init v0.23.0

Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>

examples/offline_embed.py

@@ -25,31 +25,33 @@ from vllm import LLM
 os.environ["VLLM_USE_MODELSCOPE"] = "True"
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 
+
 def get_detailed_instruct(task_description: str, query: str) -> str:
-    return f'Instruct: {task_description}\nQuery:{query}'
+    return f"Instruct: {task_description}\nQuery:{query}"
 
 
 def main():
     # Each query must come with a one-sentence instruction that describes the task
-    task = 'Given a web search query, retrieve relevant passages that answer the query'
+    task = "Given a web search query, retrieve relevant passages that answer the query"
 
     queries = [
-        get_detailed_instruct(task, 'What is the capital of China?'),
-        get_detailed_instruct(task, 'Explain gravity')
+        get_detailed_instruct(task, "What is the capital of China?"),
+        get_detailed_instruct(task, "Explain gravity"),
     ]
     # No need to add instruction for retrieval documents
     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."
+        "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="Qwen/Qwen3-Embedding-0.6B", task="embed")
+    model = LLM(model="Qwen/Qwen3-Embedding-0.6B", runner="pooling")
 
     outputs = model.embed(input_texts)
     embeddings = torch.tensor([o.outputs.embedding for o in outputs])
     # Calculate the similarity scores between the first two queries and the last two documents
-    scores = (embeddings[:2] @ embeddings[2:].T)
+    scores = embeddings[:2] @ embeddings[2:].T
     print(scores.tolist())
     # [[0.7620252966880798, 0.14078938961029053], [0.1358368694782257, 0.6013815999031067]]