Instructions to use Qwen/QwQ-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/QwQ-32B with Transformers:

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

pipe = pipeline("text-generation", model="Qwen/QwQ-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Qwen/QwQ-32B")
model = AutoModelForCausalLM.from_pretrained("Qwen/QwQ-32B", 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
Notebooks
Google Colab
Kaggle
AMD Developer Cloud
Local Apps Settings

vLLM

How to use Qwen/QwQ-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/QwQ-32B"
# 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/QwQ-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Qwen/QwQ-32B

SGLang

How to use Qwen/QwQ-32B 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/QwQ-32B" \
    --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/QwQ-32B",
		"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/QwQ-32B" \
        --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/QwQ-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Qwen/QwQ-32B with Docker Model Runner:
```
docker model run hf.co/Qwen/QwQ-32B
```

fix: add missing <think> tag to assistant messages

#81

by jybsuper - opened Jun 2, 2025

base: refs/heads/main

←

from: refs/pr/81

Discussion Files changed

-1

jybsuper

Jun 2, 2025

•

edited Jun 2, 2025

Fix chat template to prepend <think> when assistant messages contain </think> but not <think>

When add_generation_prompt=True, the template adds <|im_start|>assistant\n<think>\n as the generation prompt. This means the model will not output the opening <think> tag itself, only the thinking content followed by .

However, when applying the chat template to existing conversations, assistant messages that were generated this way would be missing their opening <think> tag, causing template inconsistency.

This fix adds logic to detect when an assistant message contains </think> but not <think>, and automatically prepends <think>\n to maintain consistency.

Example:

Given the following messages:

[
  {"role": "system", "content": "You are a helpful assistant."},
  {"role": "user", "content": "What is 2+2?"}
]

The prompt would be:

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
What is 2+2?<|im_end|>
<|im_start|>assistant
<think>

The model would respond with something like:

The user wants to do arithmetic calculation
</think>
The answer is 4.

The full conversation history would then be:

[
  {"role": "system", "content": "You are a helpful assistant."},
  {"role": "user", "content": "What is 2+2?"},
  {"role": "assistant", "content": "The user wants to do arithmetic calculation\n</think>\nThe answer is 4."}
]

Before fix:

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
What is 2+2?<|im_end|>
<|im_start|>assistant
The user wants to do arithmetic calculation
</think>
The answer is 4.<|im_end|>

After this fix:

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
What is 2+2?<|im_end|>
<|im_start|>assistant
<think>
The user wants to do arithmetic calculation
</think>
The answer is 4.<|im_end|>

This fix is required for correct loss maksing in multiturn SFT and RL.

fix: add missing <think> tag to assistant messages5baf59fc

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