diff --git a/docs/deployment/docker.md b/docs/deployment/docker.md
index 9df829453..ae7cea436 100644
--- a/docs/deployment/docker.md
+++ b/docs/deployment/docker.md
@@ -84,10 +84,10 @@ DOCKER_BUILDKIT=1 docker build . \
     If you have not changed any C++ or CUDA kernel code, you can use precompiled wheels to significantly reduce Docker build time.
 
     *   **Enable the feature** by adding the build argument: `--build-arg VLLM_USE_PRECOMPILED="1"`.
-    *   **How it works**: By default, vLLM automatically finds the correct wheels from our [Nightly Builds](https://docs.vllm.ai/en/latest/contributing/ci/nightly_builds/) by using the merge-base commit with the upstream `main` branch.
+    *   **How it works**: By default, vLLM automatically finds the correct wheels from our [Nightly Builds](../contributing/ci/nightly_builds.md) by using the merge-base commit with the upstream `main` branch.
     *   **Override commit**: To use wheels from a specific commit, provide the `--build-arg VLLM_PRECOMPILED_WHEEL_COMMIT=<commit_hash>` argument.
 
-    For a detailed explanation, refer to the documentation on 'Set up using Python-only build (without compilation)' part in [Build wheel from source](https://docs.vllm.ai/en/latest/contributing/ci/nightly_builds.html#precompiled-wheels-usage), these args are similar.
+    For a detailed explanation, refer to the documentation on 'Set up using Python-only build (without compilation)' part in [Build wheel from source](../contributing/ci/nightly_builds.md#precompiled-wheels-usage), these args are similar.
 
 ## Building for Arm64/aarch64
 
diff --git a/docs/design/debug_vllm_compile.md b/docs/design/debug_vllm_compile.md
index 731e542a0..328df5816 100644
--- a/docs/design/debug_vllm_compile.md
+++ b/docs/design/debug_vllm_compile.md
@@ -33,7 +33,7 @@ goals while minimizing impact to performance and also helps us (vLLM) when you o
 For more details on the design, please see the following resources:
 
 - [Introduction to vLLM-torch.compile blogpost](https://blog.vllm.ai/2025/08/20/torch-compile.html)
-- [vLLM-torch.compile integration design](https://docs.vllm.ai/en/latest/design/torch_compile.html)
+- [vLLM-torch.compile integration design](./torch_compile.md)
 - [vLLM Office Hours #26](https://www.youtube.com/live/xLyxc7hxCJc?si=Xulo9pe53C6ywf0V&t=561)
 - [Talk at PyTorch Conference 2025](https://youtu.be/1wV1ESbGrVQ?si=s1GqymUfwiwOrDTg&t=725)
 
diff --git a/docs/features/custom_logitsprocs.md b/docs/features/custom_logitsprocs.md
index 5ddef9db1..232f4363e 100644
--- a/docs/features/custom_logitsprocs.md
+++ b/docs/features/custom_logitsprocs.md
@@ -180,7 +180,7 @@ The `DummyLogitsProcessor.update_state()` implementation maintains a "sparse" re
 
 ### Wrapping an Existing Request-Level Logits Processor
 
-Although the vLLM engine applies logits processors at batch granularity, some users may want to use vLLM with a "request-level" logits processor implementation - an implementation which operates on individual requests. This will be especially true if your logits processor was developed for vLLM version 0, which required it to be a `Callable` (as described [here](https://docs.vllm.ai/en/v0.10.1.1/api/vllm/logits_process.html)) conforming to the following type annotation:
+Although the vLLM engine applies logits processors at batch granularity, some users may want to use vLLM with a "request-level" logits processor implementation - an implementation which operates on individual requests. This will be especially true if your logits processor was developed for vLLM version 0, which required it to be a `Callable` (as described [here][vllm.logits_process]) conforming to the following type annotation:
 
 ``` python
 RequestLogitsProcessor = Union[
diff --git a/docs/getting_started/installation/cpu.md b/docs/getting_started/installation/cpu.md
index affb94593..d3e23c359 100644
--- a/docs/getting_started/installation/cpu.md
+++ b/docs/getting_started/installation/cpu.md
@@ -172,13 +172,13 @@ Note, it is recommended to manually reserve 1 CPU for vLLM front-end process whe
 
 ### What are supported models on CPU?
 
-For the full and up-to-date list of models validated on CPU platforms, please see the official documentation: [Supported Models on CPU](https://docs.vllm.ai/en/latest/models/hardware_supported_models/cpu)
+For the full and up-to-date list of models validated on CPU platforms, please see the official documentation: [Supported Models on CPU](../../models/hardware_supported_models/cpu.md)
 
 ### How to find benchmark configuration examples for supported CPU models?
 
-For any model listed under [Supported Models on CPU](https://docs.vllm.ai/en/latest/models/hardware_supported_models/cpu), optimized runtime configurations are provided in the vLLM Benchmark Suite’s CPU test cases, defined in [cpu test cases](https://github.com/vllm-project/vllm/blob/main/.buildkite/performance-benchmarks/tests/serving-tests-cpu.json)
-For details on how these optimized configurations are determined, see: [performance-benchmark-details](https://github.com/vllm-project/vllm/tree/main/.buildkite/performance-benchmarks#performance-benchmark-details).
-To benchmark the supported models using these optimized settings, follow the steps in [running vLLM Benchmark Suite manually](https://docs.vllm.ai/en/latest/contributing/benchmarks/#manually-trigger-the-benchmark) and run the Benchmark Suite on a CPU environment.  
+For any model listed under [Supported Models on CPU](../../models/hardware_supported_models/cpu.md), optimized runtime configurations are provided in the vLLM Benchmark Suite’s CPU test cases, defined in [cpu test cases](../../../.buildkite/performance-benchmarks/tests/serving-tests-cpu.json)
+For details on how these optimized configurations are determined, see: [performance-benchmark-details](../../../.buildkite/performance-benchmarks/README.md#performance-benchmark-details).
+To benchmark the supported models using these optimized settings, follow the steps in [running vLLM Benchmark Suite manually](../../benchmarking/dashboard.md#manually-trigger-the-benchmark) and run the Benchmark Suite on a CPU environment.  
 
 Below is an example command to benchmark all CPU-supported models using optimized configurations.
 
diff --git a/docs/mkdocs/hooks/url_schemes.py b/docs/mkdocs/hooks/url_schemes.py
index f36a64ed7..66fa25d2a 100644
--- a/docs/mkdocs/hooks/url_schemes.py
+++ b/docs/mkdocs/hooks/url_schemes.py
@@ -34,9 +34,10 @@ TITLE = r"(?P<title>[^\[\]<>]+?)"
 REPO = r"(?P<repo>.+?/.+?)"
 TYPE = r"(?P<type>issues|pull|projects)"
 NUMBER = r"(?P<number>\d+)"
+PATH = r"(?P<path>[^\s]+?)"
 FRAGMENT = r"(?P<fragment>#[^\s]+)?"
 URL = f"https://github.com/{REPO}/{TYPE}/{NUMBER}{FRAGMENT}"
-RELATIVE = r"(?!(https?|ftp)://|#)(?P<path>[^\s]+?)"
+RELATIVE = rf"(?!(https?|ftp)://|#){PATH}{FRAGMENT}"
 
 # Common titles to use for GitHub links when none is provided in the link.
 TITLES = {"issues": "Issue ", "pull": "Pull Request ", "projects": "Project "}
@@ -55,6 +56,7 @@ def on_page_markdown(
         title = match.group("title")
         path = match.group("path")
         path = (Path(page.file.abs_src_path).parent / path).resolve()
+        fragment = match.group("fragment") or ""
 
         # Check if the path exists and is outside the docs dir
         if not path.exists() or path.is_relative_to(DOC_DIR):
@@ -64,7 +66,7 @@ def on_page_markdown(
         slug = "tree/main" if path.is_dir() else "blob/main"
 
         path = path.relative_to(ROOT_DIR)
-        url = f"https://github.com/vllm-project/vllm/{slug}/{path}"
+        url = f"https://github.com/vllm-project/vllm/{slug}/{path}{fragment}"
         return f"[{gh_icon} {title}]({url})"
 
     def replace_github_link(match: re.Match) -> str:
@@ -88,8 +90,4 @@ def on_page_markdown(
 
     markdown = relative_link.sub(replace_relative_link, markdown)
     markdown = github_link.sub(replace_github_link, markdown)
-
-    if "interface" in str(page.file.abs_src_path):
-        print(markdown)
-
     return markdown