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Integration for Ray LLM with load_format=runai_streamer #30154

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Integration for Ray LLM with load_format=runai_streamer #30154

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4 changes: 3 additions & 1 deletion vllm/engine/arg_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1394,7 +1394,9 @@ def create_engine_config(
)

model_config = self.create_model_config()
self.model = model_config.model
# if streaming from cloud storage, do not overwrite self.model with local dir
if not (is_cloud_storage(self.model) and self.load_format=="runai_streamer"):
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@gemini-code-assist gemini-code-assist bot Dec 5, 2025

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high

The condition to prevent overwriting self.model only checks for load_format=="runai_streamer". This is incomplete as it misses other runai_streamer variants like runai_streamer_sharded. If a user specifies load_format="runai_streamer_sharded" with a cloud storage model path, self.model will be incorrectly overwritten with a local directory, leading to the same failure this PR aims to fix. Using startswith("runai_streamer") would be more robust and cover all related formats.

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if not (is_cloud_storage(self.model) and self.load_format=="runai_streamer"):
if not (is_cloud_storage(self.model) and self.load_format.startswith("runai_streamer")):

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@jiangwu300 jiangwu300 Dec 5, 2025

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Are there other modes we need to consider as well like tokenizer? I'm not sure which load_formats actually skip tensors being downloaded and use streaming

self.model = model_config.model
self.tokenizer = model_config.tokenizer

self._check_feature_supported(model_config)
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