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

VideoBLIP model, leveraging [BLIP-2](https://arxiv.org/abs/2301.12597) with [OPT-2.7b](https://huggingface.co/facebook/opt-2.7b) (a large language model with 2.7 billion parameters) as its LLM backbone.

## 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.

> 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](https://github.com/yukw777/VideoBLIP).
{"language": "en", "license": "mit", "tags": ["vision", "image-to-text", "video-to-text", "image-captioning", "video-captioning", "visual-question-answering"], "pipeline_tag": "image-to-text"}
transformers pytorch blip-2 text2text-generation vision image-to-text video-to-text image-captioning video-captioning visual-question-answering en arxiv:2301.12597 license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #blip-2 #text2text-generation #vision #image-to-text #video-to-text #image-captioning #video-captioning #visual-question-answering #en #arxiv-2301.12597 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 


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

VideoBLIP model, leveraging BLIP-2 with OPT-2.7b (a large language model with 2.7 billion parameters) as its LLM backbone.

## 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.

> 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.
# VideoBLIP, OPT-2.7b, fine-tuned on Ego4D

VideoBLIP model, leveraging BLIP-2 with OPT-2.7b (a large language model with 2.7 billion parameters) as its LLM backbone. ## 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.

> 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.
TAGS
#transformers #pytorch #blip-2 #text2text-generation #vision #image-to-text #video-to-text #image-captioning #video-captioning #visual-question-answering #en #arxiv-2301.12597 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # VideoBLIP, OPT-2.7b, fine-tuned on Ego4D

VideoBLIP model, leveraging BLIP-2 with OPT-2.7b (a large language model with 2.7 billion parameters) as its LLM backbone. ## 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.

> 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.