快慢思考不用二选一！华为开源7B模型实现自由切，精度不变思维链减近50%

Core Viewpoint - Huawei's latest release, openPangu-Embedded-7B-v1.1, features a dual "thinking engine" that allows for seamless switching between fast and slow thinking modes, addressing a significant pain point in the industry where models traditionally had to choose one mode over the other [1][3][4]. Model Features - The openPangu-Embedded-7B-v1.1 model employs a progressive fine-tuning strategy and a unique adaptive thinking mode, enabling manual switching between fast and slow thinking or automatic transitions based on problem difficulty [3][4]. - The model significantly improves accuracy while maintaining efficiency, achieving nearly a 50% reduction in average reasoning chain length in benchmarks like CMMLU, without sacrificing precision [4][18]. Training Strategy - The training process consists of three progressive stages: 1. Selecting moderately challenging topics to ensure the model learns effectively without stagnation or overwhelming difficulty [8]. 2. Merging multiple model versions to consolidate knowledge and prevent forgetting [9]. 3. Continuously expanding the model's capabilities to tackle more complex tasks [10]. - This iterative training approach transforms the model into a continuously evolving learner rather than a passive recipient of knowledge [10]. Adaptive Mechanism - The model introduces a two-phase course for adaptive thinking: 1. The first phase teaches the model to distinguish between fast and slow thinking using labeled training data [13]. 2. The second phase allows the model to autonomously determine when to use each thinking mode based on the complexity of the task [14]. - This transition enhances the model's flexibility and autonomy in complex reasoning tasks [15]. Performance Metrics - The openPangu-Embedded-7B-v1.1 outperforms its predecessor in various benchmarks, including significant improvements in mathematical problem-solving [16][17]. - In tests, the model maintains high accuracy while reducing unnecessary reasoning steps, effectively balancing speed and precision [18]. Lightweight Model - Huawei also introduced openPangu-Embedded-1B, a lightweight model optimized for edge AI deployment, achieving high performance despite having only 1 billion parameters [20][21]. - This model demonstrates a strong performance-to-parameter ratio, setting a new benchmark for 1B-level models in China [22]. Conclusion - The release of openPangu-Embedded-7B-v1.1 represents a significant advancement in the large model field, showcasing innovative approaches to model training and adaptive thinking capabilities [23][24]. - The dual-mode thinking feature is expected to add value in various practical applications in the future [25].