QwQ: Tiny Thinking Model That Tops DeepSeek R1 (Open Source)

1. QwQ 32B is a powerful, open-source thinking model.

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QwQ 32B by Alibaba offers comparable performance to DeepSeek R1 but is significantly smaller, allowing it to run on personal computers.

• It has 32 billion parameters compared to DeepSeek's 671 billion.
• The model is designed for fast inference, achieving 450 tokens per second.
• Being open-source allows for broader accessibility and experimentation.

2. Reinforcement learning enhances QwQ's capabilities.

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QwQ utilizes reinforcement learning with outcome-based rewards to improve its critical thinking and coding abilities.

• The model was trained using a scaling approach driven by outcome-based rewards.
• It employs separate verification for math and coding tasks to ensure accuracy.
• This method allows the model to learn effectively from both correct and incorrect outputs.

3. QwQ's training includes a hybrid reinforcement learning approach.

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After initial training focused on math and coding, QwQ incorporates general reinforcement learning for broader capabilities.

• This hybrid approach enhances instruction following and alignment with human preferences.
• It maintains performance in specialized tasks while improving general capabilities.
• The model's design allows for iterative improvements based on feedback.

4. QwQ's performance benchmarks show competitive results.

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While QwQ 32B performs well, it has mixed results against other models in specific benchmarks.

• It scored 78 on the Amy 2024 benchmark, outperforming DeepSeek R1 in some areas.
• However, it scored lower on the GPT QA Diamond benchmark compared to DeepSeek R1.
• The model's efficiency is notable given its smaller parameter count.

5. QwQ's context window and thinking process have limitations.

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The model's context window is relatively small, which may affect its performance in complex tasks.

• A 132k context window is on the smaller side compared to current standards.
• The model tends to engage in extensive thinking, which can consume more tokens.
• Implementing techniques like chain of thought prompting could optimize its output.