Make distinct code and console admonitions so readers are less likely to miss them (#20585)

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

docs/features/quantization/int4.md

@@ -53,7 +53,7 @@ When quantizing weights to INT4, you need sample data to estimate the weight upd
 It's best to use calibration data that closely matches your deployment data.
 For a general-purpose instruction-tuned model, you can use a dataset like `ultrachat`:
 
-??? Code
+??? code
 
     ```python
     from datasets import load_dataset
@@ -78,7 +78,7 @@ For a general-purpose instruction-tuned model, you can use a dataset like `ultra
 
 Now, apply the quantization algorithms:
 
-??? Code
+??? code
 
     ```python
     from llmcompressor.transformers import oneshot
@@ -141,7 +141,7 @@ lm_eval --model vllm \
 
 The following is an example of an expanded quantization recipe you can tune to your own use case:
 
-??? Code
+??? code
 
     ```python
     from compressed_tensors.quantization import (