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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-8B-Instruct |
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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-8B-Instruct-abliterated** |
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> **Qwen3-VL-8B-Instruct-abliterated** is an abliterated (v1.0) variant of Qwen3-VL-8B-Instruct, designed for Abliterated Reasoning and Captioning. |
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> 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. |
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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** |
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Fine-tuned to bypass conventional content filters while preserving factual, descriptive, and reasoning-rich outputs. |
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* **High-Fidelity Reasoning and Descriptions** |
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Generates in-depth captions and reasoning for general, artistic, technical, abstract, and low-context images. |
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* **Robust Across Aspect Ratios** |
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Performs consistently on wide, tall, square, panoramic, and irregular image dimensions. |
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* **Variational Detail Control** |
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Capable of generating outputs ranging from concise summaries to intricate, multi-level descriptive reasoning. |
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* **Foundation on Qwen3-VL-8B-Instruct Architecture** |
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Built upon Qwen3-VL-8B-Instruct’s multimodal reasoning, comprehension, and instruction-following framework. |
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* **Multilingual Output Capability** |
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Primarily outputs in English, but adaptable to multiple languages via prompt engineering. |
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## Quick Start with Transformers |
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```python |
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from transformers import Qwen3VLForConditionalGeneration, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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import torch |
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model = Qwen3VLForConditionalGeneration.from_pretrained( |
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"prithivMLmods/Qwen3-VL-8B-Instruct-abliterated", |
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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-8B-Instruct-abliterated") |
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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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This model is suited for: |
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* Generating detailed, unfiltered captions and reasoning for general-purpose and artistic datasets. |
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* Research in content moderation, red-teaming, and generative safety analysis. |
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* Enabling descriptive captioning and reasoning for datasets typically excluded from mainstream models. |
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* Creative and exploratory applications such as storytelling, visual interpretation, and multimodal reasoning. |
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* Captioning and reasoning for non-standard, stylized, or abstract visual content. |
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## Limitations |
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* May generate explicit, sensitive, or offensive content depending on the prompt and input image. |
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* Not suitable for production environments that require strict content filtering or moderation. |
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* Output tone, style, and reasoning depth can vary depending on phrasing and visual complexity. |
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* May show variability in performance on synthetic or highly abstract visuals. |
