TinyGPT-V

Efficient multimodal large language model

CommonProductProductivityLanguage ModelMultimodal

TinyGPT-V is an efficient multimodal large language model implemented using a smaller backbone network. It possesses strong language understanding and generation capabilities, making it suitable for a variety of natural language processing tasks. TinyGPT-V is based on Phi-2 as its pre-training model, boasting excellent performance and efficiency.

Efficient multimodal large language model
Strong language understanding and generation capabilities
Suitable for various natural language processing tasks
Implemented using a smaller backbone network
Pre-trained based on Phi-2

Suitable for natural language processing tasks
such as text generation
machine translation
and conversational systems.

Using TinyGPT-V for text generation tasks
Applying TinyGPT-V to machine translation tasks
Building intelligent conversational systems using TinyGPT-V

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TinyGPT-V Visit Over Time

Monthly Visits

490881889

Bounce Rate

37.92%

Page per Visit

5.6

Visit Duration

00:06:18

TinyGPT-V Visit Trend

TinyGPT-V Visit Geography

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TinyGPT-V

TinyGPT-V Visit Over Time

TinyGPT-V Visit Trend

TinyGPT-V Visit Geography

TinyGPT-V Traffic Sources

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CAG — An enhancement method for language models that improves generation efficiency through preloading knowledge caches without the need for real-time retrieval.

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Llama3-ChatQA-1.5-8B — A high-level conversational question-answering and generation model built on NVIDIA technology.

MiniMax-01 — A powerful language model with a total of 456 billion parameters, capable of processing context lengths of up to 4 million tokens.

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