resolve merge confilcts

docs/source/index.rst

@@ -31,7 +31,7 @@ vLLM is fast with:
 * Efficient management of attention key and value memory with **PagedAttention**
 * 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>`_, `SqueezeLLM <https://arxiv.org/abs/2306.07629>`_
+* Quantization: `GPTQ <https://arxiv.org/abs/2210.17323>`_, `AWQ <https://arxiv.org/abs/2306.00978>`_, `SqueezeLLM <https://arxiv.org/abs/2306.07629>`_, FP8 KV Cache
 * Optimized CUDA kernels
 
 vLLM is flexible and easy to use with:
@@ -42,6 +42,8 @@ vLLM is flexible and easy to use with:
 * Streaming outputs
 * OpenAI-compatible API server
 * Support NVIDIA GPUs and AMD GPUs
+* (Experimental) Prefix caching support
+* (Experimental) Multi-lora support
 
 For more information, check out the following:
 
@@ -85,4 +87,16 @@ Documentation
    :maxdepth: 1
    :caption: Quantization
 
-   quantization/auto_awq
\ No newline at end of file
+   quantization/auto_awq
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Developer Documentation
+
+   dev/engine/engine_index
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`

docs/source/models/supported_models.rst

@@ -68,6 +68,12 @@ Alongside each architecture, we include some popular models that use it.
   * - :code:`QWenLMHeadModel`
     - Qwen
     - :code:`Qwen/Qwen-7B`, :code:`Qwen/Qwen-7B-Chat`, etc.
+  * - :code:`Qwen2ForCausalLM`
+    - Qwen2
+    - :code:`Qwen/Qwen2-beta-7B`, :code:`Qwen/Qwen2-beta-7B-Chat`, etc.
+  * - :code:`StableLMEpochForCausalLM`
+    - StableLM
+    - :code:`stabilityai/stablelm-3b-4e1t/` , :code:`stabilityai/stablelm-base-alpha-7b-v2`, etc.
   * - :code:`YiForCausalLM`
     - Yi
     - :code:`01-ai/Yi-6B`, :code:`01-ai/Yi-34B`, etc.