Recursively find the layers of a certain type in a module. Args: module (nn.Module): PyTorch module. layers (list): List of

Rotates half the hidden dims of the input.

Performs a single optimization step. Arguments: closure (`Callable`, *optional*): A closure that reevaluates the model a

Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.

Make causal mask used for bi-directional self-attention.

Format & tokenize it so it is ready for training :param tokenizer (AutoTokenizer): Model Tokenizer :param max_length (int): Maximum number of

LlamaRMSNorm is equivalent to T5LayerNorm

Args: adjust_step: useful when continuing training from a warmed up checkpoint, it allows to sync the resets by reducing the

Format various fields of the sample ('instruction', 'context', 'response') Then concatenate them using two newline characters :param sam

Format various fields of the sample ('instruction', 'context', 'response') Then concatenate them using two newline characters :param sam

Recursively find the layers of a certain type in a module. Args: module (nn.Module): PyTorch module. layers (list): List of

Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (