Fix and Clean up chat-template requirement for VLM (#6114)

Signed-off-by: Xinyuan Tong <justinning0323@outlook.com>

docs/supported_models/embedding_models.md

@@ -3,7 +3,7 @@
 SGLang provides robust support for embedding models by integrating efficient serving mechanisms with its flexible programming interface. This integration allows for streamlined handling of embedding tasks, facilitating faster and more accurate retrieval and semantic search operations. SGLang's architecture enables better resource utilization and reduced latency in embedding model deployment.
 
 ```{important}
-They are executed with `--is-embedding` and some may require `--trust-remote-code` and/or `--chat-template`
+They are executed with `--is-embedding` and some may require `--trust-remote-code`
 ```
 
 ## Example launch Command
@@ -13,7 +13,6 @@ python3 -m sglang.launch_server \
   --model-path Alibaba-NLP/gme-Qwen2-VL-2B-Instruct \  # example HF/local path
   --is-embedding \
   --host 0.0.0.0 \
-  --chat-template gme-qwen2-vl \                     # set chat template
   --port 30000 \
 ```
 

docs/supported_models/vision_language_models.md

@@ -2,16 +2,11 @@
 
 These models accept multi-modal inputs (e.g., images and text) and generate text output. They augment language models with visual encoders and require a specific chat template for handling vision prompts.
 
-```{important}
-We need to specify `--chat-template` for VLMs because the chat template provided in HuggingFace tokenizer only supports text. If you do not specify a vision model’s `--chat-template`, the server uses HuggingFace’s default template, which only supports text and the images won’t be passed in.
-```
-
 ## Example launch Command
 
 ```shell
 python3 -m sglang.launch_server \
   --model-path meta-llama/Llama-3.2-11B-Vision-Instruct \  # example HF/local path
-  --chat-template llama_3_vision \                        # required chat template
   --host 0.0.0.0 \
   --port 30000 \
 ```