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--- |
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license: apache-2.0 |
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base_model: |
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- Qwen/Qwen3-VL-30B-A3B-Thinking |
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language: |
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- en |
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pipeline_tag: image-text-to-text |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- abliterated |
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- v1.0 |
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--- |
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# **Qwen3-VL-30B-A3B-Thinking-abliterated** |
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> **Qwen3-VL-30B-A3B-Thinking-abliterated** is an abliterated (v1.0) variant of Qwen3-VL-30B-A3B-Thinking, designed for Abliterated Reasoning and Captioning. |
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> 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. |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/1xWD8FQ2qvqQzyqOzy05I.jpeg" alt="1" style="border-radius: 30px;"/> |
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## Key Highlights |
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* **Abliterated / Uncensored Captioning and Reasoning** |
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Fine-tuned to bypass standard content filters while preserving factual accuracy, descriptive depth, and logical reasoning. |
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* **High-Fidelity Reasoning and Visual Understanding** |
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Generates detailed captions and structured reasoning for diverse visual categories—artistic, technical, abstract, or low-context. |
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* **Mixture of Experts (MoE) Efficiency** |
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Built on **Qwen3-VL-MoE**, dynamically routing computation through specialized experts for enhanced precision and scalability. |
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* **Aspect-Ratio Robustness** |
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Performs consistently across wide, tall, square, panoramic, and irregular visual formats. |
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* **Variational Detail Control** |
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Supports both concise summaries and highly detailed reasoning narratives, depending on prompt configuration. |
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* **Multilingual Output Capability** |
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Defaults to English but adaptable for multilingual use through prompt engineering. |
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## Quick Start with Transformers |
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```python |
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from transformers import Qwen3VLMoeForConditionalGeneration, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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import torch |
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model = Qwen3VLMoeForConditionalGeneration.from_pretrained( |
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"prithivMLmods/Qwen3-VL-30B-A3B-Thinking-abliterated-v1", |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3-VL-30B-A3B-Thinking-abliterated-v1") |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "image", |
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", |
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}, |
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{"type": "text", "text": "Provide a detailed caption and reasoning for this image."}, |
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], |
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} |
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] |
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text = processor.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True |
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) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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).to("cuda") |
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generated_ids = model.generate(**inputs, max_new_tokens=128) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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output_text = processor.batch_decode( |
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generated_ids_trimmed, |
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skip_special_tokens=True, |
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clean_up_tokenization_spaces=False |
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) |
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print(output_text) |
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``` |
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## Intended Use |
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* Generating detailed, uncensored captions and reasoning for complex or creative visual datasets. |
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* Research in multimodal reasoning, safety evaluation, and content moderation studies. |
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* Enabling descriptive captioning and analytical reasoning for datasets excluded from mainstream models. |
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* Creative applications such as narrative generation, artistic interpretation, and visual storytelling. |
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* Advanced reasoning over diverse visual structures and aspect ratios. |
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## Limitations |
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* May produce explicit, sensitive, or offensive content depending on input and prompt. |
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* Not recommended for deployment in production systems that require strict moderation or filtering. |
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* Style, tone, and reasoning detail can vary based on prompt phrasing. |
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* May show variable performance on synthetic, abstract, or highly stylized visual inputs. |
