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

examples/runtime/engine/save_remote_state.py

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+# SPDX-License-Identifier: Apache-2.0
+"""
+Saves each worker's model state dict directly to a checkpoint, which enables a
+fast load path for large tensor-parallel models where each worker only needs to
+read its own shard rather than the entire checkpoint.
+
+Example usage:
+
+python save_remote_state.py \
+    --model-path /path/to/load \
+    --tensor-parallel-size 8 \
+    --remote-model-save-url [protocol]://[host]:[port]/[model_name] \
+
+Then, the model can be loaded with
+
+llm = Engine(
+    model_path="[protocol]://[host]:[port]/[model_name]",
+    tensor_parallel_size=8,
+)
+"""
+import dataclasses
+from argparse import ArgumentParser
+from pathlib import Path
+
+from sglang import Engine, ServerArgs
+
+parser = ArgumentParser()
+ServerArgs.add_cli_args(parser)
+
+parser.add_argument(
+    "--remote-model-save-url",
+    required=True,
+    type=str,
+    help="remote address to store model weights",
+)
+parser.add_argument(
+    "--remote-draft-model-save-url",
+    default=None,
+    type=str,
+    help="remote address to store draft model weights",
+)
+
+
+def main(args):
+    engine_args = ServerArgs.from_cli_args(args)
+    model_path = engine_args.model_path
+    if not Path(model_path).is_dir():
+        raise ValueError("model path must be a local directory")
+    # Create LLM instance from arguments
+    llm = Engine(**dataclasses.asdict(engine_args))
+    llm.save_remote_model(
+        url=args.remote_model_save_url, draft_url=args.remote_draft_model_save_url
+    )
+    print("save remote (draft) model successfully")
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    main(args)