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kpyu/video-blip-opt-2.7b-ego4d
video-blip-opt-2.7b-ego4d is a image-to-text model from kpyu. Use it when you need a caption or text from an image. It is set up for transformers. The card lists the license as mit.
VideoBLIP model, leveraging BLIP-2 with OPT-2.7b (a large language model with 2.7 billion parameters) as its LLM backbone.
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From the Hugging Face model README
VideoBLIP model, leveraging BLIP-2 with OPT-2.7b (a large language model with 2.7 billion parameters) as its LLM backbone.
VideoBLIP is an augmented BLIP-2 that can handle videos.
VideoBLIP-OPT uses off-the-shelf OPT as the language model. It inherits the same risks and limitations as mentioned in Meta's model card.
Like other large language models for which the diversity (or lack thereof) of training data induces downstream impact on the quality of our model, OPT-175B has limitations in terms of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern large language models.
VideoBLIP has not been tested in real world applications. It should not be directly deployed in any applications. Researchers should first carefully assess the safety and fairness of the model in relation to the specific context they’re being deployed within.
For code examples, please refer to the official repository.