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Improve docs and developer guide (#9044)

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Lianmin Zheng
2025-08-10 21:05:18 -07:00
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6
docs/supported_models/generative_models.md
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@@ -25,9 +25,9 @@ in the GitHub search bar.
| 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) and [Reasoning Parser](https://docs.sglang.ai/backend/separate_reasoning)|
| **Qwen** (3, 3MoE, 2.5, 2 series) | `Qwen/Qwen3-0.6B`, `Qwen/Qwen3-30B-A3B` | 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. [SGLang provides Qwen3 specific reasoning parser](https://docs.sglang.ai/backend/separate_reasoning)|
| **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) |
| **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](../basic_usage/deepseek.md) and [Reasoning Parser](../advanced_features/separate_reasoning.ipynb)|
| **Qwen** (3, 3MoE, 2.5, 2 series) | `Qwen/Qwen3-0.6B`, `Qwen/Qwen3-30B-A3B` | 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. [SGLang provides Qwen3 specific reasoning parser](../advanced_features/separate_reasoning.ipynb)|
| **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](../basic_usage/llama4.md)) |
| **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. |
| **Phi** (Phi-1.5, Phi-2, Phi-3, Phi-4, Phi-MoE series) | `microsoft/Phi-4-multimodal-instruct`, `microsoft/Phi-3.5-MoE-instruct` | Microsoft’s Phi family of small models (1.3B–5.6B); Phi-4-multimodal (5.6B) processes text, images, and speech, Phi-4-mini is a high-accuracy text model and Phi-3.5-MoE is a mixture-of-experts model. |

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docs/supported_models/support_new_models.md
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@@ -27,7 +27,7 @@ standard LLM support:
3. **Multimodal Data Processor**:
Define a new `Processor` class that inherits from `BaseMultimodalProcessor` and register this processor as your
model’s dedicated processor.
See [multimodal_processor.py](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/managers/multimodal_processor.py)
See [multimodal_processor.py](https://github.com/sgl-project/sglang/tree/main/python/sglang/srt/multimodal/processors)
for more details.
4. **Handle Multimodal Tokens**:

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docs/supported_models/transformers_fallback.md
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@@ -18,7 +18,7 @@ python3 -m sglang.launch_server \
### Quantization
Transformers fall back has supported most of available quantization in SGLang (except GGUF). See [Quantization page](https://docs.sglang.ai/backend/quantization.html) for more information about supported quantization in SGLang.
Transformers fall back has supported most of available quantization in SGLang (except GGUF). See [Quantization page](../advanced_features/quantization.md) for more information about supported quantization in SGLang.
### Remote code