SimpleMamba

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Implementation of a modular, high-performance, and simplistic mamba for high-speed applications

SimpleMamba 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Simple Mamba

## Install
`pip install simple-mamba`

## Usage
```python
import torch
from simple_mamba import MambaBlock

# Define block parameters
dim = 512
hidden_dim = 128
heads = 8
in_channels = 3
out_channels = 3
kernel_size = 3

# Create an instance of MambaBlock
mamba_block = MambaBlock(
    dim, hidden_dim, heads, in_channels, out_channels, kernel_size
)

# Create a sample input tensor
x = torch.randn(1, dim, dim)

# Pass the tensor through the MambaBlock
output = mamba_block(x)
print("Output shape:", output.shape)

```

### `SSM`
```python
import torch 
from simple_mamba import SSM

# # Example usage
vocab_size = 10000  # Example vocabulary size
embed_dim = 256  # Example embedding dimension
state_dim = 512  # State dimension
num_layers = 2  # Number of state-space layers

model = SSM(vocab_size, embed_dim, state_dim, num_layers)

# Example input (sequence of word indices)
input_seq = torch.randint(
     0, vocab_size, (32, 10)
 )  # Batch size of 32, sequence length of 10

# Forward pass
logits = model(input_seq)
print(logits.shape)  # Should be [32, 10, vocab_size]

```

# License
MIT

# Citation
```bibtex
@misc{gu2023mamba,
    title={Mamba: Linear-Time Sequence Modeling with Selective State Spaces}, 
    author={Albert Gu and Tri Dao},
    year={2023},
    eprint={2312.00752},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

```

SimpleMamba

SimpleMamba 최신버젼 다운로드

오늘의 추천 프로젝트

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SimpleMamba

SimpleMamba 최신버젼 다운로드

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