State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.
State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.
状态空间模型的时间复杂度为O(n)，而变压器为O(n²)。推理速度快5倍，支持百万标记序列，无需KV缓存。具有硬件感知设计的选择性SSM。Mamba-1（d_state=16）和Mamba-2（d_state=128，多头）。模型在HuggingFace上具有130M-2.8B的参数。
Category: developer (开发工具) · Author: davila7 · Version: @main · License: MIT
Tags: Model Architecture, Mamba, State Space Models, SSM, Linear Complexity, Long Context, Efficient Inference, Hardware-Aware, Alternative To Transformers
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Search for places (restaurants, cafes, etc.) via Google Places API proxy on localhost.
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Category:developer
Tags:Model Architecture, Mamba, State Space Models, SSM, Linear Complexity, Long Context, Efficient Inference, Hardware-Aware, Alternative To Transformers