MiniCPM-V: A GPT-4V Level MLLM on Your Phone
The recent surge of Multimodal Large Language Models (MLLMs) has fundamentally reshaped the landscape of #AI research and industry, shedding light on a promising path toward the next AI milestone. However, significant challenges remain preventing MLLMs from being practical in real-world applications. The most notable challenge comes from the huge cost of running an MLLM with a massive number of parameters and extensive computation. As a result, most MLLMs need to be deployed on high-performing cloud servers, which greatly limits their application scopes such as mobile, offline, energy-sensitive, and privacy-protective scenarios. In this work, we present MiniCPM-V, a series of efficient #MLLMs deployable on end-side devices. By integrating the latest MLLM techniques in architecture, pretraining and alignment, the latest MiniCPM-Llama3-V 2.5 has several notable features: (1) Strong performance, outperforming GPT-4V-1106, Gemini Pro and Claude 3 on OpenCompass, a comprehensive evaluation over 11 popular benchmarks, (2) strong #OCR capability and 1.8M pixel high-resolution #image perception at any aspect ratio, (3) trustworthy behavior with low hallucination rates, (4) multilingual support for 30+ languages, and (5) efficient deployment on mobile phones. More importantly, MiniCPM-V can be viewed as a representative example of a promising trend: The model sizes for achieving usable (e.g., GPT-4V) level performance are rapidly decreasing, along with the fast growth of end-side computation capacity. This jointly shows that GPT-4V level MLLMs deployed on end devices are becoming increasingly possible, unlocking a wider spectrum of real-world AI applications in the near future.
Paper: https://arxiv.org/pdf/2408.01800v1.pdf
Codes:
https://github.com/OpenBMB/MiniCPM-o
https://github.com/openbmb/minicpm-v
Datasets: Video-MME
#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics
https://t.iss.one/DataScienceT❤️
The recent surge of Multimodal Large Language Models (MLLMs) has fundamentally reshaped the landscape of #AI research and industry, shedding light on a promising path toward the next AI milestone. However, significant challenges remain preventing MLLMs from being practical in real-world applications. The most notable challenge comes from the huge cost of running an MLLM with a massive number of parameters and extensive computation. As a result, most MLLMs need to be deployed on high-performing cloud servers, which greatly limits their application scopes such as mobile, offline, energy-sensitive, and privacy-protective scenarios. In this work, we present MiniCPM-V, a series of efficient #MLLMs deployable on end-side devices. By integrating the latest MLLM techniques in architecture, pretraining and alignment, the latest MiniCPM-Llama3-V 2.5 has several notable features: (1) Strong performance, outperforming GPT-4V-1106, Gemini Pro and Claude 3 on OpenCompass, a comprehensive evaluation over 11 popular benchmarks, (2) strong #OCR capability and 1.8M pixel high-resolution #image perception at any aspect ratio, (3) trustworthy behavior with low hallucination rates, (4) multilingual support for 30+ languages, and (5) efficient deployment on mobile phones. More importantly, MiniCPM-V can be viewed as a representative example of a promising trend: The model sizes for achieving usable (e.g., GPT-4V) level performance are rapidly decreasing, along with the fast growth of end-side computation capacity. This jointly shows that GPT-4V level MLLMs deployed on end devices are becoming increasingly possible, unlocking a wider spectrum of real-world AI applications in the near future.
Paper: https://arxiv.org/pdf/2408.01800v1.pdf
Codes:
https://github.com/OpenBMB/MiniCPM-o
https://github.com/openbmb/minicpm-v
Datasets: Video-MME
#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics
https://t.iss.one/DataScienceT
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A demo of the new #model is now available on #huggingface
Excellent model for #OCR tasks, text extraction, image recognition and chat use.
🤗 HF: https://huggingface.co/spaces/deepseek-ai/deepseek-vl2-small
#DataScience #ArtificialIntelligence #MachineLearning #PythonProgramming #DeepLearning #LLM #AIResearch #BigData #NeuralNetworks #DataAnalytics #NLP #AutoML #DataVisualization #ScikitLearn #Pandas #NumPy #TensorFlow #AIethics #PredictiveModeling #GPUComputing #OpenSourceAI
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🤖🧠 PaddleOCR-VL: Redefining Multilingual Document Parsing with a 0.9B Vision-Language Model
🗓️ 20 Oct 2025
📚 AI News & Trends
In an era where information is predominantly digital, the ability to extract, interpret and organize data from documents is crucial. From invoices and research papers to multilingual contracts and handwritten notes, document parsing stands at the intersection of vision and language. Traditional Optical Character Recognition (OCR) systems have made impressive strides but they often fall ...
#PaddleOCR-VL #Multilingual #DocumentParsing #VisionLanguageModel #OCR #AI
🗓️ 20 Oct 2025
📚 AI News & Trends
In an era where information is predominantly digital, the ability to extract, interpret and organize data from documents is crucial. From invoices and research papers to multilingual contracts and handwritten notes, document parsing stands at the intersection of vision and language. Traditional Optical Character Recognition (OCR) systems have made impressive strides but they often fall ...
#PaddleOCR-VL #Multilingual #DocumentParsing #VisionLanguageModel #OCR #AI
✨PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model
📝 Summary:
PaddleOCR-VL is a new 0.9B vision-language model for document parsing. It uses a NaViT-style visual encoder and ERNIE-4.5, achieving state-of-the-art performance across 109 languages with minimal resources and fast inference. This model is highly suitable for practical deployment.
🔹 Publication Date: Published on Oct 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.14528
• PDF: https://arxiv.org/pdf/2510.14528
• Github: https://github.com/PaddlePaddle/PaddleOCR
🔹 Models citing this paper:
• https://huggingface.co/PaddlePaddle/PaddleOCR-VL
• https://huggingface.co/PaddlePaddle/PP-DocLayoutV2
• https://huggingface.co/lvyufeng/PaddleOCR-VL-0.9B
✨ Spaces citing this paper:
• https://huggingface.co/spaces/PaddlePaddle/PaddleOCR-VL_Online_Demo
• https://huggingface.co/spaces/markobinario/PaddleOCR-VL_Online_Demo
• https://huggingface.co/spaces/waytoAGI/PaddleOCR-VL_Online_Demo
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#OCR #VisionLanguageModel #DocumentAI #DeepLearning #AI
📝 Summary:
PaddleOCR-VL is a new 0.9B vision-language model for document parsing. It uses a NaViT-style visual encoder and ERNIE-4.5, achieving state-of-the-art performance across 109 languages with minimal resources and fast inference. This model is highly suitable for practical deployment.
🔹 Publication Date: Published on Oct 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.14528
• PDF: https://arxiv.org/pdf/2510.14528
• Github: https://github.com/PaddlePaddle/PaddleOCR
🔹 Models citing this paper:
• https://huggingface.co/PaddlePaddle/PaddleOCR-VL
• https://huggingface.co/PaddlePaddle/PP-DocLayoutV2
• https://huggingface.co/lvyufeng/PaddleOCR-VL-0.9B
✨ Spaces citing this paper:
• https://huggingface.co/spaces/PaddlePaddle/PaddleOCR-VL_Online_Demo
• https://huggingface.co/spaces/markobinario/PaddleOCR-VL_Online_Demo
• https://huggingface.co/spaces/waytoAGI/PaddleOCR-VL_Online_Demo
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#OCR #VisionLanguageModel #DocumentAI #DeepLearning #AI
arXiv.org
PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B...
In this report, we propose PaddleOCR-VL, a SOTA and resource-efficient model tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a compact yet powerful vision-language model...
✨olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models
📝 Summary:
olmOCR is an open-source toolkit that uses a fine-tuned vision language model to convert PDFs into clean, structured text. It enables large-scale, cost-effective extraction of trillions of tokens for training language models.
