Eilev Blip2 Opt 2.7b

🚀 Model Card for EILEV BLIP-2-OPT-2.7B

This model is based on Salesforce/blip2-opt-2.7b and trained using EILeV, a novel method enabling in-context learning for vision-language models on videos without large naturalistic video datasets.

🚀 Quick Start

Please check out the official repository: https://github.com/yukw777/EILEV

✨ Features

• Optimized for Egocentric Video: EILEV BLIP-2-OPT-2.7B is a vision-language model (VLM) optimized for egocentric video, capable of performing in-context learning over videos and texts.
• Novel Training Method: Trained using EILeV, it can elicit in-context learning in VLMs for videos without requiring massive, naturalistic video datasets.

📚 Documentation

Model Details

Model Description

EILEV BLIP-2-OPT-2.7B is a VLM optimized for egocentric video. It can perform in-context learning over videos and texts. It was trained on Ego4D.

Model Sources

• Repository: https://github.com/yukw777/EILEV
• Paper: https://arxiv.org/abs/2311.17041
• Demo: https://2e09-141-212-106-177.ngrok-free.app

Bias, Risks, and Limitations

EILEV BLIP-2-OPT-2.7B 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.

EILEV BLIP-2-OPT-2.7B 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.

📄 License

This model is released under the MIT license.