Add Sentence Transformers usage section to the model card

README.md

@@ -6,6 +6,8 @@ pipeline_tag: visual-document-retrieval
 library_name: transformers
 tags:
 - transformers
+- sentence-transformers
+- multi-vector
 - multimodal_embedding
 - embedding
 - colpali
@@ -30,6 +32,47 @@ The model is trained using a multi-stage strategy that combines large-scale text
 
 ## Usage
 
+### Sentence Transformers
+
+This model can be used with [Sentence Transformers](https://www.sbert.net/) as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`:
+
+```bash
+pip install "sentence-transformers[image]>=6.0.0"
+```
+
+```python
+from sentence_transformers import MultiVectorEncoder
+
+model = MultiVectorEncoder(
+    "OpenSearch-AI/Ops-Colqwen3-4B",
+    trust_remote_code=True,
+    model_kwargs={"dtype": "bfloat16"},
+)
+
+queries = [
+    "What is the variable represented on the y-axis of the graph?",
+    "Total outlay is maximum in which year?",
+]
+images = [
+    "https://huggingface.co/datasets/sentence-transformers/example-documents/resolve/main/doc1.jpg",
+    "https://huggingface.co/datasets/sentence-transformers/example-documents/resolve/main/doc2.jpg",
+]
+
+query_embeddings = model.encode_query(queries)
+image_embeddings = model.encode_document(images)
+print(query_embeddings[0].shape, image_embeddings[0].shape)
+# torch.Size([25, 2560]) torch.Size([1254, 2560])
+
+# Diagonal should have higher scores
+scores = model.similarity(query_embeddings, image_embeddings)
+print(scores)
+# tensor([[17.4668, 12.9785],
+#         [ 7.0088, 15.9492]], device='cuda:0')
+```
+
+### Transformers
+
+
 **Requirements**
 ```
 pillow