DeepSeek R1 has achieved state-of-the-art performance by integrating cold-start data and multi-stage training, enabling deep thinking and complex reasoning.
QwQ-32B, a model with 32 billion parameters that achieves performance comparable to DeepSeek-R1, which boasts 671 billion parameters (with 37 billion activated). This remarkable outcome underscores the effectiveness of RL when applied to robust foundation models pretrained on extensive world knowledge.
QwQ-32B is evaluated across a range of benchmarks designed to assess its mathematical reasoning, coding proficiency, and general problem-solving capabilities. The results below highlight QwQ-32B’s performance in comparison to other leading models, including DeepSeek-R1-Distilled-Qwen-32B, DeepSeek-R1-Distilled-Llama-70B, o1-mini, and the original DeepSeek-R1.
We began with a cold-start checkpoint and implemented a reinforcement learning (RL) scaling approach driven by outcome-based rewards. In the initial stage, we scale RL specifically for math and coding tasks. Rather than relying on traditional reward models, we utilized an accuracy verifier for math problems to ensure the correctness of final solutions and a code execution server to assess whether the generated codes successfully pass predefined test cases. As training episodes progress, performance in both domains shows continuous improvement. After the first stage, we add another stage of RL for general capabilities. It is trained with rewards from general reward model and some rule-based verifiers. We find that this stage of RL training with a small amount of steps can increase the performance of other general capabilities, such as instruction following, alignment with human preference, and agent performance, without significant performance drop in math and coding.
Below are brief examples demonstrating how to use QwQ-32B via Hugging Face Transformers and Alibaba Cloud DashScope API.
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/QwQ-32B"
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 the word \"strawberry\""
messages = [
{"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=32768
)
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)
from openai import OpenAI
import os
# Initialize OpenAI client
client = OpenAI(
# If the environment variable is not configured, replace with your API Key: api_key="sk-xxx"
# How to get an API Key：https://help.aliyun.com/zh/model-studio/developer-reference/get-api-key
api_key=os.getenv("DASHSCOPE_API_KEY"),
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
)
reasoning_content = ""
content = ""
is_answering = False
completion = client.chat.completions.create(
model="qwq-32b",
messages=[
{"role": "user", "content": "Which is larger, 9.9 or 9.11?"}
],
stream=True,
# Uncomment the following line to return token usage in the last chunk
# stream_options={
# "include_usage": True
# }
)
print("\n" + "=" * 20 + "reasoning content" + "=" * 20 + "\n")
for chunk in completion:
# If chunk.choices is empty, print usage
if not chunk.choices:
print("\nUsage:")
print(chunk.usage)
else:
delta = chunk.choices[0].delta
# Print reasoning content
if hasattr(delta, 'reasoning_content') and delta.reasoning_content is not None:
print(delta.reasoning_content, end='', flush=True)
reasoning_content += delta.reasoning_content
else:
if delta.content != "" and is_answering is False:
print("\n" + "=" * 20 + "content" + "=" * 20 + "\n")
is_answering = True
# Print content
print(delta.content, end='', flush=True)
content += delta.content
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