Video Blip Opt 2.7b Ego4d

🚀 VideoBLIP, OPT-2.7b, fine-tuned on Ego4D

A VideoBLIP model that leverages BLIP-2 with OPT-2.7b as its LLM backbone, suitable for vision-related tasks such as image-to-text and video-to-text.

🚀 Quick Start

This is a VideoBLIP model that combines BLIP-2 and OPT-2.7b (a large language model with 2.7 billion parameters) as its LLM backbone.

✨ Features

• Multimodal Capability: Handles both images and videos, enabling tasks like image-to-text, video-to-text, image captioning, video captioning, and visual question answering.
• Powered by BLIP-2 and OPT-2.7b: Leverages the advanced architecture of BLIP-2 and the large-scale language understanding of OPT-2.7b.

📚 Documentation

Model description

VideoBLIP is an augmented BLIP-2 that can handle videos.

Bias, Risks, Limitations, and Ethical Considerations

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.

⚠️ Important Note

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.

How to use

For code examples, please refer to the official repository.

📄 License

This project is licensed under the MIT License.