[Doc] Move examples into categories (#11840)

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

docs/source/getting_started/installation/cpu-x86.md

@@ -95,7 +95,7 @@ $ VLLM_TARGET_DEVICE=cpu python setup.py install
 $ sudo apt-get install libtcmalloc-minimal4 # install TCMalloc library
 $ find / -name *libtcmalloc* # find the dynamic link library path
 $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:$LD_PRELOAD # prepend the library to LD_PRELOAD
-$ python examples/offline_inference.py # run vLLM
+$ python examples/offline_inference/offline_inference.py # run vLLM
 ```
 
 - When using the online serving, it is recommended to reserve 1-2 CPU cores for the serving framework to avoid CPU oversubscription. For example, on a platform with 32 physical CPU cores, reserving CPU 30 and 31 for the framework and using CPU 0-29 for OpenMP:
@@ -132,7 +132,7 @@ CPU NODE SOCKET CORE L1d:L1i:L2:L3 ONLINE    MAXMHZ   MINMHZ      MHZ
 
 # On this platform, it is recommend to only bind openMP threads on logical CPU cores 0-7 or 8-15
 $ export VLLM_CPU_OMP_THREADS_BIND=0-7
-$ python examples/offline_inference.py
+$ python examples/offline_inference/offline_inference.py
 ```
 
 - If using vLLM CPU backend on a multi-socket machine with NUMA, be aware to set CPU cores using `VLLM_CPU_OMP_THREADS_BIND` to avoid cross NUMA node memory access.

docs/source/getting_started/installation/xpu.md

@@ -71,4 +71,4 @@ $      --pipeline-parallel-size=2 \
 $      -tp=8
 ```
 
-By default, a ray instance will be launched automatically if no existing one is detected in system, with `num-gpus` equals to `parallel_config.world_size`. We recommend properly starting a ray cluster before execution, referring to the <gh-file:examples/run_cluster.sh> helper script.
+By default, a ray instance will be launched automatically if no existing one is detected in system, with `num-gpus` equals to `parallel_config.world_size`. We recommend properly starting a ray cluster before execution, referring to the <gh-file:examples/online_serving/run_cluster.sh> helper script.

docs/source/getting_started/quickstart.md

@@ -31,7 +31,7 @@ For non-CUDA platforms, please refer [here](#installation-index) for specific in
 
 ## Offline Batched Inference
 
-With vLLM installed, you can start generating texts for list of input prompts (i.e. offline batch inferencing). See the example script: <gh-file:examples/offline_inference.py>
+With vLLM installed, you can start generating texts for list of input prompts (i.e. offline batch inferencing). See the example script: <gh-file:examples/offline_inference/offline_inference.py>
 
 The first line of this example imports the classes {class}`~vllm.LLM` and {class}`~vllm.SamplingParams`:
 
@@ -133,7 +133,7 @@ completion = client.completions.create(model="Qwen/Qwen2.5-1.5B-Instruct",
 print("Completion result:", completion)
 ```
 
-A more detailed client example can be found here: <gh-file:examples/openai_completion_client.py>
+A more detailed client example can be found here: <gh-file:examples/online_serving/openai_completion_client.py>
 
 ### OpenAI Chat Completions API with vLLM
 

docs/source/getting_started/troubleshooting.md

@@ -24,7 +24,7 @@ To isolate the model downloading and loading issue, you can use the `--load-form
 
 ## Model is too large
 
-If the model is too large to fit in a single GPU, you might want to [consider tensor parallelism](#distributed-serving) to split the model across multiple GPUs. In that case, every process will read the whole model and split it into chunks, which makes the disk reading time even longer (proportional to the size of tensor parallelism). You can convert the model checkpoint to a sharded checkpoint using <gh-file:examples/save_sharded_state.py>. The conversion process might take some time, but later you can load the sharded checkpoint much faster. The model loading time should remain constant regardless of the size of tensor parallelism.
+If the model is too large to fit in a single GPU, you might want to [consider tensor parallelism](#distributed-serving) to split the model across multiple GPUs. In that case, every process will read the whole model and split it into chunks, which makes the disk reading time even longer (proportional to the size of tensor parallelism). You can convert the model checkpoint to a sharded checkpoint using <gh-file:examples/offline_inference/save_sharded_state.py>. The conversion process might take some time, but later you can load the sharded checkpoint much faster. The model loading time should remain constant regardless of the size of tensor parallelism.
 
 ## Enable more logging