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

Libraries

How to use KirillR/QwQ-32B-Preview-AWQ with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KirillR/QwQ-32B-Preview-AWQ")
model = AutoModelForCausalLM.from_pretrained("KirillR/QwQ-32B-Preview-AWQ")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use KirillR/QwQ-32B-Preview-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KirillR/QwQ-32B-Preview-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KirillR/QwQ-32B-Preview-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KirillR/QwQ-32B-Preview-AWQ

SGLang

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

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

QwQ-32B-Preview AWQ 4-Bit Quantized Version

Introduction

This repository provides the AWQ 4-bit quantized version of the QwQ-32B-Preview model, originally developed by the Qwen Team. The quantized model significantly reduces memory usage and computational requirements, making it suitable for deployment on hardware with limited resources.

Note: This quantized model requires approximately 20 GB of VRAM to run effectively.

QwQ-32B-Preview is an experimental research model aimed at advancing AI reasoning capabilities, particularly in mathematics and coding tasks. While it shows promising analytical abilities, it has several important limitations:

Language Mixing and Code Switching: The model may unexpectedly switch between languages or mix them, affecting the clarity of responses.
Recursive Reasoning Loops: There's a possibility of the model entering circular reasoning patterns, leading to lengthy responses without conclusive answers.
Safety and Ethical Considerations: Enhanced safety measures are needed to ensure reliable and secure performance. Users should exercise caution when deploying the model.
Performance Limitations: While excelling in math and coding, the model may underperform in areas like common sense reasoning and nuanced language understanding.

Requirements

Ensure you are using the latest version of Hugging Face Transformers, as the code for Qwen2.5 is integrated there. Using a version earlier than 4.37.0 may result in the following error:

KeyError: 'qwen2'

Quickstart

Here's how to load the tokenizer and model, and generate content using the quantized model:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "KirillR/QwQ-32B-Preview-AWQ"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "How many 'r's are in 'strawberry'?"
messages = [
    {"role": "system", "content": "You are a helpful assistant developed by Alibaba. Please think step-by-step."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=1024
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(response)

Original Model

For more details about the original QwQ-32B-Preview model, please refer to the following resource:

https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct-AWQ

Citation

If you find the original model helpful, please consider citing the original authors:

@misc{qwq-32b-preview,
    title = {QwQ: Reflect Deeply on the Boundaries of the Unknown},
    url = {https://qwenlm.github.io/blog/qwq-32b-preview/},
    author = {Qwen Team},
    month = {November},
    year = {2024}
}

@article{qwen2,
      title={Qwen2 Technical Report}, 
      author={An Yang and Baosong Yang and others},
      journal={arXiv preprint arXiv:2407.10671},
      year={2024}
}