[Doc] Reorganize Supported Models by Type (#6167)

docs/source/models/supported_models.rst

@@ -7,6 +7,8 @@ vLLM supports a variety of generative Transformer models in `HuggingFace Transfo
 The following is the list of model architectures that are currently supported by vLLM.
 Alongside each architecture, we include some popular models that use it.
 
+Decoder-only Language Models
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 .. list-table::
   :widths: 25 25 50 5
   :header-rows: 1
@@ -95,14 +97,6 @@ Alongside each architecture, we include some popular models that use it.
     - LLaMA, Llama 2, Meta Llama 3, Vicuna, Alpaca, Yi
     - :code:`meta-llama/Meta-Llama-3-8B-Instruct`, :code:`meta-llama/Meta-Llama-3-70B-Instruct`, :code:`meta-llama/Llama-2-13b-hf`, :code:`meta-llama/Llama-2-70b-hf`, :code:`openlm-research/open_llama_13b`, :code:`lmsys/vicuna-13b-v1.3`, :code:`01-ai/Yi-6B`, :code:`01-ai/Yi-34B`, etc.
     - ✅︎
-  * - :code:`LlavaForConditionalGeneration`
-    - LLaVA-1.5
-    - :code:`llava-hf/llava-1.5-7b-hf`, :code:`llava-hf/llava-1.5-13b-hf`, etc.
-    -
-  * - :code:`LlavaNextForConditionalGeneration`
-    - LLaVA-NeXT
-    - :code:`llava-hf/llava-v1.6-mistral-7b-hf`, :code:`llava-hf/llava-v1.6-vicuna-7b-hf`, etc.
-    -
   * - :code:`MiniCPMForCausalLM`
     - MiniCPM
     - :code:`openbmb/MiniCPM-2B-sft-bf16`, :code:`openbmb/MiniCPM-2B-dpo-bf16`, etc.
@@ -143,10 +137,6 @@ Alongside each architecture, we include some popular models that use it.
     - Phi-3-Small
     - :code:`microsoft/Phi-3-small-8k-instruct`, :code:`microsoft/Phi-3-small-128k-instruct`, etc.
     -
-  * - :code:`Phi3VForCausalLM`
-    - Phi-3-Vision
-    - :code:`microsoft/Phi-3-vision-128k-instruct`, etc.
-    -
   * - :code:`QWenLMHeadModel`
     - Qwen
     - :code:`Qwen/Qwen-7B`, :code:`Qwen/Qwen-7B-Chat`, etc.
@@ -172,14 +162,40 @@ Alongside each architecture, we include some popular models that use it.
     - :code:`xverse/XVERSE-7B-Chat`, :code:`xverse/XVERSE-13B-Chat`, :code:`xverse/XVERSE-65B-Chat`, etc.
     -
 
-
-If your model uses one of the above model architectures, you can seamlessly run your model with vLLM.
-Otherwise, please refer to :ref:`Adding a New Model <adding_a_new_model>` for instructions on how to implement support for your model.
-Alternatively, you can raise an issue on our `GitHub <https://github.com/vllm-project/vllm/issues>`_ project.
-
 .. note::
     Currently, the ROCm version of vLLM supports Mistral and Mixtral only for context lengths up to 4096.
 
+.. _supported_vlms:
+
+Vision Language Models
+^^^^^^^^^^^^^^^^^^^^^^^
+
+.. list-table::
+  :widths: 25 25 50 5
+  :header-rows: 1
+
+  * - Architecture
+    - Models
+    - Example HuggingFace Models
+    - :ref:`LoRA <lora>`
+  * - :code:`LlavaForConditionalGeneration`
+    - LLaVA-1.5
+    - :code:`llava-hf/llava-1.5-7b-hf`, :code:`llava-hf/llava-1.5-13b-hf`, etc.
+    -
+  * - :code:`LlavaNextForConditionalGeneration`
+    - LLaVA-NeXT
+    - :code:`llava-hf/llava-v1.6-mistral-7b-hf`, :code:`llava-hf/llava-v1.6-vicuna-7b-hf`, etc.
+    -
+  * - :code:`Phi3VForCausalLM`
+    - Phi-3-Vision
+    - :code:`microsoft/Phi-3-vision-128k-instruct`, etc.
+    -
+
+If your model uses one of the above model architectures, you can seamlessly run your model with vLLM.
+Otherwise, please refer to :ref:`Adding a New Model <adding_a_new_model>` and :ref:`Adding a New Multimodal Model <adding_a_new_multimodal_model>` 
+for instructions on how to implement support for your model.
+Alternatively, you can raise an issue on our `GitHub <https://github.com/vllm-project/vllm/issues>`_ project.
+
 .. tip::
     The easiest way to check if your model is supported is to run the program below:
 
@@ -210,8 +226,9 @@ Alternatively, you can raise an issue on our `GitHub <https://github.com/vllm-pr
         output = llm.generate("Hello, my name is")
         print(output)
 
+
 Model Support Policy
----------------------
+=====================
 
 At vLLM, we are committed to facilitating the integration and support of third-party models within our ecosystem. Our approach is designed to balance the need for robustness and the practical limitations of supporting a wide range of models. Here’s how we manage third-party model support: