Apress.Explainable.AI.Recipes.pdf
8.2 MB
Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python (2023)
Author: Pradeepta Mishra
#XAI #Ai #DL #Python
#2023
@Machine_learn
Author: Pradeepta Mishra
#XAI #Ai #DL #Python
#2023
@Machine_learn
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Artificial Intelligence Class 10 (2023).pdf
20.8 MB
Book: ARTIFICIAL INTELLIGENCE (SUBJECT CODE 417) CLASS – 3
Authors: Orange Education Pvt Ltd
ISBN: Null
year: 2023
pages: 619
Tags:#AI
@Machine_learn
Authors: Orange Education Pvt Ltd
ISBN: Null
year: 2023
pages: 619
Tags:#AI
@Machine_learn
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Wiley_Artificial_Intelligence_Programming_with_Python_From_Zero.pdf
37.2 MB
Book: ArtificialIntelligence Programming
withPython F R O MZ E R OT OH E R O
Authors: Perry Xiao
ISBN: 978-1-119-82094-9 (ebk)
year: 2022
pages: 716
Tags:#AI #DL
@Machine_learn
withPython F R O MZ E R OT OH E R O
Authors: Perry Xiao
ISBN: 978-1-119-82094-9 (ebk)
year: 2022
pages: 716
Tags:#AI #DL
@Machine_learn
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Introduction to Generative AI.pdf
12.5 MB
Book: 📚Introduction to Generative AI
Authors: Numa Dhamani and Maggie Engler
ISBN: Null
year: 2023
pages: 318
Tags: #AI
@Machine_learn
Authors: Numa Dhamani and Maggie Engler
ISBN: Null
year: 2023
pages: 318
Tags: #AI
@Machine_learn
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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
@Machine_learn
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
@Machine_learn
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