In the field of Artificial Intelligence Generated Content (AIGC), the Kimi Linear model developed by the Moonshot team has achieved significant technological advances. This innovative model has improved the speed of processing long contexts by 2.9 times and decoding speed by 6 times, breaking through the performance bottlenecks of traditional full-attention mechanisms. Kimi Linear adopts a hybrid linear attention architecture, outperforming the commonly used Softmax attention mechanism in multiple scenarios such as context processing and reinforcement learning.

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Traditional Transformer models use the Softmax attention mechanism, which has a computational complexity of up to O(n²). This makes the computational load and memory consumption grow exponentially when processing long texts, seriously affecting the practical application of the model. The introduction of linear attention reduces this complexity to O(n), significantly improving processing efficiency. However, early linear attention had unsatisfactory performance, especially in terms of limitations in managing long sequences.

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The core innovation of the Kimi Linear model is Kimi Delta Attention (KDA), which improves the model's memory management capabilities by introducing a fine-grained gating mechanism. KDA can dynamically adjust the memory state based on the input, effectively controlling information forgetting and retention, thereby better handling information in long-term interactions.

Kimi Linear also adopts the Moonlight architecture, which mixes KDA with full-attention layers in a 3:1 ratio to achieve a balance between efficiency and model capability. This design enables Kimi Linear to demonstrate excellent performance in long-context processing while effectively reducing computational costs.

Through a series of experiments, Kimi Linear has shown excellent performance on multiple tasks, especially in palindrome and multi-query correlation recall tasks that require long-context memory, with accuracy far exceeding previous models, demonstrating the advantages of fine-grained control.

Key Points:  

🌟 The Kimi Linear model has improved the speed of long-context processing by 2.9 times and decoding speed by 6 times.  

🔍 It uses the innovative Kimi Delta Attention (KDA) mechanism to optimize memory management and information forgetting.  

📈 Through a 3:1 hybrid architecture design, it balances computational efficiency and model performance, showing outstanding capabilities in experimental results.