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A high-throughput and memory-efficient inference and serving engine for LLMs

Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git

Synced Nov 4, 07:30 UTC

[Benchmark] Add --trust-remote-code flag to multi-turn benchmark (#41661)

Signed-off-by: Dao Le <daole@inferact.ai> Signed-off-by: Dao Le <Dao007forever@gmail.com> Co-authored-by: Claude <noreply@anthropic.com>

Dao007forever committed May 5, 2026 at 01:00 UTC 4845aee6b78c5543e036df1f9bbb8a9bfa4961f2
1 file changed +9 -1
benchmarks/multi_turn/benchmark_serving_multi_turn.py
+9 -1
@@ -1473,6 +1473,12 @@ async def main() -> None:
1473 "(for example: --warmup-percentages=0%%,50%%)",
1474 )
1475
1476 + parser.add_argument(
1477 + "--trust-remote-code",
1478 + action="store_true",
1479 + help="Trust remote code when loading the tokenizer.",
1480 + )
1481 +
1482 args = parser.parse_args()
1483
1484 logger.info(args)
@@ -1515,7 +1521,9 @@ async def main() -> None:
1521 np.random.seed(args.seed)
1522
1523 logger.info("Loading tokenizer")
1518 - tokenizer = AutoTokenizer.from_pretrained(args.model)
1524 + tokenizer = AutoTokenizer.from_pretrained(
1525 + args.model, trust_remote_code=args.trust_remote_code
1526 + )
1527
1528 await get_server_info(args.url)
1529