Instructions to use Qwen/Qwen3-30B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen3-30B-A3B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Qwen/Qwen3-30B-A3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-30B-A3B")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-30B-A3B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
AMD Developer Cloud
Local Apps Settings

vLLM

How to use Qwen/Qwen3-30B-A3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/Qwen3-30B-A3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qwen/Qwen3-30B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Qwen/Qwen3-30B-A3B

SGLang

How to use Qwen/Qwen3-30B-A3B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Qwen/Qwen3-30B-A3B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qwen/Qwen3-30B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Qwen/Qwen3-30B-A3B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qwen/Qwen3-30B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Qwen/Qwen3-30B-A3B with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen3-30B-A3B
```

Enabling or disabling reasoning on-demand with remote vLLM server?

#15

by abhinavkulkarni - opened May 1, 2025

Discussion

abhinavkulkarni

May 1, 2025

•

edited May 1, 2025

Hi,

I have brought up a remote vLLM server with reasoning enabled as follows:

services:
  vllm:
    image: vllm/vllm-openai:latest
    command: --model Qwen/Qwen3-30B-A3B-FP8 --enable-reasoning --reasoning-parser deepseek_r1
    runtime: nvidia
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    volumes:
      - ~/.cache/huggingface:/root/.cache/huggingface
    ports:
      - "8000:8000"
    ipc: host
    restart: unless-stopped

Now, I can use openai.OpenAI client or requests module to get completions, but is it possible to enable or disable reasoning on-demand? The reasoning seems to be always enabled.

Thanks for the excellent models!

abhinavkulkarni changed discussion title from Possibility of enabling and disabling reasoning on-demand with remote vLLM server? to Enabling or disabling reasoning on-demand with remote vLLM server? May 1, 2025

igorkan

May 26, 2025

•

edited May 26, 2025

Hi, is there any follow-up on this? Have you managed to find a solution?

Upd: https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes

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