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 14, 05:30 UTC
[Docs] Fix broken links to `docs/api/summary.md` (#23637)
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
Harry Mellor committed
Aug 26, 2025 at 14:00 UTC
164b2273c87ad72b2d3b1f2762367de42d6e946b
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docs/examples/README.md
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vLLM's examples are split into three categories:
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-- If you are using vLLM from within Python code, see [Offline Inference](./offline_inference/)
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-- If you are using vLLM from an HTTP application or client, see [Online Serving](./online_serving/)
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-- For examples of using some of vLLM's advanced features (e.g. LMCache or Tensorizer) which are not specific to either of the above use cases, see [Others](./others/)
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+- If you are using vLLM from within Python code, see [Offline Inference](./offline_inference)
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+- If you are using vLLM from an HTTP application or client, see [Online Serving](./online_serving)
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+- For examples of using some of vLLM's advanced features (e.g. LMCache or Tensorizer) which are not specific to either of the above use cases, see [Others](./others)
docs/models/generative_models.md
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## Offline Inference
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The [LLM][vllm.LLM] class provides various methods for offline inference.
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-See [configuration](../api/summary.md#configuration) for a list of options when initializing the model.
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+See [configuration](../api/README.md#configuration) for a list of options when initializing the model.
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### `LLM.generate`
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docs/models/pooling_models.md
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## Offline Inference
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The [LLM][vllm.LLM] class provides various methods for offline inference.
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-See [configuration](../api/summary.md#configuration) for a list of options when initializing the model.
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+See [configuration](../api/README.md#configuration) for a list of options when initializing the model.
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### `LLM.embed`
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