OpenAI APIs - Completions#
SGLang provides OpenAI-compatible APIs to enable a smooth transition from OpenAI services to self-hosted local models. A complete reference for the API is available in the OpenAI API Reference.
This tutorial covers the following popular APIs:
chat/completionscompletions
Check out other tutorials to learn about vision APIs for vision-language models and embedding APIs for embedding models.
Launch A Server#
Launch the server in your terminal and wait for it to initialize.
[1]:
from sglang.test.doc_patch import launch_server_cmd
from sglang.utils import wait_for_server, print_highlight, terminate_process
server_process, port = launch_server_cmd(
"python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct --host 0.0.0.0 --log-level warning"
)
wait_for_server(f"http://localhost:{port}")
print(f"Server started on http://localhost:{port}")
/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
import pynvml # type: ignore[import]
/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
import pynvml # type: ignore[import]
`torch_dtype` is deprecated! Use `dtype` instead!
WARNING:transformers.configuration_utils:`torch_dtype` is deprecated! Use `dtype` instead!
WARNING:sglang.srt.server_args:
########################################################################
# For contributors and developers: #
# Please move environment variable definitions to sglang.srt.environ #
# using the following pattern: #
# SGLANG_XXX = EnvBool(False) #
# #
########################################################################
All deep_gemm operations loaded successfully!
/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
import pynvml # type: ignore[import]
/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
import pynvml # type: ignore[import]
`torch_dtype` is deprecated! Use `dtype` instead!
[2025-09-28 15:25:41] `torch_dtype` is deprecated! Use `dtype` instead!
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[2025-09-28 15:25:43] MOE_RUNNER_BACKEND is not initialized, using triton backend
All deep_gemm operations loaded successfully!
Loading safetensors checkpoint shards: 0% Completed | 0/1 [00:00<?, ?it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00, 5.60it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00, 5.59it/s]
Capturing batches (bs=1 avail_mem=72.85 GB): 100%|██████████| 3/3 [00:00<00:00, 6.41it/s]
NOTE: Typically, the server runs in a separate terminal.
In this notebook, we run the server and notebook code together, so their outputs are combined.
To improve clarity, the server logs are displayed in the original black color, while the notebook outputs are highlighted in blue.
To reduce the log length, we set the log level to warning for the server, the default log level is info.
We are running those notebooks in a CI environment, so the throughput is not representative of the actual performance.
Server started on http://localhost:37466
Chat Completions#
Usage#
The server fully implements the OpenAI API. It will automatically apply the chat template specified in the Hugging Face tokenizer, if one is available. You can also specify a custom chat template with --chat-template when launching the server.
[2]:
import openai
client = openai.Client(base_url=f"http://127.0.0.1:{port}/v1", api_key="None")
response = client.chat.completions.create(
model="qwen/qwen2.5-0.5b-instruct",
messages=[
{"role": "user", "content": "List 3 countries and their capitals."},
],
temperature=0,
max_tokens=64,
)
print_highlight(f"Response: {response}")
Model Thinking/Reasoning Support#
Some models support internal reasoning or thinking processes that can be exposed in the API response. SGLang provides unified support for various reasoning models through the chat_template_kwargs parameter and compatible reasoning parsers.
Supported Models and Configuration#
Model Family |
Chat Template Parameter |
Reasoning Parser |
Notes |
|---|---|---|---|
DeepSeek-R1 (R1, R1-0528, R1-Distill) |
|
|
Standard reasoning models |
DeepSeek-V3.1 |
|
|
Hybrid model (thinking/non-thinking modes) |
Qwen3 (standard) |
|
|
Hybrid model (thinking/non-thinking modes) |
Qwen3-Thinking |
N/A (always enabled) |
|
Always generates reasoning |
Kimi |
N/A (always enabled) |
|
Kimi thinking models |
Gpt-Oss |
N/A (always enabled) |
|
Gpt-Oss thinking models |
Basic Usage#
To enable reasoning output, you need to:
Launch the server with the appropriate reasoning parser
Set the model-specific parameter in
chat_template_kwargsOptionally use
separate_reasoning: Falseto not get reasoning content separately (default toTrue)
Note for Qwen3-Thinking models: These models always generate thinking content and do not support the enable_thinking parameter. Use --reasoning-parser qwen3-thinking or --reasoning-parser qwen3 to parse the thinking content.
Example: Qwen3 Models#
# Launch server:
# python3 -m sglang.launch_server --model Qwen/Qwen3-4B --reasoning-parser qwen3
from openai import OpenAI
client = OpenAI(
api_key="EMPTY",
base_url=f"http://127.0.0.1:30000/v1",
)
model = "Qwen/Qwen3-4B"
messages = [{"role": "user", "content": "How many r's are in 'strawberry'?"}]
response = client.chat.completions.create(
model=model,
messages=messages,
extra_body={
"chat_template_kwargs": {"enable_thinking": True},
"separate_reasoning": True
}
)
print("Reasoning:", response.choices[0].message.reasoning_content)
print("-"*100)
print("Answer:", response.choices[0].message.content)
ExampleOutput:
Reasoning: Okay, so the user is asking how many 'r's are in the word 'strawberry'. Let me think. First, I need to make sure I have the word spelled correctly. Strawberry... S-T-R-A-W-B-E-R-R-Y. Wait, is that right? Let me break it down.
Starting with 'strawberry', let's write out the letters one by one. S, T, R, A, W, B, E, R, R, Y. Hmm, wait, that's 10 letters. Let me check again. S (1), T (2), R (3), A (4), W (5), B (6), E (7), R (8), R (9), Y (10). So the letters are S-T-R-A-W-B-E-R-R-Y.
...
Therefore, the answer should be three R's in 'strawberry'. But I need to make sure I'm not counting any other letters as R. Let me check again. S, T, R, A, W, B, E, R, R, Y. No other R's. So three in total. Yeah, that seems right.
----------------------------------------------------------------------------------------------------
Answer: The word "strawberry" contains **three** letters 'r'. Here's the breakdown:
1. **S-T-R-A-W-B-E-R-R-Y**
- The **third letter** is 'R'.
- The **eighth and ninth letters** are also 'R's.
Thus, the total count is **3**.
**Answer:** 3.
Note: Setting "enable_thinking": False (or omitting it) will result in reasoning_content being None. Qwen3-Thinking models always generate reasoning content and don’t support the enable_thinking parameter.
Example: DeepSeek-V3 Models#
DeepSeek-V3 models support thinking mode through the thinking parameter:
# Launch server:
# python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.1 --tp 8 --reasoning-parser deepseek-v3
from openai import OpenAI
client = OpenAI(
api_key="EMPTY",
base_url=f"http://127.0.0.1:30000/v1",
)
model = "deepseek-ai/DeepSeek-V3.1"
messages = [{"role": "user", "content": "How many r's are in 'strawberry'?"}]
response = client.chat.completions.create(
model=model,
messages=messages,
extra_body={
"chat_template_kwargs": {"thinking": True},
"separate_reasoning": True
}
)
print("Reasoning:", response.choices[0].message.reasoning_content)
print("-"*100)
print("Answer:", response.choices[0].message.content)
Example Output:
Reasoning: First, the question is: "How many r's are in 'strawberry'?"
I need to count the number of times the letter 'r' appears in the word "strawberry".
Let me write out the word: S-T-R-A-W-B-E-R-R-Y.
Now, I'll go through each letter and count the 'r's.
...
So, I have three 'r's in "strawberry".
I should double-check. The word is spelled S-T-R-A-W-B-E-R-R-Y. The letters are at positions: 3, 8, and 9 are 'r's. Yes, that's correct.
Therefore, the answer should be 3.
----------------------------------------------------------------------------------------------------
Answer: The word "strawberry" contains **3** instances of the letter "r". Here's a breakdown for clarity:
- The word is spelled: S-T-R-A-W-B-E-R-R-Y
- The "r" appears at the 3rd, 8th, and 9th positions.
Note: DeepSeek-V3 models use the thinking parameter (not enable_thinking) to control reasoning output.
Parameters#
The chat completions API accepts OpenAI Chat Completions API’s parameters. Refer to OpenAI Chat Completions API for more details.
SGLang extends the standard API with the extra_body parameter, allowing for additional customization. One key option within extra_body is chat_template_kwargs, which can be used to pass arguments to the chat template processor.
[3]:
response = client.chat.completions.create(
model="qwen/qwen2.5-0.5b-instruct",
messages=[
{
"role": "system",
"content": "You are a knowledgeable historian who provides concise responses.",
},
{"role": "user", "content": "Tell me about ancient Rome"},
{
"role": "assistant",
"content": "Ancient Rome was a civilization centered in Italy.",
},
{"role": "user", "content": "What were their major achievements?"},
],
temperature=0.3, # Lower temperature for more focused responses
max_tokens=128, # Reasonable length for a concise response
top_p=0.95, # Slightly higher for better fluency
presence_penalty=0.2, # Mild penalty to avoid repetition
frequency_penalty=0.2, # Mild penalty for more natural language
n=1, # Single response is usually more stable
seed=42, # Keep for reproducibility
)
print_highlight(response.choices[0].message.content)
Streaming mode is also supported.
[4]:
stream = client.chat.completions.create(
model="qwen/qwen2.5-0.5b-instruct",
messages=[{"role": "user", "content": "Say this is a test"}],
stream=True,
)
for chunk in stream:
if chunk.choices[0].delta.content is not None:
print(chunk.choices[0].delta.content, end="")
Yes, that is a test. I was created by Alibaba Cloud based on the data available to me. I'm here to assist you with any questions you may have. What would you like to know?
Completions#
Usage#
Completions API is similar to Chat Completions API, but without the messages parameter or chat templates.
[5]:
response = client.completions.create(
model="qwen/qwen2.5-0.5b-instruct",
prompt="List 3 countries and their capitals.",
temperature=0,
max_tokens=64,
n=1,
stop=None,
)
print_highlight(f"Response: {response}")
Parameters#
The completions API accepts OpenAI Completions API’s parameters. Refer to OpenAI Completions API for more details.
Here is an example of a detailed completions request:
[6]:
response = client.completions.create(
model="qwen/qwen2.5-0.5b-instruct",
prompt="Write a short story about a space explorer.",
temperature=0.7, # Moderate temperature for creative writing
max_tokens=150, # Longer response for a story
top_p=0.9, # Balanced diversity in word choice
stop=["\n\n", "THE END"], # Multiple stop sequences
presence_penalty=0.3, # Encourage novel elements
frequency_penalty=0.3, # Reduce repetitive phrases
n=1, # Generate one completion
seed=123, # For reproducible results
)
print_highlight(f"Response: {response}")
Structured Outputs (JSON, Regex, EBNF)#
For OpenAI compatible structured outputs API, refer to Structured Outputs for more details.
[7]:
terminate_process(server_process)