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[doc] update the wrong link (#16401)

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Signed-off-by: reidliu41 <reid201711@gmail.com>
Co-authored-by: reidliu41 <reid201711@gmail.com>
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Reid 2025-04-10 21:02:37 +08:00 committed by GitHub
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2
.github/ISSUE_TEMPLATE/600-new-model.yml vendored
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@ -9,7 +9,7 @@ body:
value: > value: >
#### Before submitting an issue, please make sure the issue hasn't been already addressed by searching through [the existing and past issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue+sort%3Acreated-desc+). #### Before submitting an issue, please make sure the issue hasn't been already addressed by searching through [the existing and past issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue+sort%3Acreated-desc+).
#### We also highly recommend you read https://docs.vllm.ai/en/latest/contributing/model/adding_model.html first to understand how to add a new model. #### We also highly recommend you read https://docs.vllm.ai/en/latest/contributing/model/index.html first to understand how to add a new model.
- type: textarea - type: textarea
attributes: attributes:
label: The model to consider. label: The model to consider.

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docs/source/models/extensions/tensorizer.md
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@ -9,7 +9,7 @@ shorter Pod startup times and CPU memory usage. Tensor encryption is also suppor
For more information on CoreWeave's Tensorizer, please refer to For more information on CoreWeave's Tensorizer, please refer to
[CoreWeave's Tensorizer documentation](https://github.com/coreweave/tensorizer). For more information on serializing a vLLM model, as well a general usage guide to using Tensorizer with vLLM, see [CoreWeave's Tensorizer documentation](https://github.com/coreweave/tensorizer). For more information on serializing a vLLM model, as well a general usage guide to using Tensorizer with vLLM, see
the [vLLM example script](https://docs.vllm.ai/en/stable/getting_started/examples/offline_inference/tensorize_vllm_model.html). the [vLLM example script](https://docs.vllm.ai/en/latest/getting_started/examples/tensorize_vllm_model.html).
:::{note} :::{note}
Note that to use this feature you will need to install `tensorizer` by running `pip install vllm[tensorizer]`. Note that to use this feature you will need to install `tensorizer` by running `pip install vllm[tensorizer]`.