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Running
on
Zero
Running
on
Zero
Update app.py
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app.py
CHANGED
@@ -291,8 +291,6 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
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> [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct): The Qwen2.5-VL-7B-Instruct model is a multimodal AI model developed by Alibaba Cloud that excels at understanding both text and images. It's a Vision-Language Model (VLM) designed to handle various visual understanding tasks, including image understanding, video analysis, and even multilingual support.
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> [Qwen2.5-VL-7B-Abliterated-Caption-it](prithivMLmods/Qwen2.5-VL-7B-Abliterated-Caption-it): Qwen2.5-VL-7B-Abliterated-Caption-it is a fine-tuned version of Qwen2.5-VL-7B-Instruct, optimized for Abliterated Captioning / Uncensored Captioning. This model excels at generating detailed, context-rich, and high-fidelity captions across diverse image categories and variational aspect ratios, offering robust visual understanding without filtering or censorship.
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> [olmOCR-7B-0825](https://huggingface.co/allenai/olmOCR-7B-0825): olmOCR-7B-0825 is a 7B parameter open large model designed for OCR tasks with robust text extraction, especially in complex document layouts. Multimodal model emphasizing strong document reading and extraction capabilities combined with vision-language understanding to support detailed document parsing tasks.
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"""
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> [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct): The Qwen2.5-VL-7B-Instruct model is a multimodal AI model developed by Alibaba Cloud that excels at understanding both text and images. It's a Vision-Language Model (VLM) designed to handle various visual understanding tasks, including image understanding, video analysis, and even multilingual support.
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> [Qwen2.5-VL-7B-Abliterated-Caption-it](prithivMLmods/Qwen2.5-VL-7B-Abliterated-Caption-it): Qwen2.5-VL-7B-Abliterated-Caption-it is a fine-tuned version of Qwen2.5-VL-7B-Instruct, optimized for Abliterated Captioning / Uncensored Captioning. This model excels at generating detailed, context-rich, and high-fidelity captions across diverse image categories and variational aspect ratios, offering robust visual understanding without filtering or censorship.
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"""
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