[Docs] Fix syntax highlighting of shell commands (#19870)

Signed-off-by: Lukas Geiger <lukas.geiger94@gmail.com>

docs/features/quantization/auto_awq.md

@@ -9,7 +9,7 @@ The main benefits are lower latency and memory usage.
 
 You can quantize your own models by installing AutoAWQ or picking one of the [6500+ models on Huggingface](https://huggingface.co/models?search=awq).
 
-```console
+```bash
 pip install autoawq
 ```
 
@@ -43,7 +43,7 @@ After installing AutoAWQ, you are ready to quantize a model. Please refer to the
 
 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
+```bash
 python examples/offline_inference/llm_engine_example.py \
     --model TheBloke/Llama-2-7b-Chat-AWQ \
     --quantization awq

docs/features/quantization/bitblas.md

@@ -12,7 +12,7 @@ vLLM now supports [BitBLAS](https://github.com/microsoft/BitBLAS) for more effic
 
 Below are the steps to utilize BitBLAS with vLLM.
 
-```console
+```bash
 pip install bitblas>=0.1.0
 ```
 

docs/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
+```bash
 pip install bitsandbytes>=0.45.3
 ```
 
@@ -54,6 +54,6 @@ llm = LLM(
 
 Append the following to your model arguments for 4bit inflight quantization:
 
-```console
+```bash
 --quantization bitsandbytes
 ```

docs/features/quantization/fp8.md

@@ -23,7 +23,7 @@ The FP8 types typically supported in hardware have two distinct representations,
 
 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
+```bash
 pip install llmcompressor
 ```
 
@@ -81,7 +81,7 @@ Since simple RTN does not require data for weight quantization and the activatio
 
 Install `vllm` and `lm-evaluation-harness` for evaluation:
 
-```console
+```bash
 pip install vllm lm-eval==0.4.4
 ```
 
@@ -99,9 +99,9 @@ Evaluate accuracy with `lm_eval` (for example on 250 samples of `gsm8k`):
 !!! note
     Quantized models can be sensitive to the presence of the `bos` token. `lm_eval` does not add a `bos` token by default, so make sure to include the `add_bos_token=True` argument when running your evaluations.
 
-```console
-$ MODEL=$PWD/Meta-Llama-3-8B-Instruct-FP8-Dynamic
-$ lm_eval \
+```bash
+MODEL=$PWD/Meta-Llama-3-8B-Instruct-FP8-Dynamic
+lm_eval \
   --model vllm \
   --model_args pretrained=$MODEL,add_bos_token=True \
   --tasks gsm8k  --num_fewshot 5 --batch_size auto --limit 250

docs/features/quantization/gguf.md

@@ -11,7 +11,7 @@ title: GGUF
 
 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
+```bash
 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 \
@@ -20,7 +20,7 @@ vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
 
 You can also add `--tensor-parallel-size 2` to enable tensor parallelism inference with 2 GPUs:
 
-```console
+```bash
 # 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 \
@@ -32,7 +32,7 @@ vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
 
 GGUF assumes that huggingface can convert the metadata to a config file. In case huggingface doesn't support your model you can manually create a config and pass it as hf-config-path
 
-```console
+```bash
 # If you model is not supported by huggingface you can manually provide a huggingface compatible config path
 vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
    --tokenizer TinyLlama/TinyLlama-1.1B-Chat-v1.0 \

docs/features/quantization/gptqmodel.md

@@ -21,7 +21,7 @@ for more details on this and other advanced features.
 
 You can quantize your own models by installing [GPTQModel](https://github.com/ModelCloud/GPTQModel) or picking one of the [5000+ models on Huggingface](https://huggingface.co/models?search=gptq).
 
-```console
+```bash
 pip install -U gptqmodel --no-build-isolation -v
 ```
 
@@ -60,7 +60,7 @@ Here is an example of how to quantize `meta-llama/Llama-3.2-1B-Instruct`:
 
 To run an GPTQModel quantized model with vLLM, you can use [DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2](https://huggingface.co/ModelCloud/DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2) with the following command:
 
-```console
+```bash
 python examples/offline_inference/llm_engine_example.py \
     --model ModelCloud/DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2
 ```

docs/features/quantization/int4.md

@@ -14,13 +14,13 @@ Please visit the HF collection of [quantized INT4 checkpoints of popular LLMs re
 
 To use INT4 quantization with vLLM, you'll need to install the [llm-compressor](https://github.com/vllm-project/llm-compressor/) library:
 
