Fix some issues with current docs. (#6588)

docs/backend/openai_api_embeddings.ipynb

@@ -9,9 +9,7 @@
     "SGLang provides OpenAI-compatible APIs to enable a smooth transition from OpenAI services to self-hosted local models.\n",
     "A complete reference for the API is available in the [OpenAI API Reference](https://platform.openai.com/docs/guides/embeddings).\n",
     "\n",
-    "This tutorial covers the embedding APIs for embedding models, such as  \n",
+    "This tutorial covers the embedding APIs for embedding models. For a list of the supported models see the [corresponding overview page](https://docs.sglang.ai/supported_models/embedding_models.html)\n"
-    "- [intfloat/e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct)  \n",
-    "- [Alibaba-NLP/gte-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct)  \n"
    ]
   },
   {

docs/backend/openai_api_vision.ipynb

@@ -10,13 +10,7 @@
     "A complete reference for the API is available in the [OpenAI API Reference](https://platform.openai.com/docs/guides/vision).\n",
     "This tutorial covers the vision APIs for vision language models.\n",
     "\n",
-    "SGLang supports various vision language models such as Llama 3.2, LLaVA-OneVision, Qwen2.5-VL, Gemma3 and [more](https://docs.sglang.ai/supported_models/multimodal_language_models): \n",
+    "SGLang supports various vision language models such as Llama 3.2, LLaVA-OneVision, Qwen2.5-VL, Gemma3 and [more](https://docs.sglang.ai/supported_models/multimodal_language_models).\n",
-    "- [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct)  \n",
-    "- [lmms-lab/llava-onevision-qwen2-72b-ov-chat](https://huggingface.co/lmms-lab/llava-onevision-qwen2-72b-ov-chat)  \n",
-    "- [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)\n",
-    "- [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it)\n",
-    "- [openbmb/MiniCPM-V](https://huggingface.co/openbmb/MiniCPM-V)\n",
-    "- [deepseek-ai/deepseek-vl2](https://huggingface.co/deepseek-ai/deepseek-vl2)\n",
     "\n",
     "As an alternative to the OpenAI API, you can also use the [SGLang offline engine](https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/offline_batch_inference_vlm.py)."
    ]

docs/index.rst

@@ -28,6 +28,11 @@ The core features include:
    backend/openai_api_embeddings.ipynb
    backend/native_api.ipynb
    backend/offline_engine_api.ipynb
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Advanced Backend Configurations
+
    backend/server_arguments.md
    backend/sampling_params.md
    backend/hyperparameter_tuning.md
@@ -77,4 +82,4 @@ The core features include:
       references/general
       references/hardware
       references/advanced_deploy
-      references/performance_tuning
+      references/performance_analysis_and_optimization

docs/references/deepseek.md

@@ -3,7 +3,7 @@
 SGLang provides many optimizations specifically designed for the DeepSeek models, making it the inference engine recommended by the official [DeepSeek team](https://github.com/deepseek-ai/DeepSeek-V3/tree/main?tab=readme-ov-file#62-inference-with-sglang-recommended) from Day 0.
 
 This document outlines current optimizations for DeepSeek.
-Additionally, the SGLang team is actively developing enhancements following this [Roadmap](https://github.com/sgl-project/sglang/issues/2591).
+For an overview of the implemented features see the completed [Roadmap](https://github.com/sgl-project/sglang/issues/2591).
 
 ## Launch DeepSeek V3 with SGLang
 
@@ -221,6 +221,6 @@ Important Notes:
 
 ## FAQ
 
-1. **Question**: What should I do if model loading takes too long and NCCL timeout occurs?
+**Q: Model loading is taking too long, and I'm encountering an NCCL timeout. What should I do?**
 
-    **Answer**: You can try to add `--dist-timeout 3600` when launching the model, this allows for 1-hour timeout.
+A: If you're experiencing extended model loading times and an NCCL timeout, you can try increasing the timeout duration. Add the argument `--dist-timeout 3600` when launching your model. This will set the timeout to one hour, which often resolves the issue.

docs/references/performance_analysis_and_optimization.rst

@@ -0,0 +1,7 @@
+Performance Analysis & Optimization
+===================================
+.. toctree::
+   :maxdepth: 1
+
+   benchmark_and_profiling.md
+   accuracy_evaluation.md

docs/references/performance_tuning.rst

@@ -1,7 +0,0 @@
-Performance Tuning
-====================
-.. toctree::
-   :maxdepth: 1
-
-   benchmark_and_profiling.md
-   accuracy_evaluation.md

docs/start/install.md

@@ -23,8 +23,6 @@ uv pip install "sglang[all]>=0.4.6.post5"
   1. Use `export CUDA_HOME=/usr/local/cuda-<your-cuda-version>` to set the `CUDA_HOME` environment variable.
   2. Install FlashInfer first following [FlashInfer installation doc](https://docs.flashinfer.ai/installation.html), then install SGLang as described above.
 
-- If you encounter `ImportError; cannot import name 'is_valid_list_of_images' from 'transformers.models.llama.image_processing_llama'`, try to use the specified version of `transformers` in [pyproject.toml](https://github.com/sgl-project/sglang/blob/main/python/pyproject.toml). Currently, just running `pip install transformers==4.51.1`.
-
 ## Method 2: From source
 
 ```bash