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:
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:
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:
attn_layers = Decoder(
...
attn_flash=True,
...
)
Usage example:
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.