-```console
+```bash
 pip install llmcompressor
 ```
 
 Additionally, install `vllm` and `lm-evaluation-harness` for evaluation:
 
-```console
+```bash
 pip install vllm lm-eval==0.4.4
 ```
 
@@ -116,8 +116,8 @@ model = LLM("./Meta-Llama-3-8B-Instruct-W4A16-G128")
 
 To evaluate accuracy, you can use `lm_eval`:
 
-```console
-$ lm_eval --model vllm \
+```bash
+lm_eval --model vllm \
   --model_args pretrained="./Meta-Llama-3-8B-Instruct-W4A16-G128",add_bos_token=true \
   --tasks gsm8k \
   --num_fewshot 5 \

docs/features/quantization/int8.md

@@ -15,13 +15,13 @@ Please visit the HF collection of [quantized INT8 checkpoints of popular LLMs re
 
 To use INT8 quantization with vLLM, you'll need to install the [llm-compressor](https://github.com/vllm-project/llm-compressor/) library:
 
-```console
+```bash
 pip install llmcompressor
 ```
 
 Additionally, install `vllm` and `lm-evaluation-harness` for evaluation:
 
-```console
+```bash
 pip install vllm lm-eval==0.4.4
 ```
 
@@ -122,8 +122,8 @@ model = LLM("./Meta-Llama-3-8B-Instruct-W8A8-Dynamic-Per-Token")
 
 To evaluate accuracy, you can use `lm_eval`:
 
-```console
-$ lm_eval --model vllm \
+```bash
+lm_eval --model vllm \
   --model_args pretrained="./Meta-Llama-3-8B-Instruct-W8A8-Dynamic-Per-Token",add_bos_token=true \
   --tasks gsm8k \
   --num_fewshot 5 \

docs/features/quantization/modelopt.md

@@ -4,7 +4,7 @@ The [NVIDIA TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-O
 
 We recommend installing the library with:
 
-```console
+```bash
 pip install nvidia-modelopt
 ```
 

docs/features/quantization/quantized_kvcache.md

@@ -65,7 +65,7 @@ For optimal model quality when using FP8 KV Cache, we recommend using calibrated
 
 First, install the required dependencies:
 
-```console
+```bash
 pip install llmcompressor
 ```
 

docs/features/quantization/quark.md

@@ -13,7 +13,7 @@ AWQ, GPTQ, Rotation and SmoothQuant.
 
 Before quantizing models, you need to install Quark. The latest release of Quark can be installed with pip:
 
-```console
+```bash
 pip install amd-quark
 ```
 
@@ -22,13 +22,13 @@ for more installation details.
 
 Additionally, install `vllm` and `lm-evaluation-harness` for evaluation:
 
-```console
+```bash
 pip install vllm lm-eval==0.4.4
 ```
 
 ## Quantization Process
 
-After installing Quark, we will use an example to illustrate how to use Quark.  
+After installing Quark, we will use an example to illustrate how to use Quark.
 The Quark quantization process can be listed for 5 steps as below:
 
 1. Load the model
@@ -209,8 +209,8 @@ Now, you can load and run the Quark quantized model directly through the LLM ent
 
 Or, you can use `lm_eval` to evaluate accuracy:
 
-```console
-$ lm_eval --model vllm \
+```bash
+lm_eval --model vllm \
   --model_args pretrained=Llama-2-70b-chat-hf-w-fp8-a-fp8-kvcache-fp8-pertensor-autosmoothquant,kv_cache_dtype='fp8',quantization='quark' \
   --tasks gsm8k
 ```
@@ -222,7 +222,7 @@ to quantize large language models more conveniently. It supports quantizing mode
 of different quantization schemes and optimization algorithms. It can export the quantized model
 and run evaluation tasks on the fly. With the script, the example above can be:
 
-```console
+```bash
 python3 quantize_quark.py --model_dir meta-llama/Llama-2-70b-chat-hf \
                           --output_dir /path/to/output \
                           --quant_scheme w_fp8_a_fp8 \

docs/features/quantization/torchao.md

@@ -4,7 +4,7 @@ TorchAO is an architecture optimization library for PyTorch, it provides high pe
 
 We recommend installing the latest torchao nightly with
 
-```console
+```bash
 # Install the latest TorchAO nightly build
 # Choose the CUDA version that matches your system (cu126, cu128, etc.)
 pip install \