Update README.md

README.md

@@ -67,7 +67,7 @@ KeyError: 'qwen2'
 ```
 
 ## Usage
-
+### sentence-transformers support
 Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
 ```
@@ -120,12 +120,42 @@ embeddings = model.encode(
 print(embeddings)
 ```
 
+### vllm support
+```
+pip install -U vllm==0.8.5
+```
+```python
+import torch
+import vllm
+from vllm import LLM
+def get_detailed_instruct(task_description: str, query: str) -> str:
+    return f'Instruct: {task_description}\nQuery:{query}'
+
+task = 'Given a query, retrieve documents that answer the query'
+queries = [
+    get_detailed_instruct(task, 'What is the capital of China?'),
+    get_detailed_instruct(task, 'Explain gravity')
+]
+documents = [
+    "The capital of China is Beijing.",
+    "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
+]
+input_texts = queries + documents
+
+model = LLM(model="{MODEL_NAME_OR_PATH}", task="embed", trust_remote_code=True, dtype="float16")
+
+outputs = model.embed(input_texts)
+embeddings = torch.tensor([o.outputs.embedding for o in outputs])
+scores = (embeddings[:2] @ embeddings[2:].T)
+print(scores.tolist())
+```
+
 ## Citation
 If you find this model useful, please consider giving a star and citation.
 ```
 @misc{zhao2025kalmembeddingv2,
       title={KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model}, 
-      author={Xinping Zhao and Xinshuo Hu and Zifei Shan and Shouzheng Huang and Yao Zhou and Zetian Sun and Zhenyu Liu and Dongfang Li and Xinyuan Wei and Qian Chen and Youcheng Pan and Yang Xiang and Meishan Zhang and Haofen Wang and Jun Yu and Baotian Hu and Min Zhang},
+      author={Xinping Zhao and Xinshuo Hu and Zifei Shan and Shouzheng Huang and Yao Zhou and Xin Zhang and Zetian Sun and Zhenyu Liu and Dongfang Li and Xinyuan Wei and Youcheng Pan and Yang Xiang and Meishan Zhang and Haofen Wang and Jun Yu and Baotian Hu and Min Zhang},
       year={2025},
       eprint={2506.20923},
       archivePrefix={arXiv},
@@ -134,13 +164,13 @@ If you find this model useful, please consider giving a star and citation.
 }
 
 @misc{hu2025kalmembedding,
-  title={KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model}, 
-  author={Xinshuo Hu and Zifei Shan and Xinping Zhao and Zetian Sun and Zhenyu Liu and Dongfang Li and Shaolin Ye and Xinyuan Wei and Qian Chen and Baotian Hu and Haofen Wang and Jun Yu and Min Zhang},
-  year={2025},
-  eprint={2501.01028},
-  archivePrefix={arXiv},
-  primaryClass={cs.CL},
-  url={https://arxiv.org/abs/2501.01028}, 
+      title={KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model}, 
+      author={Xinshuo Hu and Zifei Shan and Xinping Zhao and Zetian Sun and Zhenyu Liu and Dongfang Li and Shaolin Ye and Xinyuan Wei and Qian Chen and Baotian Hu and Haofen Wang and Jun Yu and Min Zhang},
+      year={2025},
+      eprint={2501.01028},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2501.01028}, 
 }
 ```