[Docs] Update docs for Qwen3 and Qwen3MoE (#5836)

docs/supported_models/generative_models.md

@@ -17,7 +17,7 @@ python3 -m sglang.launch_server \
 | Model Family (Variants)             | Example HuggingFace Identifier                     | Description                                                                            |
 |-------------------------------------|--------------------------------------------------|----------------------------------------------------------------------------------------|
 | **DeepSeek** (v1, v2, v3/R1)        | `deepseek-ai/DeepSeek-R1`                        | Series of advanced reasoning-optimized models (including a 671B MoE) trained with reinforcement learning; top performance on complex reasoning, math, and code tasks. [SGLang provides Deepseek v3/R1 model-specific optimizations](https://docs.sglang.ai/references/deepseek)|
-| **Qwen** (2, 2.5 series, MoE)       | `Qwen/Qwen2.5-14B-Instruct`                       | Alibaba’s Qwen model family (7B to 72B) with SOTA performance; Qwen2.5 series improves multilingual capability and includes base, instruct, MoE, and code-tuned variants. |
+| **Qwen** (3, 3MoE, 2.5, 2 series)       | `Qwen/Qwen3-4B-Base`, `Qwen/Qwen3-MoE-15B-A2B`       | Alibaba’s latest Qwen3 series for complex reasoning, language understanding, and generation tasks; Support for MoE variants along with previous generation 2.5, 2, etc. |
 | **Llama** (2, 3.x, 4 series)        | `meta-llama/Llama-4-Scout-17B-16E-Instruct`       | Meta’s open LLM series, spanning 7B to 400B parameters (Llama 2, 3, and new Llama 4) with well-recognized performance. [SGLang provides Llama-4 model-specific optimizations](https://docs.sglang.ai/references/llama4)  |
 | **Mistral** (Mixtral, NeMo, Small3) | `mistralai/Mistral-7B-Instruct-v0.2`             | Open 7B LLM by Mistral AI with strong performance; extended into MoE (“Mixtral”) and NeMo Megatron variants for larger scale. |
 | **Gemma** (v1, v2, v3)              | `google/gemma-3-1b-it`                            | Google’s family of efficient multilingual models (1B–27B); Gemma 3 offers a 128K context window, and its larger (4B+) variants support vision input. |