@option(Configs.model)
def feedback_transformer_kv(c: Configs):
    '\n    Create [updated feedback transformer](index.html#kv_shared), with precalculated keys and values.\n    '
    from labml_nn.transformers.feedback import FeedbackTransformerKV, FeedbackTransformerLayer, FeedbackAttention, FeedForward
    return AutoregressiveModel(c.n_tokens, c.d_model, FeedbackTransformerKV(FeedbackTransformerLayer(d_model=c.d_model, attn=FeedbackAttention(c.heads, c.d_model, c.dropout, is_kv_precomputed=True), feed_forward=FeedForward(c.d_model, c.d_ff, c.dropout), dropout_prob=c.dropout), c.n_layers, c.d_model, c.heads)).to(c.device)
Create [updated feedback transformer](index.html#kv_shared), with precalculated keys and values.
@option(Configs.model)
def feedback_transformer_kv(c: Configs):
    '\n    \n    '
    from labml_nn.transformers.feedback import FeedbackTransformerKV, FeedbackTransformerLayer, FeedbackAttention, FeedForward
    return AutoregressiveModel(c.n_tokens, c.d_model, FeedbackTransformerKV(FeedbackTransformerLayer(d_model=c.d_model, attn=FeedbackAttention(c.heads, c.d_model, c.dropout, is_kv_precomputed=True), feed_forward=FeedForward(c.d_model, c.d_ff, c.dropout), dropout_prob=c.dropout), c.n_layers, c.d_model, c.heads)).to(c.device)