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---
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license: mit
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---
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# MAGMA -- Multimodal Augmentation of Generative Models through Adapter-based Finetuning
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Paper: https://arxiv.org/abs/2112.05253
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## Abstract
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Large-scale pretraining is fast becoming the norm in Vision-Language (VL) modeling. However, prevailing VL approaches are limited by the requirement for labeled data and the use of complex multi-step pretraining objectives. We present MAGMA - a simple method for augmenting generative language models with additional modalities using adapter-based finetuning. Building on Frozen, we train a series of VL models that autoregressively generate text from arbitrary combinations of visual and textual input. The pretraining is entirely end-to-end using a single language modeling objective, simplifying optimization compared to previous approaches. Importantly, the language model weights remain unchanged during training, allowing for transfer of encyclopedic knowledge and in-context learning abilities from language pretraining. MAGMA outperforms Frozen on open-ended generative tasks, achieving state of the art results on the OKVQA benchmark and competitive results on a range of other popular VL benchmarks, while pretraining on 0.2% of the number of samples used to train SimVLM.
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## Usage
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```py
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from magma import Magma
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from huggingface_hub import hf_hub_url, cached_download
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checkpoint_url = hf_hub_url(repo_id="osanseviero/magma", filename="model.pt")
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checkpoint_path = cached_download(checkpoint_url)
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model = Magma.from_checkpoint(
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config_path = "configs/MAGMA_v1.yml",
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checkpoint_path = checkpoint_path,
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device = 'cuda:0'
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)
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``` |
