

* **Multilingual Output Capability**
  Primarily outputs in English, but adaptable to multiple languages 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-8B-Instruct-abliterated",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3-VL-8B-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_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, unfiltered captions and reasoning for general-purpose and artistic datasets.
* Research in content moderation, red-teaming, and generative safety analysis.
* Enabling descriptive captioning and reasoning for datasets typically excluded from mainstream models.
* Creative and exploratory applications such as storytelling, visual interpretation, and multimodal reasoning.
* Captioning and reasoning for non-standard, stylized, or abstract visual content.

## Limitations

* May generate explicit, sensitive, or offensive content depending on the prompt and input image.
* Not suitable for production environments that require strict content filtering or moderation.
* Output tone, style, and reasoning depth can vary depending on phrasing and visual complexity.
* May show variability in performance on synthetic or highly abstract visuals.