Instructions to use MLLM-CL/MRLoRA_Router with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLLM-CL/MRLoRA_Router with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="MLLM-CL/MRLoRA_Router")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLLM-CL/MRLoRA_Router", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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- MR-LoRA
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pipeline_tag: visual-question-answering
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library_name: transformers
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---
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## MLLM-CL Benchmark Description
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MLLM-CL is a novel benchmark encompassing domain and ability continual learning, where the former focuses on independently and identically distributed (IID) evaluation across evolving mainstream domains,
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- MR-LoRA
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pipeline_tag: visual-question-answering
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library_name: transformers
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datasets:
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- MLLM-CL/MLLM-CL-ReplayData
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---
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## MLLM-CL Benchmark Description
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MLLM-CL is a novel benchmark encompassing domain and ability continual learning, where the former focuses on independently and identically distributed (IID) evaluation across evolving mainstream domains,
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