Visual Document Retrieval
Safetensors
ColPali
sentence-transformers
colpali_engine
qwen3_5
multimodal-retrieval
late-interaction
colqwen
ColQwen3_5
vllm
vidore
mteb
qwen3.5
model-merge
MaxSim
multi-vector
Instructions to use vultr/VultronRetrieverCore-Qwen3.5-4.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ColPali
How to use vultr/VultronRetrieverCore-Qwen3.5-4.5B 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 vultr/VultronRetrieverCore-Qwen3.5-4.5B with sentence-transformers:
from sentence_transformers import MultiVectorEncoder model = MultiVectorEncoder("vultr/VultronRetrieverCore-Qwen3.5-4.5B") 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
Quantisation?
1
#2 opened about 1 month ago
by
dineshananthi