MPLUG-OWL has numerous possible use cases for a technical audience. For image captioning, users can provide an image as input and receive automatically generated captions that accurately describe the content. This can be useful in applications such as image search and indexing, where the model can assist in organizing and categorizing large collections of images. Additionally, MPLUG-OWL can be used in dialog systems, allowing users to have more interactive and natural conversations with AI assistants. The model can process textual prompts and previous dialog history to generate contextually-appropriate responses, enhancing the conversational experience. Furthermore, MPLUG-OWL can aid in creative writing by providing suggestions, generating plot ideas, or even acting as a co-writer. By analyzing prompts and images, the model can offer inspiration and generate coherent text that aligns with the user's creative vision. Overall, MPLUG-OWL holds promise for a wide range of products and practical uses, enabling more efficient and engaging interactions between humans and AI.
- Cost per run
- Avg run time
- Nvidia A100 (40GB) GPU
|Zephyr 7b Alpha||$?||3,019|
|Falcon 40b Instruct||$?||30,603|
You can use this area to play around with demo applications that incorporate the Mplug Owl model. These demos are maintained and hosted externally by third-party creators. If you see an error, message me on Twitter.
Currently, there are no demos available for this model.
Summary of this model and related resources.
|Model Name||Mplug Owl|
An instruction-tuned multimodal large language model that generates text ba...Read more »
|Model Link||View on Replicate|
|API Spec||View on Replicate|
|Github Link||View on Github|
|Paper Link||View on Arxiv|
How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?
How much does it cost to run this model? How long, on average, does it take to complete a run?
|Cost per Run||$0.0184|
|Prediction Hardware||Nvidia A100 (40GB) GPU|
|Average Completion Time||8 seconds|