docs: Update the MD files (#6373)

Co-authored-by: Brayden Zhong <b8zhong@uwaterloo.ca>

docs/supported_models/multimodal_language_models.md

@@ -24,5 +24,6 @@ python3 -m sglang.launch_server \
 | **LLaVA** (v1.5 & v1.6)    | *e.g.* `liuhaotian/llava-v1.5-13b`         | `vicuna_v1.1`    | Open vision-chat models that add an image encoder to LLaMA/Vicuna (e.g. LLaMA2 13B) for following multimodal instruction prompts.                                                                               |
 | **LLaVA-NeXT** (8B, 72B)   | `lmms-lab/llava-next-72b`                  | `chatml-llava`   | Improved LLaVA models (with an 8B Llama3 version and a 72B version) offering enhanced visual instruction-following and accuracy on multimodal benchmarks.                                                       |
 | **LLaVA-OneVision**        | `lmms-lab/llava-onevision-qwen2-7b-ov`     | `chatml-llava`   | Enhanced LLaVA variant integrating Qwen as the backbone; supports multiple images (and even video frames) as inputs via an OpenAI Vision API-compatible format.                                                 |
-| **Gemma 3 (Multimodal)**   | `google/gemma-3-4b-it`                     | `gemma-it`       | Gemma 3’s larger models (4B, 12B, 27B) accept images (each image encoded as 256 tokens) alongside text in a combined 128K-token context.                                                                        |
+| **Gemma 3 (Multimodal)**   | `google/gemma-3-4b-it`                     | `gemma-it`       | Gemma 3's larger models (4B, 12B, 27B) accept images (each image encoded as 256 tokens) alongside text in a combined 128K-token context.                                                                        |
 | **Kimi-VL** (A3B)          | `moonshotai/Kimi-VL-A3B-Instruct`          | `kimi-vl`        | Kimi-VL is a multimodal model that can understand and generate text from images.                                                                                                                                |
+| **Mistral-Small-3.1-24B**  | `mistralai/Mistral-Small-3.1-24B-Instruct-2503` | `mistral`   | Mistral 3.1 is a multimodal model that can generate text from text or images input. It also supports tool calling and structured output. |

docs/supported_models/vision_language_models.md

-# Vision Language Models
-
-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.
-
-## Example launch Command
-
-```shell
-python3 -m sglang.launch_server \
-  --model-path meta-llama/Llama-3.2-11B-Vision-Instruct \  # example HF/local path
-  --host 0.0.0.0 \
-  --port 30000 \
-```
-
-## Supporting Matrixs
-
-| Model Family (Variants)        | Example HuggingFace Identifier                     | Chat Template        | Description                                                                            |
-|--------------------------------|--------------------------------------------------|----------------------|----------------------------------------------------------------------------------------|
-| **Qwen-VL** (Qwen2 series)     | `Qwen/Qwen2.5-VL-7B-Instruct`                    | `qwen2-vl`           | Alibaba’s vision-language extension of Qwen; for example, Qwen2.5-VL (7B and larger variants) can analyze and converse about image content. |
-| **DeepSeek-VL2**               | `deepseek-ai/deepseek-vl2`                       | `deepseek-vl2`       | Vision-language variant of DeepSeek (with a dedicated image processor), enabling advanced multimodal reasoning on image and text inputs. |
-| **Janus-Pro** (1B, 7B)      | `deepseek-ai/Janus-Pro-7B`                     | `janus-pro`       | DeepSeek’s open-source multimodal model capable of both image understanding and generation. Janus-Pro employs a decoupled architecture for separate visual encoding paths, enhancing performance in both tasks. |
-| **MiniCPM-V / MiniCPM-o**      | `openbmb/MiniCPM-V-2_6`                          | `minicpmv`           | MiniCPM-V (2.6, ~8B) supports image inputs, and MiniCPM-o adds audio/video; these multimodal LLMs are optimized for end-side deployment on mobile/edge devices. |
-| **Llama 3.2 Vision** (11B)     | `meta-llama/Llama-3.2-11B-Vision-Instruct`        | `llama_3_vision`     | Vision-enabled variant of Llama 3 (11B) that accepts image inputs for visual question answering and other multimodal tasks. |
-| **Pixtral** (12B, 124B)              | `mistral-community/pixtral-12b`                           | `mistral`     | Pixtral is a vision-language model from Mistral AI that can process both text and images. |
-| **LLaVA** (v1.5 & v1.6)        | *e.g.* `liuhaotian/llava-v1.5-13b`               | `vicuna_v1.1`        | Open vision-chat models that add an image encoder to LLaMA/Vicuna (e.g. LLaMA2 13B) for following multimodal instruction prompts. |
-| **LLaVA-NeXT** (8B, 72B)       | `lmms-lab/llava-next-72b`                        | `chatml-llava`       | Improved LLaVA models (with an 8B Llama3 version and a 72B version) offering enhanced visual instruction-following and accuracy on multimodal benchmarks. |
-| **LLaVA-OneVision**            | `lmms-lab/llava-onevision-qwen2-7b-ov`           | `chatml-llava`       | Enhanced LLaVA variant integrating Qwen as the backbone; supports multiple images (and even video frames) as inputs via an OpenAI Vision API-compatible format. |
-| **Gemma 3 (Multimodal)**       | `google/gemma-3-4b-it`                           | `gemma-it`           | Gemma 3’s larger models (4B, 12B, 27B) accept images (each image encoded as 256 tokens) alongside text in a combined 128K-token context. |
-| **Kimi-VL** (A3B)              | `moonshotai/Kimi-VL-A3B-Instruct`                | `kimi-vl`            | Kimi-VL is a multimodal model that can understand and generate text from images. |
-| **Mistral-Small-3.1-24B**      | `mistralai/Mistral-Small-3.1-24B-Instruct-2503`    | `mistral`            | Mistral 3.1 is a multimodal model that can generate text from text or images input. It also supports tool calling and structured output. |