🔹 Publication Date: Published on Feb 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.18443
• PDF: https://arxiv.org/pdf/2502.18443
• Github: https://github.com/allenai/olmocr
✨ Datasets citing this paper:
• https://huggingface.co/datasets/davanstrien/test-olmocr2
• https://huggingface.co/datasets/davanstrien/newspapers-olmocr2
• https://huggingface.co/datasets/stckmn/ocr-output-Directive017-1761355297
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#OCR #VLMs #LLM #DataExtraction #OpenSource
📝 Summary:
olmOCR is an open-source toolkit that uses a fine-tuned vision language model to convert PDFs into clean, structured text. It enables large-scale, cost-effective extraction of trillions of tokens for training language models.
🔹 Publication Date: Published on Feb 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.18443
• PDF: https://arxiv.org/pdf/2502.18443
• Github: https://github.com/allenai/olmocr
✨ Datasets citing this paper:
• https://huggingface.co/datasets/davanstrien/test-olmocr2
• https://huggingface.co/datasets/davanstrien/newspapers-olmocr2
• https://huggingface.co/datasets/stckmn/ocr-output-Directive017-1761355297
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#OCR #VLMs #LLM #DataExtraction #OpenSource
✨HunyuanOCR Technical Report
📝 Summary:
HunyuanOCR is a lightweight Vision-Language Model for OCR, using a unified end-to-end architecture ViT + LLM. It achieves state-of-the-art performance in diverse tasks, outperforming larger models and commercial APIs, powered by data-driven and RL strategies.
🔹 Publication Date: Published on Nov 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19575
• PDF: https://arxiv.org/pdf/2511.19575
• Github: https://github.com/Tencent-Hunyuan/HunyuanOCR
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#OCR #VisionLanguageModel #LLM #AI #MachineLearning
📝 Summary:
HunyuanOCR is a lightweight Vision-Language Model for OCR, using a unified end-to-end architecture ViT + LLM. It achieves state-of-the-art performance in diverse tasks, outperforming larger models and commercial APIs, powered by data-driven and RL strategies.
🔹 Publication Date: Published on Nov 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19575
• PDF: https://arxiv.org/pdf/2511.19575
• Github: https://github.com/Tencent-Hunyuan/HunyuanOCR
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#OCR #VisionLanguageModel #LLM #AI #MachineLearning
✨NVIDIA Nemotron Parse 1.1
📝 Summary:
Nemotron-Parse-1.1 is a lightweight OCR and document parsing model with improved capabilities. It excels in general OCR, markdown, structured tables, and text extraction from images using an encoder-decoder architecture. The model achieves competitive accuracy and is publicly released.
🔹 Publication Date: Published on Nov 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20478
• PDF: https://arxiv.org/pdf/2511.20478
🔹 Models citing this paper:
• https://huggingface.co/nvidia/NVIDIA-Nemotron-Parse-v1.1
• https://huggingface.co/nvidia/NVIDIA-Nemotron-Parse-v1.1-TC
✨ Spaces citing this paper:
• https://huggingface.co/spaces/prithivMLmods/NVIDIA-Nemotron-Parse-OCR
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#OCR #DocumentParsing #DeepLearning #AI #NVIDIA
📝 Summary:
Nemotron-Parse-1.1 is a lightweight OCR and document parsing model with improved capabilities. It excels in general OCR, markdown, structured tables, and text extraction from images using an encoder-decoder architecture. The model achieves competitive accuracy and is publicly released.
🔹 Publication Date: Published on Nov 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20478
• PDF: https://arxiv.org/pdf/2511.20478
🔹 Models citing this paper:
• https://huggingface.co/nvidia/NVIDIA-Nemotron-Parse-v1.1
• https://huggingface.co/nvidia/NVIDIA-Nemotron-Parse-v1.1-TC
✨ Spaces citing this paper:
• https://huggingface.co/spaces/prithivMLmods/NVIDIA-Nemotron-Parse-OCR
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#OCR #DocumentParsing #DeepLearning #AI #NVIDIA