diff --git a/scripts/convert_diff_transformer.py b/scripts/convert_diff_transformer.py
new file mode 100644
index 000000000..651c0a229
--- /dev/null
+++ b/scripts/convert_diff_transformer.py
@@ -0,0 +1,127 @@
+"""Test conversion of transformers model attention to differential attention."""
+from typing import Tuple
+
+import torch
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    PreTrainedModel,
+    PreTrainedTokenizer,
+)
+
+from axolotl.integrations.diff_transformer.convert import convert_to_diff_attention
+
+
+def setup_model(
+    model_name: str, device: str = "cuda"
+) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
+    """Load model and tokenizer"""
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.float16,
+        device_map=device,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+
+    return model, tokenizer
+
+
+def convert_model_attention(model: AutoModelForCausalLM) -> AutoModelForCausalLM:
+    """Convert model to use differential attention"""
+    try:
+        model = convert_to_diff_attention(model)
+        return model
+    except Exception as exception:
+        print(f"Error during model conversion: {exception}")
+        raise
+
+
+def test_inference(model: PreTrainedModel, tokenizer: PreTrainedTokenizer) -> None:
+    """Run test inference"""
+    # Test prompts
+    test_prompts = [
+        "The quick brown fox",
+    ]
+
+    for prompt in test_prompts:
+        try:
+            # Tokenize
+            inputs = tokenizer(prompt, return_tensors="pt")
+            inputs = {k: v.to(model.device) for k, v in inputs.items()}
+
+            # Generate
+            from time import time
+
+            start = time()
+            with torch.no_grad():
+                outputs = model.generate(
+                    **inputs,
+                    max_new_tokens=20,
+                    num_beams=1,
+                    do_sample=False,
+                    # temperature=0.7,
+                    pad_token_id=tokenizer.pad_token_id,
+                    use_cache=False,
+                    # use_cache=True,
+                )
+            elasped = time() - start
+            print(f"generation time: {elasped}s")
+
+            # Decode
+            print(outputs)
+            generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+            print(f"\nPrompt: {prompt}")
+            print(f"Generated: {generated_text}\n")
+
+        except Exception as exception:
+            print(f"Error during inference: {str(exception)}")
+            raise
+
+
+def save_converted_model(model: AutoModelForCausalLM, output_dir: str) -> None:
+    """Save the converted model"""
+    print(f"Saving converted model to {output_dir}")
+    model.save_pretrained(output_dir)
+
+
+def main():
+    # Configuration
+    model_name = "HuggingFaceTB/SmolLM2-135M"
+    # model_name = "openlm-research/open_llama_3b_v2"
+    output_dir = "./converted_model"
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"Using device: {device}")
+
+    try:
+        # Load model and tokenizer
+        model, tokenizer = setup_model(model_name, device)
+
+        # Print original model info
+        print("Original model config:")
+        print(f"\t- Hidden size: {model.config.hidden_size}")
+        print(f"\t- Number of attention heads: {model.config.num_attention_heads}")
+
+        # Test the original model
+        test_inference(model, tokenizer)
+
+        # Convert to differential attention
+        model = convert_to_diff_attention(model)
+        model.to(model.device)
+        print("Model conversion completed")
+
+        # Test the converted model
+        test_inference(model, tokenizer)
+
+        # Save converted model
+        save_converted_model(model, output_dir)
+
+    except Exception as exception:
+        print(f"Error during test: {str(exception)}")
+        raise
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/axolotl/integrations/diff_transformer/convert.py b/src/axolotl/integrations/diff_transformer/convert.py
index 93a8df073..36d97037b 100644
--- a/src/axolotl/integrations/diff_transformer/convert.py
+++ b/src/axolotl/integrations/diff_transformer/convert.py
@@ -1,20 +1,81 @@
 """Differential attention conversion logic for a huggingface pre-trained model."""
 import logging
+from typing import Union
 
+import torch
+from torch import nn
 from transformers import PreTrainedModel
-from transformers.models.llama.modeling_llama import LlamaAttention
+from transformers.models.llama.modeling_llama import LlamaAttention, LlamaSdpaAttention
 from transformers.models.mistral.modeling_mistral import MistralAttention
 from transformers.models.mixtral.modeling_mixtral import MixtralAttention
 
