QwQ-32B: NEW Opensource LLM Beats Deepseek R1! (Fully Tested)


Summary

The video introduces Alibaba's new open-source model with 32 billion parameters, showcasing advancements in reinforcement learning, foundation model pre-training, and environmental reasoning enhancements. It compares Alibaba's model with Deep Seek R1 in terms of parameters and performance in reasoning tasks. The video provides installation instructions for accessing the model via platforms like Hugging Face and Model Zoo for chat applications. It demonstrates the model's capabilities in reasoning, coding, and basic web JavaScript logic, highlighting its progress in solving mathematical equations and problem-solving tasks, while also noting areas for improvement in accuracy and logical reasoning skills. Overall, the model shows promise in various AI applications and problem-solving challenges, with ongoing enhancements and opportunities for further development.


Introduction of Alibaba's New Model

Introducing Alibaba's new open-source model with 32 billion parameters and its advancements in reinforcement learning and reasoning.

Key Advancements in the Model

Overview of the three key advancements in the model, including reinforcement learning, Foundation model pre-training, and environmental reasoning enhancements.

Performance Comparison with Deep Seek R1

Comparison of Alibaba's model with Deep Seek R1 model in terms of parameters and performance in reasoning tasks.

Installation and Accessing the Model

Instructions on how to install and access the model through platforms like Hugging Face and Model Zoo for chat applications.

2025 AI Conference Announcement

Announcement of the 2025 AI conference scheduled for March 17-21, focusing on various AI topics and sessions for developers and researchers.

Demonstration of Model's Abilities

Demonstration of the model's capabilities in reasoning, coding, and basic web JavaScript logic through interactive prompts.

SVG Code Creation Challenge

Evaluation of the model's performance in generating SVG code to represent a specific shape, highlighting its limitations in styling accuracy.

Logical Reasoning Challenge

Testing the model's logical reasoning skills with a train distance problem and evaluating its accuracy in providing the correct answer.

Mathematical Equation Challenge

Assessment of the model's ability to solve a sequence-based mathematical equation and its step-by-step progression in reaching the correct answer.

Problem-Solving Challenge

Evaluation of the model's problem-solving skills in identifying the heavier ball using a balance scale, noting its initial correct steps followed by an incorrect final conclusion.

Overall Performance and Conclusion

Summary of the model's performance in reasoning, math, coding, and problem-solving challenges, acknowledging its strengths and areas for improvement.

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