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

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

docs/source/features/quantization/auto_awq.md

@@ -15,7 +15,7 @@ The main benefits are lower latency and memory usage.
 You can quantize your own models by installing AutoAWQ or picking one of the [400+ models on Huggingface](https://huggingface.co/models?sort=trending&search=awq).
 
 ```console
-$ pip install autoawq
+pip install autoawq
 ```
 
 After installing AutoAWQ, you are ready to quantize a model. Here is an example of how to quantize `mistralai/Mistral-7B-Instruct-v0.2`:
@@ -47,7 +47,7 @@ print(f'Model is quantized and saved at "{quant_path}"')
 To run an AWQ model with vLLM, you can use [TheBloke/Llama-2-7b-Chat-AWQ](https://huggingface.co/TheBloke/Llama-2-7b-Chat-AWQ) with the following command:
 
 ```console
-$ python examples/offline_inference/llm_engine_example.py --model TheBloke/Llama-2-7b-Chat-AWQ --quantization awq
+python examples/offline_inference/llm_engine_example.py --model TheBloke/Llama-2-7b-Chat-AWQ --quantization awq
 ```
 
 AWQ models are also supported directly through the LLM entrypoint:

docs/source/features/quantization/bnb.md

@@ -9,7 +9,7 @@ Compared to other quantization methods, BitsAndBytes eliminates the need for cal
 Below are the steps to utilize BitsAndBytes with vLLM.
 
 ```console
-$ pip install bitsandbytes>=0.45.0
+pip install bitsandbytes>=0.45.0
 ```
 
 vLLM reads the model's config file and supports both in-flight quantization and pre-quantized checkpoint.
@@ -17,7 +17,7 @@ vLLM reads the model's config file and supports both in-flight quantization and
 You can find bitsandbytes quantized models on <https://huggingface.co/models?other=bitsandbytes>.
 And usually, these repositories have a config.json file that includes a quantization_config section.
 
-## Read quantized checkpoint.
+## Read quantized checkpoint
 
 ```python
 from vllm import LLM
@@ -37,10 +37,11 @@ model_id = "huggyllama/llama-7b"
 llm = LLM(model=model_id, dtype=torch.bfloat16, trust_remote_code=True, \
 quantization="bitsandbytes", load_format="bitsandbytes")
 ```
+
 ## OpenAI Compatible Server
 
 Append the following to your 4bit model arguments:
 
-```
+```console
 --quantization bitsandbytes --load-format bitsandbytes
 ```

docs/source/features/quantization/fp8.md

@@ -41,7 +41,7 @@ Currently, we load the model at original precision before quantizing down to 8-b
 To produce performant FP8 quantized models with vLLM, you'll need to install the [llm-compressor](https://github.com/vllm-project/llm-compressor/) library:
 
 ```console
-$ pip install llmcompressor
+pip install llmcompressor
 ```
 
 ## Quantization Process
@@ -98,7 +98,7 @@ tokenizer.save_pretrained(SAVE_DIR)
 Install `vllm` and `lm-evaluation-harness`:
 
 ```console
-$ pip install vllm lm-eval==0.4.4
+pip install vllm lm-eval==0.4.4
 ```
 
 Load and run the model in `vllm`:

docs/source/features/quantization/fp8_e4m3_kvcache.md

@@ -17,7 +17,7 @@ unquantized model through a quantizer tool (e.g. AMD quantizer or NVIDIA AMMO).
 To install AMMO (AlgorithMic Model Optimization):
 
 ```console
-$ pip install --no-cache-dir --extra-index-url https://pypi.nvidia.com nvidia-ammo
+pip install --no-cache-dir --extra-index-url https://pypi.nvidia.com nvidia-ammo
 ```
 
 Studies have shown that FP8 E4M3 quantization typically only minimally degrades inference accuracy. The most recent silicon

docs/source/features/quantization/gguf.md

@@ -13,16 +13,16 @@ Currently, vllm only supports loading single-file GGUF models. If you have a mul
 To run a GGUF model with vLLM, you can download and use the local GGUF model from [TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF](https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF) with the following command:
 
 ```console
-$ wget https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf
-$ # We recommend using the tokenizer from base model to avoid long-time and buggy tokenizer conversion.
-$ vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf --tokenizer TinyLlama/TinyLlama-1.1B-Chat-v1.0
+wget https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf
+# We recommend using the tokenizer from base model to avoid long-time and buggy tokenizer conversion.
+vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf --tokenizer TinyLlama/TinyLlama-1.1B-Chat-v1.0
 ```
 
 You can also add `--tensor-parallel-size 2` to enable tensor parallelism inference with 2 GPUs:
 
 ```console
-$ # We recommend using the tokenizer from base model to avoid long-time and buggy tokenizer conversion.
-$ vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf --tokenizer TinyLlama/TinyLlama-1.1B-Chat-v1.0 --tensor-parallel-size 2
+# We recommend using the tokenizer from base model to avoid long-time and buggy tokenizer conversion.
+vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf --tokenizer TinyLlama/TinyLlama-1.1B-Chat-v1.0 --tensor-parallel-size 2
 ```
 
 ```{warning}

docs/source/features/quantization/int8.md

@@ -16,7 +16,7 @@ INT8 computation is supported on NVIDIA GPUs with compute capability > 7.5 (Turi
 To use INT8 quantization with vLLM, you'll need to install the [llm-compressor](https://github.com/vllm-project/llm-compressor/) library:
 
 ```console
-$ pip install llmcompressor
+pip install llmcompressor
 ```
 
 ## Quantization Process