Instructions to use vidore/colpali with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ColPali
How to use vidore/colpali 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
- sentence-transformers
How to use vidore/colpali with sentence-transformers:
from sentence_transformers import MultiVectorEncoder model = MultiVectorEncoder("vidore/colpali") queries = ["Which planet is known as the Red Planet?"] documents = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) - Notebooks
- Google Colab
- Kaggle
how improve performance
#8
by marcoaleixo - opened
while trying on colab I'm far from having 0.39s per image.
I'm basically running your example ( thanks ) https://github.com/illuin-tech/colpali/blob/main/scripts/infer/run_inference_with_python.py#L14 but using a T4.
I'm seeing almost 15/20s per page, any clues?
and I noticed that you mentioned that you used a nvidia L4, I will try with that one.
thanks