prithivMLmods commited on
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Update app.py

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  1. app.py +1 -1
app.py CHANGED
@@ -337,10 +337,10 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
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  gr.Markdown("**Model Info 💻** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/Multimodal-VLMs-5x/discussions)")
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  gr.Markdown("> [Vision Matters 7B Math](https://huggingface.co/Yuting6/Vision-Matters-7B): vision-matters is a simple visual perturbation framework that can be easily integrated into existing post-training pipelines including sft, dpo, and grpo. our findings highlight the critical role of visual perturbation: better reasoning begins with better seeing.")
 
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  gr.Markdown("> [ViGaL 7B](https://huggingface.co/yunfeixie/ViGaL-7B): vigal-7b shows that training a 7b mllm on simple games like snake using reinforcement learning boosts performance on benchmarks like mathvista and mmmu without needing worked solutions or diagrams indicating transferable reasoning skills.")
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  gr.Markdown("> [Visionary-R1](https://huggingface.co/maifoundations/Visionary-R1): visionary-r1 is a novel framework for training visual language models (vlms) to perform robust visual reasoning using reinforcement learning (rl). unlike traditional approaches that rely heavily on (sft) or (cot) annotations, visionary-r1 leverages only visual question-answer pairs and rl, making the process more scalable and accessible.")
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  gr.Markdown("> [R1-Onevision-7B](https://huggingface.co/Fancy-MLLM/R1-Onevision-7B): r1-onevision model enhances vision-language understanding and reasoning capabilities, making it suitable for various tasks such as visual reasoning and image understanding. with its robust ability to perform multimodal reasoning, r1-onevision emerges as a powerful ai assistant capable of addressing different domains.")
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- gr.Markdown("> [MonkeyOCR-3B-0709](https://huggingface.co/omlab/VLM-R1-Qwen2.5VL-3B-Math-0305): vlm-r1 is a framework designed to enhance the reasoning and generalization capabilities of vision-language models (vlms) using a reinforcement learning (rl) approach inspired by the r1 methodology originally developed for large language models.")
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  gr.Markdown(">⚠️note: all the models in space are not guaranteed to perform well in video inference use cases.")
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  # Define the submit button actions
 
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  gr.Markdown("**Model Info 💻** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/Multimodal-VLMs-5x/discussions)")
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  gr.Markdown("> [Vision Matters 7B Math](https://huggingface.co/Yuting6/Vision-Matters-7B): vision-matters is a simple visual perturbation framework that can be easily integrated into existing post-training pipelines including sft, dpo, and grpo. our findings highlight the critical role of visual perturbation: better reasoning begins with better seeing.")
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+ gr.Markdown("> [MonkeyOCR-3B-0709](https://huggingface.co/echo840/MonkeyOCR-3B-0709): MonkeyOCR adopts a structure-recognition-relation (SRR) triplet paradigm, which simplifies the multi-tool pipeline of modular approaches while avoiding the inefficiency of using large multimodal models for full-page document processing.")
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  gr.Markdown("> [ViGaL 7B](https://huggingface.co/yunfeixie/ViGaL-7B): vigal-7b shows that training a 7b mllm on simple games like snake using reinforcement learning boosts performance on benchmarks like mathvista and mmmu without needing worked solutions or diagrams indicating transferable reasoning skills.")
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  gr.Markdown("> [Visionary-R1](https://huggingface.co/maifoundations/Visionary-R1): visionary-r1 is a novel framework for training visual language models (vlms) to perform robust visual reasoning using reinforcement learning (rl). unlike traditional approaches that rely heavily on (sft) or (cot) annotations, visionary-r1 leverages only visual question-answer pairs and rl, making the process more scalable and accessible.")
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  gr.Markdown("> [R1-Onevision-7B](https://huggingface.co/Fancy-MLLM/R1-Onevision-7B): r1-onevision model enhances vision-language understanding and reasoning capabilities, making it suitable for various tasks such as visual reasoning and image understanding. with its robust ability to perform multimodal reasoning, r1-onevision emerges as a powerful ai assistant capable of addressing different domains.")
 
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  gr.Markdown(">⚠️note: all the models in space are not guaranteed to perform well in video inference use cases.")
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  # Define the submit button actions