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", device_map="auto")
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", 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
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
```

Any plans for a VL version?

#19

by kentrolla - opened Jun 25, 2025

Discussion

kentrolla

Jun 25, 2025

•

edited Jun 25, 2025

I'm using a LoRA tuned Qwen3 at the moment and it is by far the absolute best model for my use case. However, image processing and understanding is a huge deal, so I'm wondering if any VL model/version is planned?

I am basically building system that is utilizing a modified Qwen 3, that uses it's internal reasoning paired with a self-managed quad memory system along with a plethora of tools that the model decides on it's own to use, as well as self logging and assessment. The model is not just doing a task because it was assigned to it, it understands why it's doing something and has a hierarchical structure to achieving the goal, in addition to being able to proactively message me for questions or additional input. Learning post-task is also done through a lesson system based on how the performance of the task was overall, and the AI adapts and learns based off of this to continually grow.

Currently I'm at the point where I've had to rely on external image to text, but it's intermittent. My AI has been able to complete complex tasks, such as using the GUI to access my PC, control the mouse and decide on it's own on what to click, to search, then analyze the data (on a Chrome browser for example). Everything is working and has been tested multiple times in different ways.

What would really skyrocket my project is a true image processor.

mookiezi

Sep 14, 2025

wow do you have anything about this publish anywhere

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