Instructions to use Qwen/Qwen3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen3-8B with Transformers:

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

pipe = pipeline("text-generation", model="Qwen/Qwen3-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B")
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
Local Apps Settings

vLLM

How to use Qwen/Qwen3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/Qwen3-8B"
# 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/Qwen3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Qwen/Qwen3-8B

SGLang

How to use Qwen/Qwen3-8B 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/Qwen3-8B" \
    --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/Qwen3-8B",
		"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/Qwen3-8B" \
        --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/Qwen3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Qwen/Qwen3-8B with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen3-8B
```

New 8B model much slower than old 7B model when running on vLLM.

by stev236 - opened May 8, 2025

Discussion

stev236

May 8, 2025

Did anybody else notice that this model runs much slower on vLLM than the old 7B-1M model?

I'm using vLLM, on 4 GPUs (Ampere), weight-only FP8 compression using the Marlin kernel, 'enable_thinking=False', and tried with batchsize 1 and batchsize 4 with similar results.

Shown below is the performance reported by vLLM when summarizing a 28k token document. I ran each task twice. I presume the second run is always much faster because of some prefix caching. As you can see, the new model takes almost 2x longer on the first run. On the second run, with prefix caching likely cutting time dramatically, it still takes 50% longer.

Old model: Qwen2.5-7b-1M ----------------------------------------------------------------
[00:13<00:00, 13.75s/it, est. speed input: 2063.93 toks/s, output: 14.69 toks/s] (first run)
[00:02<00:00, 2.23s/it, est. speed input: 12732.36 toks/s, output: 87.04 toks/s] (second run)

New model: Qwen3-8b: ----------------------------------------------------------------
[00:20<00:00, 20.77s/it, est. speed input: 1366.44 toks/s, output: 8.62 toks/s] (first run)
[00:02<00:00, 2.94s/it, est. speed input: 9656.35 toks/s, output: 60.90 toks/s] (second run)

As I reported elsewhere, even the 4B model is slower than the old 7B model.
How can that be ??

ruyrdiaz

May 8, 2025

Hi! I am working with huggingface transformers and it came to my attention that Qwen2.5 utilized flash attention and now I'm testing Qwen3 without flash attention. This could be related to your question.

stev236 changed discussion status to closed Aug 2, 2025

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment