Visual Document Retrieval
Transformers
Safetensors
ColPali
multilingual
colvec1
feature-extraction
text
image
video
multimodal-embedding
vidore
colqwen3_5
multilingual-embedding
custom_code
Instructions to use webAI-Official/webAI-ColVec1-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use webAI-Official/webAI-ColVec1-9b with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("webAI-Official/webAI-ColVec1-9b", trust_remote_code=True, device_map="auto") - ColPali
How to use webAI-Official/webAI-ColVec1-9b with ColPali:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
added link to the new model
Browse files
README.md
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# webAI-Official/webAI-ColVec1-9b
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## ⚡ Summary
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**webAI-Official/webAI-ColVec1-9b** is a state-of-the-art [ColBERT](https://arxiv.org/abs/2407.01449)-style multimodal embedding model based on *[Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B)*. It maps text queries, visual documents (images, PDFs) into aligned multi-vector embeddings.
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# webAI-Official/webAI-ColVec1-9b
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**!!! A new version of this model is available here [webAI-ColVec1.1-8b](https://huggingface.co/webAI-Official/webAI-ColVec1.1-8b) !!!**
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## ⚡ Summary
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**webAI-Official/webAI-ColVec1-9b** is a state-of-the-art [ColBERT](https://arxiv.org/abs/2407.01449)-style multimodal embedding model based on *[Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B)*. It maps text queries, visual documents (images, PDFs) into aligned multi-vector embeddings.
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