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add mamba
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## mamba-minimal
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Mamba在一个PyTorch文件中的简单、极简实现。
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Simple, minimal implementation of Mamba in one file of PyTorch.
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特点:
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* 与官方实现的前向和后向传递具有相同的数值输出
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* 简化的、可读的、带注释的代码
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Featuring:
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* Equivalent numerical output as official implementation for both forward and backward pass
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* Simplified, readable, annotated code
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不包括:
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* 速度。官方实现经过大量优化,这些优化是Mamba论文的核心贡献之一。为了可读性将大部分实现保持简单。
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* 正确的参数初始化(尽管可以在不牺牲可读性的情况下添加)
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Does NOT include:
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* Speed. The official implementation is heavily optimized, and these optimizations are core contributions of the Mamba paper. I kept most implementations simple for readability.
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* Proper parameter initialization (though this could be added without sacrificing readability)
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## 演示
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## Demo
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See [demo.ipynb](demo.ipynb) for examples of prompt completions.
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参见[demo.ipynb](demo.ipynb)以获取提示完成的示例。
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```python
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from model import Mamba
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@ -23,12 +21,12 @@ tokenizer = AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b')
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generate(model, tokenizer, 'Mamba is the')
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```
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> Mamba is the world's longest venomous snake with an estimated length of over 150 m. With such a large size and a venomous bite, Mamba kills by stabbing the victim (which is more painful and less effective than a single stab of the bite)
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> Mamba 是世界上最长的毒蛇,估计长度超过150米。由于其巨大的体型和剧毒的咬合力,Mamba通过刺伤受害者来杀人(这比单次咬合的刺痛感更强,但效果更差)
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150 meters... 🫢 scary!
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150米……🫢 可怕!
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## References
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## 参考资料
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The Mamba architecture was introduced in [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/abs/2312.00752) by [Albert Gu](https://twitter.com/_albertgu?lang=en) and [Tri Dao](https://twitter.com/tri_dao?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor).
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Mamba架构由[Albert Gu](https://twitter.com/_albertgu?lang=en)和[Tri Dao](https://twitter.com/tri_dao?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor)在[Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/abs/2312.00752)中提出。
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The official implementation is here: https://github.com/state-spaces/mamba/tree/main
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官方实现见此处: https://github.com/state-spaces/mamba/tree/main
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