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 1, 21:15 UTC
docs: fix tokenizer optimization typo (#44066)
Signed-off-by: chunyang.wen <chunyang.wen@gmail.com>
Chunyang Wen committed
Jun 5, 2026 at 17:12 UTC
efc347f1b2e635753ae8a594e9714109c200fcd3
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docs/configuration/optimization.md
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@@ -296,7 +296,7 @@ llm = LLM(model="Qwen/Qwen3-8B")
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The `fastokens` Python package (>= 0.2.0) must be installed; if it isn't,
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vLLM raises a clear `ImportError` at tokenizer load. The override applies to
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any `--tokenizer-mode` that ends up loading an HF fast tokenizer (`hf`,
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-`deepseek_v32`, `deepseek_v4`, `qwen_vl`, …). Modes that don't use the HF
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+`deepseek_v32`, `deepseek_v4`, `qwen_vl`, …). Models that don't use the HF
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fast tokenizer (`mistral`, `grok2`, `kimi_audio`) ignore the flag.
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Tokenizer-bound workloads — long shared prefixes, bursty short prompts,