integrations.liger.models.qwen3_5

integrations.liger.models.qwen3_5

Liger FLCE for Qwen3.5. Based on transformers v5.3.0.

Functions

Name	Description
apply_liger_kernel_to_qwen3_5	Apply Liger kernels to replace original implementation in HuggingFace Qwen3.5 models.
lce_forward

apply_liger_kernel_to_qwen3_5

integrations.liger.models.qwen3_5.apply_liger_kernel_to_qwen3_5(
    cross_entropy=False,
    fused_linear_cross_entropy=False,
    rms_norm=False,
    rms_norm_gated=False,
    glu_activation=False,
    layer_norm=False,
    **kwargs,
)

Apply Liger kernels to replace original implementation in HuggingFace Qwen3.5 models.

Note: Qwen3_5RMSNorm uses zero-init weight with offset 1.0 (like Gemma), so we use LigerRMSNorm with offset=1.0 and init_fn=“zeros”.

Parameters

Name	Type	Description	Default
cross_entropy	bool	Whether to apply Liger’s cross entropy loss. Default is False.	False
fused_linear_cross_entropy	bool	Whether to apply Liger’s fused linear cross entropy loss. Default is False. cross_entropy and fused_linear_cross_entropy cannot both be True. If fused_linear_cross_entropy is True, the logits will not be materialized but more memory efficient.	False
rms_norm	bool	Whether to apply Liger’s RMSNorm. Default is False.	False
rms_norm_gated	bool	Whether to apply fused RMSNorm+SiLU gate kernel for Qwen3_5RMSNormGated (used in linear attention layers). Default is False.	False
glu_activation	bool	Whether to apply Liger’s SwiGLU MLP. Default is False.	False
layer_norm	bool	Whether to apply Liger’s LayerNorm. Default is False.	False

lce_forward

integrations.liger.models.qwen3_5.lce_forward(
    self,
    input_ids=None,
    attention_mask=None,
    position_ids=None,
    past_key_values=None,
    inputs_embeds=None,
    labels=None,
    use_cache=None,
    cache_position=None,
    logits_to_keep=0,
    **kwargs,
)

Parameters

Name	Type	Description	Default
labels	torch.LongTensor of shape (batch_size, sequence_length), optional	Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].	None
logits_to_keep	int or torch.Tensor, optional	If an int, compute logits for the last logits_to_keep tokens. If 0, calculate logits for all input_ids (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a torch.Tensor, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length).	0

Returns: