fix[Docs]: link anchor is incorrect #20309 (#20315)

Signed-off-by: zxw <1020938856@qq.com>

docs/configuration/engine_args.md

@@ -6,7 +6,7 @@ title: Engine Arguments
 Engine arguments control the behavior of the vLLM engine.
 
 - For [offline inference][offline-inference], they are part of the arguments to [LLM][vllm.LLM] class.
-- For [online serving][openai-compatible-server], they are part of the arguments to `vllm serve`.
+- For [online serving][serving-openai-compatible-server], they are part of the arguments to `vllm serve`.
 
 You can look at [EngineArgs][vllm.engine.arg_utils.EngineArgs] and [AsyncEngineArgs][vllm.engine.arg_utils.AsyncEngineArgs] to see the available engine arguments.
 

docs/design/arch_overview.md

@@ -74,7 +74,7 @@ python -m vllm.entrypoints.openai.api_server --model <model>
 
 That code can be found in <gh-file:vllm/entrypoints/openai/api_server.py>.
 
-More details on the API server can be found in the [OpenAI-Compatible Server][openai-compatible-server] document.
+More details on the API server can be found in the [OpenAI-Compatible Server][serving-openai-compatible-server] document.
 
 ## LLM Engine
 

docs/features/structured_outputs.md

@@ -21,7 +21,7 @@ The following parameters are supported, which must be added as extra parameters:
 - `guided_grammar`: the output will follow the context free grammar.
 - `structural_tag`: Follow a JSON schema within a set of specified tags within the generated text.
 
-You can see the complete list of supported parameters on the [OpenAI-Compatible Server][openai-compatible-server] page.
+You can see the complete list of supported parameters on the [OpenAI-Compatible Server][serving-openai-compatible-server] page.
 
 Structured outputs are supported by default in the OpenAI-Compatible Server. You
 may choose to specify the backend to use by setting the

docs/getting_started/installation/intel_gaudi.md

@@ -110,7 +110,7 @@ docker run \
 ### Supported features
 
 - [Offline inference][offline-inference]
-- Online serving via [OpenAI-Compatible Server][openai-compatible-server]
+- Online serving via [OpenAI-Compatible Server][serving-openai-compatible-server]
 - HPU autodetection - no need to manually select device within vLLM
 - Paged KV cache with algorithms enabled for Intel Gaudi accelerators
 - Custom Intel Gaudi implementations of Paged Attention, KV cache ops,

docs/models/generative_models.md

@@ -134,7 +134,7 @@ outputs = llm.chat(conversation, chat_template=custom_template)
 
 ## Online Serving
 
-Our [OpenAI-Compatible Server][openai-compatible-server] provides endpoints that correspond to the offline APIs:
+Our [OpenAI-Compatible Server][serving-openai-compatible-server] provides endpoints that correspond to the offline APIs:
 
 - [Completions API][completions-api] is similar to `LLM.generate` but only accepts text.
 - [Chat API][chat-api]  is similar to `LLM.chat`, accepting both text and [multi-modal inputs][multimodal-inputs] for models with a chat template.

docs/models/pooling_models.md

@@ -113,7 +113,7 @@ A code example can be found here: <gh-file:examples/offline_inference/basic/scor
 
 ## Online Serving
 
-Our [OpenAI-Compatible Server][openai-compatible-server] provides endpoints that correspond to the offline APIs:
+Our [OpenAI-Compatible Server][serving-openai-compatible-server] provides endpoints that correspond to the offline APIs:
 
 - [Pooling API][pooling-api] is similar to `LLM.encode`, being applicable to all types of pooling models.
 - [Embeddings API][embeddings-api] is similar to `LLM.embed`, accepting both text and [multi-modal inputs][multimodal-inputs] for embedding models.

docs/models/supported_models.md

@@ -34,7 +34,7 @@ llm.apply_model(lambda model: print(type(model)))
 If it is `TransformersForCausalLM` then it means it's based on Transformers!
 
 !!! tip
-    You can force the use of `TransformersForCausalLM` by setting `model_impl="transformers"` for [offline-inference][offline-inference] or `--model-impl transformers` for the [openai-compatible-server][openai-compatible-server].
+    You can force the use of `TransformersForCausalLM` by setting `model_impl="transformers"` for [offline-inference][offline-inference] or `--model-impl transformers` for the [openai-compatible-server][serving-openai-compatible-server].
 
 !!! note
     vLLM may not fully optimise the Transformers implementation so you may see degraded performance if comparing a native model to a Transformers model in vLLM.
@@ -53,8 +53,8 @@ For a model to be compatible with the Transformers backend for vLLM it must:
 
 If the compatible model is:
 
-- on the Hugging Face Model Hub, simply set `trust_remote_code=True` for [offline-inference][offline-inference] or `--trust-remote-code` for the [openai-compatible-server][openai-compatible-server].
-- in a local directory, simply pass directory path to `model=<MODEL_DIR>` for [offline-inference][offline-inference] or `vllm serve <MODEL_DIR>` for the [openai-compatible-server][openai-compatible-server].
+- on the Hugging Face Model Hub, simply set `trust_remote_code=True` for [offline-inference][offline-inference] or `--trust-remote-code` for the [openai-compatible-server][serving-openai-compatible-server].
+- in a local directory, simply pass directory path to `model=<MODEL_DIR>` for [offline-inference][offline-inference] or `vllm serve <MODEL_DIR>` for the [openai-compatible-server][serving-openai-compatible-server].
 
 This means that, with the Transformers backend for vLLM, new models can be used before they are officially supported in Transformers or vLLM!
 

docs/serving/openai_compatible_server.md

 ---
 title: OpenAI-Compatible Server
 ---
-[](){ #openai-compatible-server }
+[](){ #serving-openai-compatible-server }
 
 vLLM provides an HTTP server that implements OpenAI's [Completions API](https://platform.openai.com/docs/api-reference/completions), [Chat API](https://platform.openai.com/docs/api-reference/chat), and more! This functionality lets you serve models and interact with them using an HTTP client.