blip3-phi3-mini-instruct-r-v1
Maintainer: Salesforce
143
🛠️
| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
blip3-phi3-mini-instruct-r-v1 is a large multimodal language model developed by Salesforce AI Research. It is part of the BLIP3 series of foundational multimodal models trained at scale on high-quality image caption datasets and interleaved image-text data. The pretrained version of this model, blip3-phi3-mini-base-r-v1, achieves state-of-the-art performance under 5 billion parameters and demonstrates strong in-context learning capabilities. The instruct-tuned version, blip3-phi3-mini-instruct-r-v1, also achieves state-of-the-art performance among open-source and closed-source vision-language models under 5 billion parameters. It supports flexible high-resolution image encoding with efficient visual token sampling.
Model inputs and outputs
Inputs
- Images: The model can accept high-resolution images as input.
- Text: The model can accept text prompts or questions as input.
Outputs
- Image captioning: The model can generate captions describing the contents of an image.
- Visual question answering: The model can answer questions about the contents of an image.
Capabilities
The blip3-phi3-mini-instruct-r-v1 model demonstrates strong performance on a wide range of vision-language tasks, including image-text retrieval, image captioning, and visual question answering. It can generate detailed and accurate captions for images and provide informative answers to visual questions.
What can I use it for?
The blip3-phi3-mini-instruct-r-v1 model can be used for a variety of applications that involve understanding and generating natural language in the context of visual information. Some potential use cases include:
- Image captioning: Automatically generating captions to describe the contents of images for applications such as photo organization, content moderation, and accessibility.
- Visual question answering: Enabling users to ask questions about the contents of images and receive informative answers, which could be useful for educational, assistive, or exploratory applications.
- Multimodal search and retrieval: Allowing users to search for and discover relevant images or documents based on natural language queries.
Things to try
One interesting aspect of the blip3-phi3-mini-instruct-r-v1 model is its ability to perform well on a range of tasks while being relatively lightweight (under 5 billion parameters). This makes it a potentially useful building block for developing more specialized or constrained vision-language applications, such as those targeting memory or latency-constrained environments. Developers could experiment with fine-tuning or adapting the model to their specific use cases to take advantage of its strong underlying capabilities.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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