colqwen2-v0.1
Maintainer: vidore
94
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| Property | Value |
|---|---|
| Run this model | Run 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
colqwen2-v0.1 is a model based on a novel model architecture and training strategy called ColPali, which is designed to efficiently index documents from their visual features. It is an extension of the Qwen2-VL-2B model that generates ColBERT-style multi-vector representations of text and images. This version is the untrained base version to guarantee deterministic projection layer initialization.
The model was introduced in the paper ColPali: Efficient Document Retrieval with Vision Language Models and first released in this repository. It was developed by the team at vidore.
Model inputs and outputs
Inputs
- Images: The model takes dynamic image resolutions as input and does not resize them, maintaining their aspect ratio.
- Text: The model can take text inputs, such as queries, to be used alongside the image inputs.
Outputs
- The model outputs multi-vector representations of the text and images, which can be used for efficient document retrieval.
Capabilities
colqwen2-v0.1 is designed to efficiently index documents from their visual features. It can generate multi-vector representations of text and images using the ColBERT strategy, which enables improved performance compared to previous models like BiPali.
What can I use it for?
The colqwen2-v0.1 model can be used for a variety of document retrieval tasks, such as searching for relevant documents based on visual features. It could be particularly useful for applications that deal with large document repositories, such as academic paper search engines or enterprise knowledge management systems.
Things to try
One interesting aspect of colqwen2-v0.1 is its ability to handle dynamic image resolutions without resizing them. This can be useful for preserving the original aspect ratio and visual information of the documents being indexed. You could experiment with different image resolutions and observe how the model's performance changes.
Additionally, you could explore the model's performance on a variety of document types beyond just PDFs, such as scanned images or screenshots, to see how it generalizes to different visual input formats.
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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