Large Language Models

Contents

Large Language Models#

These models accept text input and produce text output (e.g., chat completions). They are primarily large language models (LLMs), some with mixture-of-experts (MoE) architectures for scaling.

Example launch Command#

python3 -m sglang.launch_server \
  --model-path meta-llama/Llama-3.2-1B-Instruct \  # example HF/local path
  --host 0.0.0.0 \
  --port 30000 \

Supported models#

Below the supported models are summarized in a table.

If you are unsure if a specific architecture is implemented, you can search for it via GitHub. For example, to search for Qwen3ForCausalLM, use the expression:

repo:sgl-project/sglang path:/^python\/sglang\/srt\/models\// Qwen3ForCausalLM

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 and Reasoning Parser
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
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
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.
MiniCPM (v3, 4B)	`openbmb/MiniCPM3-4B`	OpenBMB’s series of compact LLMs for edge devices; MiniCPM 3 (4B) achieves GPT-3.5-level results in text tasks.
OLMoE (Open MoE)	`allenai/OLMoE-1B-7B-0924`	Allen AI’s open Mixture-of-Experts model (7B total, 1B active parameters) delivering state-of-the-art results with sparse expert activation.
StableLM (3B, 7B)	`stabilityai/stablelm-tuned-alpha-7b`	StabilityAI’s early open-source LLM (3B & 7B) for general text generation; a demonstration model with basic instruction-following ability.
Command-R (Cohere)	`CohereForAI/c4ai-command-r-v01`	Cohere’s open conversational LLM (Command series) optimized for long context, retrieval-augmented generation, and tool use.
DBRX (Databricks)	`databricks/dbrx-instruct`	Databricks’ 132B-parameter MoE model (36B active) trained on 12T tokens; competes with GPT-3.5 quality as a fully open foundation model.
Grok (xAI)	`xai-org/grok-1`	xAI’s grok-1 model known for vast size(314B parameters) and high quality; integrated in SGLang for high-performance inference.
ChatGLM (GLM-130B family)	`THUDM/chatglm2-6b`	Zhipu AI’s bilingual chat model (6B) excelling at Chinese-English dialogue; fine-tuned for conversational quality and alignment.
InternLM 2 (7B, 20B)	`internlm/internlm2-7b`	Next-gen InternLM (7B and 20B) from SenseTime, offering strong reasoning and ultra-long context support (up to 200K tokens).
ExaONE 3 (Korean-English)	`LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct`	LG AI Research’s Korean-English model (7.8B) trained on 8T tokens; provides high-quality bilingual understanding and generation.
Baichuan 2 (7B, 13B)	`baichuan-inc/Baichuan2-13B-Chat`	BaichuanAI’s second-generation Chinese-English LLM (7B/13B) with improved performance and an open commercial license.
XVERSE (MoE)	`xverse/XVERSE-MoE-A36B`	Yuanxiang’s open MoE LLM (XVERSE-MoE-A36B: 255B total, 36B active) supporting ~40 languages; delivers 100B+ dense-level performance via expert routing.
SmolLM (135M–1.7B)	`HuggingFaceTB/SmolLM-1.7B`	Hugging Face’s ultra-small LLM series (135M–1.7B params) offering surprisingly strong results, enabling advanced AI on mobile/edge devices.
GLM-4 (Multilingual 9B)	`ZhipuAI/glm-4-9b-chat`	Zhipu’s GLM-4 series (up to 9B parameters) – open multilingual models with support for 1M-token context and even a 5.6B multimodal variant (Phi-4V).
MiMo (7B series)	`XiaomiMiMo/MiMo-7B-RL`	Xiaomi’s reasoning-optimized model series, leverages Multiple-Token Prediction for faster inference.
Arcee AFM-4.5B	`arcee-ai/AFM-4.5B-Base`	Arcee’s foundational model series for real world reliability and edge deployments.
Persimmon (8B)	`adept/persimmon-8b-chat`	Adept’s open 8B model with a 16K context window and fast inference; trained for broad usability and licensed under Apache 2.0.
Granite 3.0, 3.1 (IBM)	`ibm-granite/granite-3.1-8b-instruct`	IBM’s open dense foundation models optimized for reasoning, code, and business AI use cases. Integrated with Red Hat and watsonx systems.
Granite 3.0 MoE (IBM)	`ibm-granite/granite-3.0-3b-a800m-instruct`	IBM’s Mixture-of-Experts models offering strong performance with cost-efficiency. MoE expert routing designed for enterprise deployment at scale.