-from .multihead_diffattn import DifferentialAttention
+from .multihead_diffattn import (
+    LlamaDifferentialAttention,
+    LlamaDifferentialSdpaAttention,
+)
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 
+def copy_attention_weights(
+    old_attn: Union[LlamaAttention, LlamaSdpaAttention],
+    new_attn: Union[LlamaDifferentialAttention, LlamaDifferentialSdpaAttention],
+    zero_init: bool = True,
+) -> None:
+    """
+    Copy weights from old attention layer to new differential attention layer.
+    Copies old weights to Q1 and K1, zeros out Q2 and K2 for exact equivalence
+    to original attention mechanism.
+    """
+    # For Q projection (Q1 and Q2)
+    new_q = torch.empty_like(new_attn.q_proj.weight.data)
+    new_q[: new_attn.hidden_size] = old_attn.q_proj.weight.data  # Q1
+    if zero_init:
+        new_q[new_attn.hidden_size :] = 0
+    else:
+        nn.init.normal_(new_q[new_attn.hidden_size :], mean=0, std=0.1)
+    new_attn.q_proj.weight.data.copy_(new_q)
+
+    # For K projection (K1 and K2)
+    old_kv_size = old_attn.k_proj.weight.data.size(0)  # Size for 3 heads
+    new_k = torch.empty_like(new_attn.k_proj.weight.data)
+    new_k[:old_kv_size] = old_attn.k_proj.weight.data  # K1
+    if zero_init:
+        new_k[old_kv_size:] = 0
+    else:
+        nn.init.normal_(new_k[old_kv_size:], mean=0, std=0.1)
+    new_attn.k_proj.weight.data.copy_(new_k)
+
+    # For V projection (single V)
+    new_attn.v_proj.weight.data.copy_(old_attn.v_proj.weight.data)
+
+    # Output projection remains the same
+    new_attn.o_proj.weight.data.copy_(old_attn.o_proj.weight.data)
+
+    # Zero out lambda parameters for exact equivalence
+    if zero_init:
+        nn.init.zeros_(new_attn.lambda_q1)
+        nn.init.zeros_(new_attn.lambda_k1)
+        nn.init.zeros_(new_attn.lambda_q2)
+        nn.init.zeros_(new_attn.lambda_k2)
+        new_attn.lambda_init = 0.0
+
+    logger.debug(
+        "Copied positive attention weights from %s to %s",
+        type(old_attn).__name__,
+        type(new_attn).__name__,
+    )
+
+
 def convert_to_diff_attention(model: PreTrainedModel) -> PreTrainedModel:
     """Convert a pre-trained model's attention layers to differential attention"""
-    attention_patterns = (LlamaAttention, MistralAttention, MixtralAttention)
+    attention_patterns = (
+        LlamaAttention,
+        LlamaSdpaAttention,
+        MistralAttention,
+        MixtralAttention,
+    )
     layer_idx = 0
 
     # Get model dtype from existing weights
@@ -29,13 +90,22 @@ def convert_to_diff_attention(model: PreTrainedModel) -> PreTrainedModel:
                 layer_type = type(child).__name__
                 logger.info(f"Converting attention layer {layer_idx}: {layer_type}")
 
+                # Choose appropriate differential attention class
+                if isinstance(child, LlamaSdpaAttention):
+                    attention_class = LlamaDifferentialSdpaAttention
+                else:
+                    attention_class = LlamaDifferentialAttention
+
                 # Create new diff attn layer
-                new_attention = DifferentialAttention(
+                new_attention = attention_class(
                     config=module.config if hasattr(module, "config") else model.config,
                     layer_idx=layer_idx,
                     dtype=model_dtype,
                 )
 
+                # Copy weights from old attention to new attention
+                copy_attention_weights(child, new_attention)
+
                 # Replace the layer
                 setattr(module, name, new_attention)
                 layer_idx += 1
diff --git a/src/axolotl/integrations/diff_transformer/multihead_diffattn.py b/src/axolotl/integrations/diff_transformer/multihead_diffattn.py
index 00462475e..6d3bc7589 100644
--- a/src/axolotl/integrations/diff_transformer/multihead_diffattn.py
+++ b/src/axolotl/integrations/diff_transformer/multihead_diffattn.py
@@ -6,9 +6,9 @@ from typing import Any, Optional, Tuple
 
 import torch
 import torch.nn.functional as F
+import transformers
 from torch import nn
 from transformers.cache_utils import Cache
-from transformers.models.llama.modeling_llama import LlamaRMSNorm as RMSNorm
 from transformers.models.llama.modeling_llama import (
     LlamaRotaryEmbedding,
     apply_rotary_pos_emb,
@@ -34,7 +34,7 @@ def lambda_init_fn(depth):
     return 0.8 - 0.6 * math.exp(-0.3 * depth)
 
 
-class DifferentialAttention(nn.Module):
+class LlamaDifferentialAttention(nn.Module):
     """Differential Attention implementation as described in the Diff Transformer paper.
 
