[CI/Build] Add markdown linter (#11857)

Signed-off-by: Rafael Vasquez <rafvasq21@gmail.com>

docs/source/features/spec_decode.md

@@ -192,11 +192,11 @@ A few important things to consider when using the EAGLE based draft models:
 
 1. The EAGLE draft models available in the [HF repository for EAGLE models](https://huggingface.co/yuhuili) cannot be
    used directly with vLLM due to differences in the expected layer names and model definition.
-   To use these models with vLLM, use the [following script](https://gist.github.com/abhigoyal1997/1e7a4109ccb7704fbc67f625e86b2d6d) 
+   To use these models with vLLM, use the [following script](https://gist.github.com/abhigoyal1997/1e7a4109ccb7704fbc67f625e86b2d6d)
    to convert them. Note that this script does not modify the model's weights.
 
    In the above example, use the script to first convert
-   the [yuhuili/EAGLE-LLaMA3-Instruct-8B](https://huggingface.co/yuhuili/EAGLE-LLaMA3-Instruct-8B) model 
+   the [yuhuili/EAGLE-LLaMA3-Instruct-8B](https://huggingface.co/yuhuili/EAGLE-LLaMA3-Instruct-8B) model
    and then use the converted checkpoint as the draft model in vLLM.
 
 2. The EAGLE based draft models need to be run without tensor parallelism
@@ -207,7 +207,6 @@ A few important things to consider when using the EAGLE based draft models:
    reported in the reference implementation [here](https://github.com/SafeAILab/EAGLE). This issue is under
    investigation and tracked here: [https://github.com/vllm-project/vllm/issues/9565](https://github.com/vllm-project/vllm/issues/9565).
 
-
 A variety of EAGLE draft models are available on the Hugging Face hub:
 
 | Base Model                                                           | EAGLE on Hugging Face                     | # EAGLE Parameters |
@@ -224,7 +223,6 @@ A variety of EAGLE draft models are available on the Hugging Face hub:
 | Qwen2-7B-Instruct                                                    | yuhuili/EAGLE-Qwen2-7B-Instruct          | 0.26B              |
 | Qwen2-72B-Instruct                                                   | yuhuili/EAGLE-Qwen2-72B-Instruct         | 1.05B              |
 
-
 ## Lossless guarantees of Speculative Decoding
 
 In vLLM, speculative decoding aims to enhance inference efficiency while maintaining accuracy. This section addresses the lossless guarantees of
@@ -250,8 +248,6 @@ speculative decoding, breaking down the guarantees into three key areas:
    same request across runs. For more details, see the FAQ section
    titled *Can the output of a prompt vary across runs in vLLM?* in the [FAQs](#faq).
 
-**Conclusion**
-
 While vLLM strives to ensure losslessness in speculative decoding, variations in generated outputs with and without speculative decoding
 can occur due to following factors:
 
@@ -259,8 +255,6 @@ can occur due to following factors:
 - **Batch Size and Numerical Stability**: Changes in batch size may cause variations in logprobs and output probabilities, potentially
   due to non-deterministic behavior in batched operations or numerical instability.
 
-**Mitigation Strategies**
-
 For mitigation strategies, please refer to the FAQ entry *Can the output of a prompt vary across runs in vLLM?* in the [FAQs](#faq).
 
 ## Resources for vLLM contributors