This content originally appeared on DEV Community and was authored by Jimmy Guerrero
In this talk we’ll delve into Multi-Modal LLMs, exploring the fusion of language and vision in cutting-edge models. We'll highlight the challenges in handling diverse data heterogeneity, its architecture design, strategies for efficient training, and optimization techniques to enhance both performance and inference speed. Through case studies and future outlooks, we’ll illustrate the importance of these optimizations in advancing applications across various domains.
About the Speaker
Neha Sharma has a rich background in digital products and technology services, having delivered successful projects for industry giants like IBM and launching innovative products for tech startups. As a Product Manager at Ori, Neha specializes in developing cutting-edge AI solutions by actively engaging on various AI-based use cases centered around latest/popular LLMs, demonstrating her commitment to staying at the forefront of AI technology.
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Recorded on July 3, 2024 at the AI, Machine Learning and Computer Vision Meetup.
This content originally appeared on DEV Community and was authored by Jimmy Guerrero
Jimmy Guerrero | Sciencx (2024-07-05T17:34:35+00:00) Computer Vision Meetup: Performance Optimisation for Multimodal LLMs. Retrieved from https://www.scien.cx/2024/07/05/computer-vision-meetup-performance-optimisation-for-multimodal-llms/
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