---
license: apache-2.0
base_model:
- Qwen/Qwen3-VL-30B-A3B-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/5-zzb13RZrHDvNZMBzr4U.png)

# **Qwen3-VL-30B-A3B-Instruct-abliterated**

> **Qwen3-VL-30B-A3B-Instruct-abliterated** is an abliterated (v1.0) variant of Qwen3-VL-30B-A3B-Instruct**, designed for Abliterated Reasoning and Captioning.
> This model leverages the Qwen3-VL-MoE (Mixture of Experts) architecture to deliver deeply descriptive, context-rich, and reasoning-oriented multimodal outputs. It handles complex, sensitive, and nuanced visual content while maintaining balanced interpretive coherence and multilingual adaptability.

<img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/1xWD8FQ2qvqQzyqOzy05I.jpeg" alt="1" style="border-radius: 30px;"/>

## Key Highlights

* **Abliterated / Uncensored Captioning and Reasoning**
  Fine-tuned to bypass standard content filters while preserving factual accuracy, descriptive depth, and logical reasoning.

* **High-Fidelity Reasoning and Visual Understanding**
  Generates detailed captions and structured reasoning for diverse visual categories—artistic, technical, abstract, or low-context.

* **Mixture of Experts (MoE) Efficiency**
  Built on **Qwen3-VL-MoE**, dynamically routing computation through specialized experts for enhanced precision and scalability.

* **Aspect-Ratio Robustness**
  Performs consistently across wide, tall, square, panoramic, and irregular visual formats.

* **Variational Detail Control**
  Supports both concise summaries and highly detailed reasoning narratives, depending on prompt configuration.

* **Multilingual Output Capability**
  Defaults to English but adaptable for multilingual use through prompt engineering.

## Quick Start with Transformers

```python
from transformers import Qwen3VLMoeForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch

model = Qwen3VLMoeForConditionalGeneration.from_pretrained(
    "prithivMLmods/Qwen3-VL-30B-A3B-Instruct-abliterated",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3-VL-30B-A3B-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, uncensored captions and reasoning for complex or creative visual datasets.
* Research in multimodal reasoning, safety evaluation, and content moderation studies.
* Enabling descriptive captioning and analytical reasoning for datasets excluded from mainstream models.
* Creative applications such as narrative generation, artistic interpretation, and visual storytelling.
* Advanced reasoning over diverse visual structures and aspect ratios.

## Limitations

* May produce explicit, sensitive, or offensive content depending on input and prompt.
* Not recommended for deployment in production systems that require strict moderation or filtering.
* Style, tone, and reasoning detail can vary based on prompt phrasing.
* May show variable performance on synthetic, abstract, or highly stylized visual inputs.