ContextQMD
Back to Transformers

SGLang

docs/source/en/community_integrations/sglang.md

5.8.02.8 KB
Original Source
<!--Copyright 2025 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be rendered properly in your Markdown viewer. -->

SGLang

SGLang is a low-latency, high-throughput inference engine for large language models (LLMs). It also includes a frontend language for building agentic workflows.

Set model_impl="transformers" to load a Transformers modeling backend.

py
import sglang as sgl

llm = sgl.Engine("meta-llama/Llama-3.2-1B-Instruct", model_impl="transformers")
print(llm.generate(["The capital of France is"], {"max_new_tokens": 20})[0])

Pass --model-impl transformers to the sglang.launch_server command for online serving.

bash
python3 -m sglang.launch_server \
  --model-path meta-llama/Llama-3.2-1B-Instruct \
  --model-impl transformers \
  --host 0.0.0.0 \
  --port 30000

Transformers integration

Setting model_impl="transformers" tells SGLang to skip its native model matching and use the Transformers model directly.

  1. [PreTrainedConfig.from_pretrained] loads the model's config.json from the Hub or your Hugging Face cache.
  2. [AutoModel.from_config] resolves the model class based on the config.
  3. During loading, _attn_implementation is set to "sglang". This routes attention calls through SGLang's RadixAttention kernels.
  4. SGLang's parallel linear class replaces linear layers to support tensor parallelism.
  5. The load_weights function populates the model with weights from safetensors files.

The model benefits from all SGLang optimizations while using the Transformers model structure.

[!WARNING] Compatible models require _supports_attention_backend=True so SGLang can control attention execution. See the Building a compatible model backend for inference guide for details.

Resources