adapters and VL_adapter
The unified adapter library provides a general-purpose framework for parameter-efficient transfer learning across modalities, while the vision-language adapter is a specialized implementation of adapter techniques for a specific multimodal task domain, making them complementary tools where VL-Adapter demonstrates one application within the broader adapter-hub ecosystem.
About adapters
adapter-hub/adapters
A Unified Library for Parameter-Efficient and Modular Transfer Learning
This library helps machine learning engineers and researchers fine-tune large language models (LLMs) more efficiently. It takes a pre-trained Transformer model and task-specific datasets, then allows you to add and train small, specialized 'adapter' modules. The output is a highly optimized model for specific NLP tasks like text classification or question answering, without needing to retrain the entire large model.
About VL_adapter
ylsung/VL_adapter
PyTorch code for "VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks" (CVPR2022)
This project helps machine learning engineers or researchers efficiently adapt large pre-trained vision-and-language models for new image-text or video-text tasks. It takes existing models like VL-T5 or VL-BART along with your specific dataset (e.g., VQAv2, MSCOCO, TVQA), and outputs a specialized model that performs well on your task with significantly fewer parameters to train. This is ideal for those working on multimodal AI applications.
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