File size: 1,329 Bytes
ca4fc4d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
### Alibi Positional Bias
Alibi positional bias allows the model to learn relative positions between tokens, enabling it to better capture the relationships and dependencies between tokens in a sequence.
Usage example:
```python
attn_layers = Decoder(
...
alibi_pos_bias=True,
alibi_num_heads=4,
...
)
```
### Rotary Position Encodings (xpos)
Rotary position encodings introduce a more efficient way to encode positions in the input sequence. They avoid the need for absolute positional embeddings, reducing the model's memory footprint and improving training speed.
Usage example:
```python
attn_layers = Decoder(
...
rotary_xpos=True,
...
)
```
### Flash Attention
Flash attention speeds up the self-attention mechanism by reducing the number of attention computations. It accelerates training and inference while maintaining a high level of performance.
Usage example:
```python
attn_layers = Decoder(
...
attn_flash=True,
...
)
```
Usage example:
```python
attn_layers = Decoder(
...
deepnorm=True,
...
)
```
### Deep Normalization (deepnorm)
Deep normalization is a technique that normalizes the activations within a layer, helping with training stability and convergence. It allows the model to better learn complex patterns and generalize to unseen data. |