xgen-mm-phi3-mini-instruct-r-v1

Maintainer: Salesforce

Total Score

134

Last updated 5/28/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

xgen-mm-phi3-mini-instruct-r-v1 is a series of foundational Large Multimodal Models (LMMs) developed by Salesforce AI Research. This model advances upon the successful designs of the BLIP series, incorporating fundamental enhancements that ensure a more robust and superior foundation. The pretrained foundation model, xgen-mm-phi3-mini-base-r-v1, achieves state-of-the-art performance under 5 billion parameters and demonstrates strong in-context learning capabilities. The instruct fine-tuned model, xgen-mm-phi3-mini-instruct-r-v1, also achieves state-of-the-art performance among open-source and closed-source Vision-Language Models (VLMs) under 5 billion parameters.

Model inputs and outputs

The xgen-mm-phi3-mini-instruct-r-v1 model is designed for image-to-text tasks. It takes in images and generates corresponding textual descriptions.

Inputs

  • Images: The model can accept high-resolution images as input.

Outputs

  • Textual Descriptions: The model generates textual descriptions that caption the input images.

Capabilities

The xgen-mm-phi3-mini-instruct-r-v1 model demonstrates strong performance in image captioning tasks, outperforming other models of similar size on benchmarks like COCO, NoCaps, and TextCaps. It also shows robust capabilities in open-ended visual question answering on datasets like OKVQA and TextVQA.

What can I use it for?

The xgen-mm-phi3-mini-instruct-r-v1 model can be used in a variety of applications that involve generating textual descriptions from images, such as:

  • Image captioning: Automatically generate captions for images to aid in indexing, search, and accessibility.
  • Visual question answering: Develop applications that can answer questions about the content of images.
  • Image-based task automation: Build systems that can understand image-based instructions and perform related tasks.

The model's state-of-the-art performance and efficiency make it a compelling choice for Salesforce's customers looking to incorporate advanced computer vision and language capabilities into their products and services.

Things to try

One interesting aspect of the xgen-mm-phi3-mini-instruct-r-v1 model is its support for flexible high-resolution image encoding with efficient visual token sampling. This allows the model to generate high-quality, detailed captions for a wide range of image sizes and resolutions. Developers could experiment with feeding the model images of different sizes and complexities to see how it handles varied input and generates descriptive outputs.

Additionally, the model's strong in-context learning capabilities suggest it may be well-suited for few-shot or zero-shot learning tasks, where the model can adapt to new scenarios with limited training data. Trying prompts that require the model to follow instructions or reason about unfamiliar concepts could be a fruitful area of exploration.



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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