sglangv0.5.2 & support Qwen3-Next-80B-A3B-Instruct

examples/runtime/engine/embedding.py

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+import sglang as sgl
+
+
+def main():
+    # Sample prompts.
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+    # Create an LLM.
+    llm = sgl.Engine(
+        model_path="Alibaba-NLP/gte-Qwen2-1.5B-instruct", is_embedding=True
+    )
+
+    outputs = llm.encode(prompts)
+    # Print the outputs (embedding vectors)
+    for prompt, output in zip(prompts, outputs):
+        print("===============================")
+        print(f"Prompt: {prompt}\nEmbedding vector: {output['embedding']}")
+
+
+# The __main__ condition is necessary here because we use "spawn" to create subprocesses
+# Spawn starts a fresh program every time, if there is no __main__, it will run into infinite loop to keep spawning processes from sgl.Engine
+if __name__ == "__main__":
+    main()