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Frameworks for managing and distributing tasks across multiple compute nodes.
Distinguishing note: Focuses on worker-controller patterns for model inference tasks.
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LLaVA is a multimodal large language model architecture designed to process and interpret both image and text inputs to generate natural language responses. It functions as a research-oriented platform for visual instruction tuning, providing a framework to align language models with human intent through training on diverse datasets of paired images and text queries. The system distinguishes itself through a specialized vision-language training pipeline that connects visual data to language models using projection layers and instruction-based fine-tuning. It supports distributed inference by
Coordinates a pool of independent model workers to distribute inference tasks across multiple hardware nodes.