TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones — mentioned by T-A

Wed, 03 Jan 2024 20:56:35 GMT

From the paper's abstract [1]: It stands out by requiring merely a 24G GPU for training and an 8G GPU or CPU for inference. Built upon Phi-2, TinyGPT-V couples an effective language backbone with pre-trained vision modules from BLIP-2 or CLIP. TinyGPT-V's 2.8B parameters can undergo a unique quantisation process, suitable for local deployment and inference tasks on 8G various devices. [1] https://arxiv.org/abs/2312.16862

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TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones · HackerPapers discussions

TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones — mentioned by T-A