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[Doc]: fix typos in various files (#28863)

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Signed-off-by: Didier Durand <durand.didier@gmail.com>
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Didier Durand
2025-11-18 05:32:14 +01:00
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parent bf9e1e8767
commit 083cf326dc
6 changed files with 7 additions and 7 deletions
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2
docs/features/disagg_prefill.md
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@@ -91,6 +91,6 @@ Disaggregated prefilling is highly related to infrastructure, so vLLM relies on
We recommend three ways of implementations:
- **Fully-customized connector**: Implement your own `Connector`, and call third-party libraries to send and receive KV caches, and many many more (like editing vLLM's model input to perform customized prefilling, etc). This approach gives you the most control, but at the risk of being incompatible with future vLLM versions.
- **Fully-customized connector**: Implement your own `Connector`, and call third-party libraries to send and receive KV caches, and many many more (like editing vLLM's model input to perform customized prefilling, etc.). This approach gives you the most control, but at the risk of being incompatible with future vLLM versions.
- **Database-like connector**: Implement your own `LookupBuffer` and support the `insert` and `drop_select` APIs just like SQL.
- **Distributed P2P connector**: Implement your own `Pipe` and support the `send_tensor` and `recv_tensor` APIs, just like `torch.distributed`.

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docs/features/lora.md
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@@ -4,7 +4,7 @@ This document shows you how to use [LoRA adapters](https://arxiv.org/abs/2106.09
LoRA adapters can be used with any vLLM model that implements [SupportsLoRA][vllm.model_executor.models.interfaces.SupportsLoRA].
Adapters can be efficiently served on a per request basis with minimal overhead. First we download the adapter(s) and save
Adapters can be efficiently served on a per-request basis with minimal overhead. First we download the adapter(s) and save
them locally with
```python