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", 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/Qwen3-8B 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
```

yangapku commited on Apr 29, 2025

Commit

2069b3f

verified ·

1 Parent(s): a80f5e5

Update README.md

Browse files

Files changed (1) hide show

README.md +5 -5

README.md CHANGED Viewed

@@ -92,7 +92,7 @@ print("thinking content:", thinking_content)
 print("content:", content)
 ```
-For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.4` or  to create an OpenAI-compatible API endpoint:
 - SGLang:
     ```shell
     python -m sglang.launch_server --model-path Qwen/Qwen3-8B --reasoning-parser qwen3
@@ -102,7 +102,7 @@ For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.4` or  to create
     vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1
     ```
-For local use, applications such as llama.cpp, Ollama, LMStudio, and MLX-LM have also supported Qwen3.
 ## Switching Between Thinking and Non-Thinking Mode
@@ -276,7 +276,7 @@ YaRN is currently supported by several inference frameworks, e.g., `transformers
     {
         ...,
         "rope_scaling": {
-            "type": "yarn",
             "factor": 4.0,
             "original_max_position_embeddings": 32768
         }
@@ -288,12 +288,12 @@ YaRN is currently supported by several inference frameworks, e.g., `transformers
   For `vllm`, you can use
     ```shell
-    vllm serve ... --rope-scaling '{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072
     ```
   For `sglang`, you can use
     ```shell
-    python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
     ```
   For `llama-server` from `llama.cpp`, you can use

 print("content:", content)
 ```
+For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
 - SGLang:
     ```shell
     python -m sglang.launch_server --model-path Qwen/Qwen3-8B --reasoning-parser qwen3
     vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1
     ```
+For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
 ## Switching Between Thinking and Non-Thinking Mode
     {
         ...,
         "rope_scaling": {
+            "rope_type": "yarn",
             "factor": 4.0,
             "original_max_position_embeddings": 32768
         }
   For `vllm`, you can use
     ```shell
+    vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072
     ```
   For `sglang`, you can use
     ```shell
+    python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
     ```
   For `llama-server` from `llama.cpp`, you can use