[Doc][5/N] Move Community and API Reference to the bottom (vllm-proje…

…ct#11896) Signed-off-by: DarkLight1337 <[email protected]> Co-authored-by: Simon Mo <[email protected]> Signed-off-by: Fred Reiss <[email protected]>
frreiss · Jan 10, 2025 · f10d474 · f10d474
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diff --git a/README.md b/README.md
@@ -41,7 +41,7 @@ vLLM is a fast and easy-to-use library for LLM inference and serving.
 vLLM is fast with:
 
 - State-of-the-art serving throughput
-- Efficient management of attention key and value memory with **PagedAttention**
+- Efficient management of attention key and value memory with [**PagedAttention**](https://blog.vllm.ai/2023/06/20/vllm.html)
 - Continuous batching of incoming requests
 - Fast model execution with CUDA/HIP graph
 - Quantizations: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), INT4, INT8, and FP8.

diff --git a/docs/source/design/automatic_prefix_caching.md b/docs/source/design/automatic_prefix_caching.md
@@ -2,7 +2,7 @@
 
 # Automatic Prefix Caching
 
-The core idea of [PagedAttention](#design-paged-attention) is to partition the KV cache of each request into KV Blocks. Each block contains the attention keys and values for a fixed number of tokens. The PagedAttention algorithm allows these blocks to be stored in non-contiguous physical memory so that we can eliminate memory fragmentation by allocating the memory on demand.
+The core idea of [PagedAttention](https://blog.vllm.ai/2023/06/20/vllm.html) is to partition the KV cache of each request into KV Blocks. Each block contains the attention keys and values for a fixed number of tokens. The PagedAttention algorithm allows these blocks to be stored in non-contiguous physical memory so that we can eliminate memory fragmentation by allocating the memory on demand.
 
 To automatically cache the KV cache, we utilize the following key observation: Each KV block can be uniquely identified by the tokens within the block and the tokens in the prefix before the block.
 

diff --git a/docs/source/index.md b/docs/source/index.md
@@ -26,7 +26,7 @@ vLLM is a fast and easy-to-use library for LLM inference and serving.
 vLLM is fast with:
 
 - State-of-the-art serving throughput
-- Efficient management of attention key and value memory with **PagedAttention**
+- Efficient management of attention key and value memory with [**PagedAttention**](https://blog.vllm.ai/2023/06/20/vllm.html)
 - Continuous batching of incoming requests
 - Fast model execution with CUDA/HIP graph
 - Quantization: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), INT4, INT8, and FP8
@@ -54,6 +54,8 @@ For more information, check out the following:
 
 ## Documentation
 
+% How to start using vLLM?
+
 ```{toctree}
 :caption: Getting Started
 :maxdepth: 1
@@ -65,6 +67,8 @@ getting_started/troubleshooting
 getting_started/faq
 ```
 
+% What does vLLM support?
+
 ```{toctree}
 :caption: Models
 :maxdepth: 1
@@ -75,6 +79,8 @@ models/supported_models
 models/extensions/index
 ```
 
+% Additional capabilities
+
 ```{toctree}
 :caption: Features
 :maxdepth: 1
@@ -89,6 +95,8 @@ features/spec_decode
 features/compatibility_matrix
 ```
 
+% Details about running vLLM
+
 ```{toctree}
 :caption: Inference and Serving
 :maxdepth: 1
@@ -104,6 +112,8 @@ serving/usage_stats
 serving/integrations/index
 ```
 
+% Scaling up vLLM for production
+
 ```{toctree}
 :caption: Deployment
 :maxdepth: 1
@@ -115,6 +125,8 @@ deployment/frameworks/index
 deployment/integrations/index
 ```
 
+% Making the most out of vLLM
+
 ```{toctree}
 :caption: Performance
 :maxdepth: 1
@@ -123,28 +135,7 @@ performance/optimization
 performance/benchmarks
 ```
 
-% Community: User community resources
-
-```{toctree}
-:caption: Community
-:maxdepth: 1
-
-community/meetups
-community/sponsors
-```
-
-```{toctree}
-:caption: API Reference
-:maxdepth: 2
-
-api/offline_inference/index
-api/engine/index
-api/inference_params
-api/multimodal/index
-api/model/index
-```
-
-% Design Documents: Details about vLLM internals
+% Explanation of vLLM internals
 
 ```{toctree}
 :caption: Design Documents
@@ -159,7 +150,7 @@ design/automatic_prefix_caching
 design/multiprocessing
 ```
 
-% Developer Guide: How to contribute to the vLLM project
+% How to contribute to the vLLM project
 
 ```{toctree}
 :caption: Developer Guide
@@ -172,6 +163,29 @@ contributing/model/index
 contributing/vulnerability_management
 ```
 
+% Technical API specifications
+
+```{toctree}
+:caption: API Reference
+:maxdepth: 2
+
+api/offline_inference/index
+api/engine/index
+api/inference_params
+api/multimodal/index
+api/model/index
+```
+
+% Latest news and acknowledgements
+
+```{toctree}
+:caption: Community
+:maxdepth: 1
+
+community/meetups
+community/sponsors
+```
+
 # Indices and tables
 
 - {ref}`genindex`