LLaVA-Phi: Limitations and What You Can Expect in the Future
#llms #llavaphi #whatisllavaphi #llavaphilimitations #futureofllms #phi2 #llavaphiarchitecture #mideagroup
https://hackernoon.com/llava-phi-limitations-and-what-you-can-expect-in-the-future
#llms #llavaphi #whatisllavaphi #llavaphilimitations #futureofllms #phi2 #llavaphiarchitecture #mideagroup
https://hackernoon.com/llava-phi-limitations-and-what-you-can-expect-in-the-future
Hackernoon
LLaVA-Phi: Limitations and What You Can Expect in the Future
We introduce LLaVA-Phi, a vision language assistant developed using the compact language model Phi-2.
LLaVA-Phi: Qualitative Results - Take A Look At Its Remarkable Generelization Capabilities
#llms #llavaphi #llava15 #scienceqa #whatisllavaphi #llavaphiqualitativeresults #mideagroup #visionlanguageassistant
https://hackernoon.com/llava-phi-qualitative-results-take-a-look-at-its-remarkable-generelization-capabilities
#llms #llavaphi #llava15 #scienceqa #whatisllavaphi #llavaphiqualitativeresults #mideagroup #visionlanguageassistant
https://hackernoon.com/llava-phi-qualitative-results-take-a-look-at-its-remarkable-generelization-capabilities
Hackernoon
LLaVA-Phi: Qualitative Results - Take A Look At Its Remarkable Generelization Capabilities
We present several examples that demonstrate the remarkable generalization capabilities of LLaVA-Phi, comparing its outputs with those of the LLaVA-1.5-13B
LLaVA-Phi: How We Rigorously Evaluated It Using an Extensive Array of Academic Benchmarks
#llms #llavaphi #whatisllavaphi #llavaphiexperiments #mobilevlm #mmbench #instructblip #vizwizqa
https://hackernoon.com/llava-phi-how-we-rigorously-evaluated-it-using-an-extensive-array-of-academic-benchmarks
#llms #llavaphi #whatisllavaphi #llavaphiexperiments #mobilevlm #mmbench #instructblip #vizwizqa
https://hackernoon.com/llava-phi-how-we-rigorously-evaluated-it-using-an-extensive-array-of-academic-benchmarks
Hackernoon
LLaVA-Phi: How We Rigorously Evaluated It Using an Extensive Array of Academic Benchmarks
We rigorously evaluated LLaVA-Phi using an extensive array of academic benchmarks specifically designed for multi-modal models.