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[Doc]: fix typos in various files (#28567)

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Signed-off-by: Didier Durand <durand.didier@gmail.com>
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Didier Durand
2025-11-15 20:27:50 +01:00
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parent 07cadab27a
commit 2bb4435cb7
5 changed files with 6 additions and 6 deletions
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docs/design/moe_kernel_features.md
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@@ -68,7 +68,7 @@ Modular kernels are supported by the following `FusedMoEMethodBase` classes.
## Fused MoE Experts Kernels
The are a number of MoE experts kernel implementations for different quantization types and architectures. Most follow the general API of the base Triton [`fused_experts`][vllm.model_executor.layers.fused_moe.fused_moe.fused_experts] function. Many have modular kernel adatpers so they can be used with compatible all2all backends. This table lists each experts kernel and its particular properties.
The are a number of MoE experts kernel implementations for different quantization types and architectures. Most follow the general API of the base Triton [`fused_experts`][vllm.model_executor.layers.fused_moe.fused_moe.fused_experts] function. Many have modular kernel adapters so they can be used with compatible all2all backends. This table lists each experts kernel and its particular properties.
Each kernel must be provided with one of the supported input activation formats. Some flavors of kernels support both standard and batched formats through different entry points, e.g. `TritonExperts` and `BatchedTritonExperts`. Batched format kernels are currently only needed for matching with certain all2all backends, e.g. `pplx`, `DeepEPLLPrepareAndFinalize`.

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docs/features/quantization/quark.md
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@@ -298,7 +298,7 @@ There are two steps to generate and deploy a mixed precision model quantized wit
Firstly, the layerwise mixed-precision configuration for a given LLM model is searched and then quantized using AMD Quark. We will provide a detailed tutorial with Quark APIs later.
As examples, we provide some ready-to-use quantized mixed precision model to show the usage in vLLM and the accuracy benifits. They are:
As examples, we provide some ready-to-use quantized mixed precision model to show the usage in vLLM and the accuracy benefits. They are:
- amd/Llama-2-70b-chat-hf-WMXFP4FP8-AMXFP4FP8-AMP-KVFP8
- amd/Mixtral-8x7B-Instruct-v0.1-WMXFP4FP8-AMXFP4FP8-AMP-KVFP8