[Docs] Enable some more markdown lint rules for the docs (#28731)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

.markdownlint.yaml

@@ -6,9 +6,6 @@ MD024:
 MD031:
   list_items: false
 MD033: false
-MD045: false
 MD046: false
-MD051: false
 MD052: false
-MD053: false
 MD059: false

docs/contributing/benchmarks.md

@@ -10,8 +10,6 @@ vLLM provides comprehensive benchmarking tools for performance testing and evalu
 - **[Parameter sweeps](#parameter-sweeps)**: Automate `vllm bench` runs for multiple configurations
 - **[Performance benchmarks](#performance-benchmarks)**: Automated CI benchmarks for development
 
-[Benchmark CLI]: #benchmark-cli
-
 ## Benchmark CLI
 
 This section guides you through running benchmark tests with the extensive

docs/contributing/ci/update_pytorch_version.md

@@ -95,7 +95,7 @@ when manually triggering a build on Buildkite. This branch accomplishes two thin
 to warm it up so that future builds are faster.
 
 <p align="center" width="100%">
-    <img width="60%" src="https://github.com/user-attachments/assets/a8ff0fcd-76e0-4e91-b72f-014e3fdb6b94">
+    <img width="60%" alt="Buildkite new build popup" src="https://github.com/user-attachments/assets/a8ff0fcd-76e0-4e91-b72f-014e3fdb6b94">
 </p>
 
 ## Update dependencies

docs/deployment/frameworks/chatbox.md

@@ -29,8 +29,8 @@ pip install vllm
     - API Path: `/chat/completions`
     - Model: `qwen/Qwen1.5-0.5B-Chat`
 
-    ![](../../assets/deployment/chatbox-settings.png)
+    ![Chatbox settings screen](../../assets/deployment/chatbox-settings.png)
 
 1. Go to `Just chat`, and start to chat:
 
-    ![](../../assets/deployment/chatbox-chat.png)
+    ![Chatbot chat screen](../../assets/deployment/chatbox-chat.png)

docs/deployment/frameworks/dify.md

@@ -46,12 +46,12 @@ And install [Docker](https://docs.docker.com/engine/install/) and [Docker Compos
     - **Model Name for API Endpoint**: `Qwen/Qwen1.5-7B-Chat`
     - **Completion Mode**: `Completion`
 
-    ![](../../assets/deployment/dify-settings.png)
+    ![Dify settings screen](../../assets/deployment/dify-settings.png)
 
 1. To create a test chatbot, go to `Studio → Chatbot → Create from Blank`, then select Chatbot as the type:
 
-    ![](../../assets/deployment/dify-create-chatbot.png)
+    ![Dify create chatbot screen](../../assets/deployment/dify-create-chatbot.png)
 
 1. Click the chatbot you just created to open the chat interface and start interacting with the model:
 
-    ![](../../assets/deployment/dify-chat.png)
+    ![Dify chat screen](../../assets/deployment/dify-chat.png)

docs/design/fused_moe_modular_kernel.md

@@ -19,9 +19,9 @@ The input activation format completely depends on the All2All Dispatch being use
 
 The FusedMoE operation is generally made of multiple operations, in both the Contiguous and Batched variants, as described in the diagrams below
 
-![](../assets/design/fused_moe_modular_kernel/fused_moe_non_batched.png "FusedMoE Non-Batched")
+![FusedMoE Non-Batched](../assets/design/fused_moe_modular_kernel/fused_moe_non_batched.png)
 
-![](../assets/design/fused_moe_modular_kernel/fused_moe_batched.png "FusedMoE Batched")
+![FusedMoE Batched](../assets/design/fused_moe_modular_kernel/fused_moe_batched.png)
 
 !!! note
     The main difference, in terms of operations, between the Batched and Non-Batched cases is the Permute / Unpermute operations. All other operations remain.
@@ -57,7 +57,7 @@ The `FusedMoEModularKernel` acts as a bridge between the `FusedMoEPermuteExperts
 The `FusedMoEPrepareAndFinalize` abstract class exposes `prepare`, `prepare_no_receive`  and `finalize` functions.
 The `prepare` function is responsible for input activation Quantization and All2All Dispatch. If implemented, The `prepare_no_receive` is like `prepare` except it does not wait to receive results from other workers.  Instead it returns a "receiver" callback that must be invoked to wait for the final results of worker. It is not required that this method is supported by all `FusedMoEPrepareAndFinalize` classes, but if it is available, it can be used to interleave work with the initial all to all communication, e.g. interleaving shared experts with fused experts.  The `finalize` function is responsible for invoking the All2All Combine. Additionally the `finalize` function may or may not do the TopK weight application and reduction (Please refer to the TopKWeightAndReduce section)
 
-![](../assets/design/fused_moe_modular_kernel/prepare_and_finalize_blocks.png "FusedMoEPrepareAndFinalize Blocks")
+![FusedMoEPrepareAndFinalize Blocks](../assets/design/fused_moe_modular_kernel/prepare_and_finalize_blocks.png)
 
 ### FusedMoEPermuteExpertsUnpermute
 
@@ -88,7 +88,7 @@ The core FusedMoE implementation performs a series of operations. It would be in
 It is sometimes efficient to perform TopK weight application and Reduction inside the `FusedMoEPermuteExpertsUnpermute::apply()`. Find an example [here](https://github.com/vllm-project/vllm/pull/20228). We have a `TopKWeightAndReduce` abstract class to facilitate such implementations. Please refer to the TopKWeightAndReduce section.
 `FusedMoEPermuteExpertsUnpermute::finalize_weight_and_reduce_impl()` returns the `TopKWeightAndReduce` object that the implementation wants the `FusedMoEPrepareAndFinalize::finalize()` to use.
 
-![](../assets/design/fused_moe_modular_kernel/fused_experts_blocks.png "FusedMoEPermuteExpertsUnpermute Blocks")
+![FusedMoEPermuteExpertsUnpermute Blocks](../assets/design/fused_moe_modular_kernel/fused_experts_blocks.png)
 
 ### FusedMoEModularKernel