Update README.md

README.md

@@ -92,6 +92,59 @@ On LMEB, reranking models demonstrate a clear advantage, with even the 0.27B Nan
 ![lmeb_emb](./assets/lmeb_emb.jpg)
 
 # Usage
+## Using Sentence Transformers
+
+KaLM-Reranker-V1-Nano can be loaded as a modular Sentence Transformers
+`CrossEncoder`. This integration requires `sentence-transformers>=5.6,<6`,
+`transformers>=5.3,<6`, and the PyTorch backend. The repository contains custom
+modeling code, so load only trusted revisions and pass `trust_remote_code=True`.
+
+```bash
+pip install "sentence-transformers>=5.6,<6" "transformers>=5.3,<6"
+```
+
+```python
+import torch
+from sentence_transformers import CrossEncoder
+
+model = CrossEncoder(
+    "KaLM-Embedding/KaLM-Reranker-V1-Nano",
+    trust_remote_code=True,
+    device="cuda",
+    model_kwargs={"dtype": torch.bfloat16, "chunk_size": 4},
+)
+
+query = "What is the capital of China?"
+documents = [
+    "The capital of China is Beijing.",
+    "Gravity attracts bodies toward one another.",
+]
+pairs = [(query, document) for document in documents]
+
+# The default output is P(yes).
+scores = model.predict(pairs)
+rankings = model.rank(query, documents, return_documents=True)
+
+# CrossEncoder prompts are interpreted as KaLM task instructions.
+instruction = "Given a web search query, retrieve passages that answer the query."
+custom_scores = model.predict(pairs, prompt=instruction)
+custom_rankings = model.rank(query, documents, prompt=instruction)
+
+# Use Identity to return yes_logit - no_logit instead of P(yes).
+margins = model.predict(pairs, activation_fn=torch.nn.Identity())
+```
+
+Inputs must be ordered as `(query, document)`. By default, queries are
+truncated to 512 tokens and documents to 1024 tokens. `chunk_size=4` performs a
+mask-aware mean over each consecutive group of four encoder token states before
+passing the compressed encoder output to the decoder. Set `chunk_size=None` to
+disable compression, or change `model[0].chunk_size` after loading.
+
+For CPU inference, use `device="cpu"` and
+`model_kwargs={"dtype": torch.float32, "chunk_size": 4}`. Only the PyTorch
+inference backend is currently supported; training, ONNX, and OpenVINO are not
+included in this release.
+
 ## Using transformers
 ```python
 import argparse