ColQwen2.5 3b: Visual Retriever based on Qwen2.5 VL 3B Instruct with ColBERT strategy ColQwen is a model based on a novel model architecture and training strategy based on Vision Language Models (VLMs) to efficiently index documents from their visual features. It is a Qwen2.5 VL 3B extension that generates ColBERT style multi vector representations of text and images. It was introduced in the paper ColPali: Efficient Document Retrieval with Vision Language Models and first released in this repository This version is the untrained base version to guarantee deterministic projection layer initialization. Usage [!WARNING] This version should not be used: it is solely the base version useful for deterministic LoRA initialization. Citation If you use any datasets or models from this organization in your research, please cite the original dataset as follows: Developed by: T Systems International
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