[Tokenizer] Add an option to specify tokenizer#284
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WoosukKwon merged 13 commits intomainfrom Jun 28, 2023
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Jun 28, 2023
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This pr is very useful, my local test is always hard code tokenizer path |
zhuohan123
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LGTM! Thanks for the great work!
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| if "llama" in tokenizer_name.lower() and kwargs.get("use_fast", True): | ||
| logger.info( | ||
| "OpenLLaMA models do not support the fast tokenizer. " | ||
| "Using the slow tokenizer instead.") | ||
| elif config.model_type == "llama" and kwargs.get("use_fast", True): | ||
| # LLaMA fast tokenizer causes protobuf errors in some environments. | ||
| # However, we found that the below LLaMA fast tokenizer works well in | ||
| # most environments. | ||
| model_name = "hf-internal-testing/llama-tokenizer" | ||
| logger.info( | ||
| f"Using the LLaMA fast tokenizer in '{model_name}' to avoid " | ||
| "potential protobuf errors.") | ||
| elif config.model_type in _MODEL_TYPES_WITH_SLOW_TOKENIZER: | ||
| if kwargs.get("use_fast", False) == True: | ||
| raise ValueError( | ||
| f"Cannot use the fast tokenizer for {config.model_type} due to " | ||
| "bugs in the fast tokenizer.") | ||
| logger.info( | ||
| f"Using the slow tokenizer for {config.model_type} due to bugs in " | ||
| "the fast tokenizer. This could potentially lead to performance " | ||
| "degradation.") | ||
| kwargs["use_fast"] = False | ||
| return AutoTokenizer.from_pretrained(model_name, *args, **kwargs) | ||
| "For some LLaMA-based models, initializing the fast tokenizer may " | ||
| "take a long time. To eliminate the initialization time, consider " | ||
| f"using '{_FAST_LLAMA_TOKENIZER}' instead of the original " | ||
| "tokenizer.") |
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After this PR, do we need to manually specify llama to use the fast tokenizer for benchmarking?
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It depends. Actually, LLaMA fast tokenizers in lmsys/vicuna-7b-v1.3 or huggyllama/llama-7b work in my docker environment. So hf-internal-testing/llama-tokenizer is not needed when I use vLLM in my docker environment.
|
wow is cool，bro you are so cool |
|
THANKS VERY MUCH! |
michaelfeil
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Jul 1, 2023
hongxiayang
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yukavio
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SUMMARY: * only run 4 x a10 tests for python 3.10.12 NOTE: AWS looks to be having availability issues with these instances. i'm day to day with this repo being migrated to GCP, so in the meantime let's reduce demand. TEST PLAN: n/a Co-authored-by: andy-neuma <andy@neuralmagic.com>
billishyahao
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Dec 31, 2024
* fix cuda compilation * checkout tuned gemm from develop
dtrifiro
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Dec 11, 2025
Sync to upstream's [v0.11.0](https://github.com/vllm-project/vllm/releases/tag/v0.11.0) release + a cherry pick of vllm-project#24768 This PR targets CUDA but may also be sufficient for ROCM. Dockerfile updates: - general updates to match upstream's Dockerfile - nvcc, nvrtc and cuobjdump were addded for deepgemm JIT requirementes: neuralmagic/nm-vllm-ent@2a545c8 - missing paths were added for triton JIT: neuralmagic/nm-vllm-ent@b3027fc Tests: Branch in nm-cicd: https://github.com/neuralmagic/nm-cicd/tree/sync-v0.11-cuda accept-sync: https://github.com/neuralmagic/nm-cicd/actions/runs/18270550524 -- please ignore unit tests, they need to be updated to v1. Image tested: quay.io/vllm/automation-vllm:cuda-18270550524 Image validation: https://github.com/neuralmagic/nm-cicd/actions/runs/18271507914 Whisper runs: https://github.com/neuralmagic/nm-cicd/actions/runs/18281815955/job/52046560584 https://github.com/neuralmagic/nm-cicd/actions/runs/18281511979
mickg10
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Feb 11, 2026
(cherry picked from commit 0c8ef2a) Signed-off-by: Salar <skhorasgani@tenstorrent.com>
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Fixes #111 #246 #259 #270 #281
This PR adds
tokenizerto the input/cli arguments. If it isNone, vLLM uses the model name/path as the tokenizer name/path. In addition, from this PR, vLLM does not usehf-internal-testing/llama-tokenizeras the default tokenizer for llama models.