[Doc] Group examples into categories (#11782)

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

examples/fp8/README.md

@@ -56,7 +56,7 @@ python3 examples/fp8/extract_scales.py --quantized_model <QUANTIZED_MODEL_DIR> -
 ```
 ### 4. Load KV Cache Scaling Factors into VLLM.
 This script evaluates the inference throughput of language models using various backends such as vLLM. It measures the time taken to process a given number of prompts and generate sequences for each prompt. The recently generated KV cache scaling factors are now integrated into the benchmarking process and allow for KV cache scaling factors to be utilized for FP8.
-```python
+```
 # prerequisites:
 # -  LLaMa 2 kv_cache_scales.json file
 
@@ -90,7 +90,7 @@ optional arguments:
   --kv-cache-dtype {auto,fp8} Data type for kv cache storage. If "auto", will use model data type. FP8_E5M2 (without scaling) is only supported on cuda version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is instead supported ```for common inference criteria.
   --quantization-param-path QUANT_PARAM_JSON Path to the JSON file containing the KV cache scaling factors. This should generally be supplied, when KV cache dtype is FP8. Otherwise, KV cache scaling factors default to 1.0, which may cause accuracy issues. FP8_E5M2 (without scaling) is only supported on cuda version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is instead supported for common inference criteria.
 ```
-```
 Example:
+```console
 python3 benchmarks/benchmark_throughput.py --input-len <INPUT_LEN> --output-len <OUTPUT_LEN> -tp <TENSOR_PARALLEL_SIZE> --kv-cache-dtype fp8 --quantization-param-path <path/to/kv_cache_scales.json> --model <path-to-llama2>
-```python
+```

examples/prometheus_grafana/README.md

@@ -1,4 +1,4 @@
-# vLLM + Prometheus/Grafana 
+# Prometheus and Grafana 
 
 This is a simple example that shows you how to connect vLLM metric logging to the Prometheus/Grafana stack. For this example, we launch Prometheus and Grafana via Docker. You can checkout other methods through [Prometheus](https://prometheus.io/) and [Grafana](https://grafana.com/) websites. 
 
@@ -6,7 +6,7 @@ Install:
 - [`docker`](https://docs.docker.com/engine/install/)
 - [`docker compose`](https://docs.docker.com/compose/install/linux/#install-using-the-repository)
 
-### Launch
+## Launch
 
 Prometheus metric logging is enabled by default in the OpenAI-compatible server. Launch via the entrypoint:
 ```bash
@@ -35,11 +35,11 @@ python3 ../../benchmarks/benchmark_serving.py \
 
 Navigating to [`http://localhost:8000/metrics`](http://localhost:8000/metrics) will show the raw Prometheus metrics being exposed by vLLM.
 
-### Grafana Dashboard
+## Grafana Dashboard
 
 Navigate to [`http://localhost:3000`](http://localhost:3000). Log in with the default username (`admin`) and password (`admin`).
 
-#### Add Prometheus Data Source
+### Add Prometheus Data Source
 
 Navigate to [`http://localhost:3000/connections/datasources/new`](http://localhost:3000/connections/datasources/new) and select Prometheus. 
 
@@ -47,7 +47,7 @@ On Prometheus configuration page, we need to add the `Prometheus Server URL` in
 
 Click `Save & Test`. You should get a green check saying "Successfully queried the Prometheus API.".
 
-#### Import Dashboard 
+### Import Dashboard 
 
 Navigate to [`http://localhost:3000/dashboard/import`](http://localhost:3000/dashboard/import), upload `grafana.json`, and select the `prometheus` datasource. You should see a screen that looks like the following: