lora_gemma¶
- torchtune.models.gemma.lora_gemma(lora_attn_modules: List[Literal['q_proj', 'k_proj', 'v_proj', 'output_proj']], apply_lora_to_mlp: bool = False, *, vocab_size: int, num_layers: int, num_heads: int, head_dim: int, num_kv_heads: int, embed_dim: int, intermediate_dim: int, max_seq_len: int, attn_dropout: float = 0.0, norm_eps: float = 1e-06, rope_base: int = 10000, norm_embeddings: bool = True, lora_rank: int, lora_alpha: float, lora_dropout: float = 0.0, use_dora: bool = False, quantize_base: bool = False) TransformerDecoder[source]¶
Return a version of Gemma with LoRA applied based on the passed in configuration. Note: output projection lora is not supported because it is tied to token embeddings
- Parameters:
lora_attn_modules (List[LORA_ATTN_MODULES]) – list of which linear layers LoRA should be applied to in each self-attention block. Options are
{"q_proj", "k_proj", "v_proj", "output_proj"}.apply_lora_to_mlp (bool) – whether to apply LoRA to the MLP in each transformer layer. Default: False
vocab_size (int) – number of tokens in vocabulary.
num_layers (int) – number of layers in the transformer decoder.
num_heads (int) – number of query heads. For MHA this is also the number of heads for key and value
head_dim (int) – dimension of head
num_kv_heads (int) – number of key and value heads.
embed_dim (int) – embedding dimension for self-attention
intermediate_dim (int) – intermediate dimension for MLP
max_seq_len (int) – maximum sequence length the model will be run with,
attn_dropout (float) – dropout value passed onto scaled_dot_product_attention. Default: 0.0
norm_eps (float) – epsilon in RMS norms Default: 1e-6
rope_base (int) – base for the rotary positional embeddings. Default: 10_000
norm_embeddings (bool) – whether to apply layer norm before the self-attention and mlp layers. Default: True
lora_rank (int) – rank of each low-rank approximation
lora_alpha (float) – scaling factor for the low-rank approximation
lora_dropout (float) – LoRA dropout probability. Default: 0.0
use_dora (bool) – Decompose the LoRA weight into magnitude and direction, as introduced in “DoRA: Weight-Decomposed Low-Rank Adaptation” (https://arxiv.org/abs/2402.09353).
quantize_base – (bool): Whether to quantize base model weights or not. Only applied to base weights within linear layers LoRA is applied to. The final output linear projection is not supported for quantization currently.
- Returns:
Instantiation of Gemma model with LoRA applied to a subset of the attention projections in each layer.
- Return type:
