prithivMLmods/Qwen3-VL-30B-A3B-Instruct-abliterated-v1

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.

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.

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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-30B-A3B-Instruct-abliterated.

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Quick Start with Transformers

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-v1",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3-VL-30B-A3B-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_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.