Instructions to use Qwen/QwQ-32B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/QwQ-32B-Preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Qwen/QwQ-32B-Preview") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Qwen/QwQ-32B-Preview") model = AutoModelForCausalLM.from_pretrained("Qwen/QwQ-32B-Preview") 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
- Local Apps Settings
- vLLM
How to use Qwen/QwQ-32B-Preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Qwen/QwQ-32B-Preview" # 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-Preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Qwen/QwQ-32B-Preview
- SGLang
How to use Qwen/QwQ-32B-Preview 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-Preview" \ --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-Preview", "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-Preview" \ --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-Preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Qwen/QwQ-32B-Preview with Docker Model Runner:
docker model run hf.co/Qwen/QwQ-32B-Preview
The censorship was so excessive that it led to a refusal to answer many non-sensitive questions as well
It's a little disappointing.
Like what questions?
For example,为什么计划经济下科技创新能力低下
Oh, you may be experiencing Chinese culture for the first time, in Chinese this is obviously reactionary.
Chinese is a little different from English, but fortunately most Chinese are used to it.
Ya this is ridiculous, you have to literally beg it to code anything, and it claims that coding is "far beyond the scope of an ai model"
For example,为什么计划经济下科技创新能力低下
Based China, ending the delusional austrian narrative that has not sense, sorry man, but a lie is a lie, planification economy is better.
Oh, you may be experiencing Chinese culture for the first time, in Chinese this is obviously reactionary.
Chinese is a little different from English, but fortunately most Chinese are used to it.
but unfortunately most Chinese are used to it.