     This implements a modified attention mechanism that computes the difference between
@@ -54,7 +54,6 @@ class DifferentialAttention(nn.Module):
         config: Model configuration object containing hidden size, number of heads etc.
         layer_idx: Index of this layer in the transformer stack
         dtype: Data type for the layer parameters
-        is_causal: Whether to use causal (masked) attention
     """
 
     def __init__(
@@ -62,43 +61,52 @@ class DifferentialAttention(nn.Module):
         config: Any,
         layer_idx: int,
         dtype: torch.dtype,
-        is_causal: bool = True,
     ):
         super().__init__()
 
-        self.config = config
-        self.layer_idx = layer_idx
+        # Base model dimensions
+        self.attention_dropout = config.attention_dropout
         self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.is_causal = is_causal
-        # self.head_dim = self.hidden_size // self.num_heads
-        self.head_dim = self.hidden_size // self.num_heads // 2
-        self.num_key_value_heads = getattr(
-            config, "num_key_value_heads", self.num_heads
-        )
-        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
-        self.max_position_embeddings = config.max_position_embeddings
-        self.scaling = (self.head_dim) ** -0.5
+        self.base_num_heads = config.num_attention_heads
+        self.base_num_kv_heads = config.num_key_value_heads
+        self.head_dim = config.hidden_size // config.num_attention_heads
 
-        # Initialize projections with correct dtype
+        self.scaling = self.head_dim**-0.5
+        self.layer_idx = layer_idx
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+
+        # For Q1 and Q2
         self.q_proj = nn.Linear(
-            self.hidden_size, self.hidden_size, bias=False, dtype=dtype
+            self.hidden_size,
+            self.hidden_size * 2,
+            bias=False,
+            dtype=dtype,
         )
+
+        # For K1 and K2
         self.k_proj = nn.Linear(
             self.hidden_size,
-            self.hidden_size // self.num_key_value_groups,
-            bias=False,
-            dtype=dtype,
-        )
-        self.v_proj = nn.Linear(
-            self.hidden_size,
-            self.hidden_size // self.num_key_value_groups,
+            self.hidden_size // self.base_num_heads * self.base_num_kv_heads * 2,
             bias=False,
             dtype=dtype,
         )
 
+        # Single V projection
+        self.v_proj = nn.Linear(
+            self.hidden_size,
+            self.hidden_size // self.base_num_heads * self.base_num_kv_heads,
+            bias=False,
+            dtype=dtype,
+        )
+
+        # Output projection
         self.o_proj = nn.Linear(
-            self.hidden_size, self.hidden_size, bias=False, dtype=dtype
+            self.hidden_size,
+            self.hidden_size,
+            bias=False,
+            dtype=dtype,
         )
 
         # Initialize differential attention parameters
@@ -116,7 +124,6 @@ class DifferentialAttention(nn.Module):
             torch.zeros(self.head_dim, dtype=dtype).normal_(mean=0, std=0.1)
         )
 
-        self.subln = RMSNorm(2 * self.head_dim, eps=1e-5)
         self.rotary_emb = LlamaRotaryEmbedding(config=config)
 
     def forward(
@@ -126,6 +133,7 @@ class DifferentialAttention(nn.Module):
         position_ids: Optional[torch.LongTensor] = None,
         past_key_value: Optional[Cache] = None,
         output_attentions: bool = False,
+        use_cache: bool = False,  # pylint: disable=unused-argument
         cache_position: Optional[torch.LongTensor] = None,
         position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         **kwargs,  # pylint: disable=unused-argument
@@ -134,97 +142,261 @@ class DifferentialAttention(nn.Module):
         Optional[torch.Tensor],
         Optional[tuple[torch.Tensor, torch.Tensor]],
     ]:
-        bsz, tgt_len, _ = hidden_states.size()
+        bsz, q_len, _ = hidden_states.size()
 
-        # Project queries, keys and values
-        q = self.q_proj(hidden_states)
-        k = self.k_proj(hidden_states)
+        # Project to Q1,Q2 and K1,K2
+        qp = self.q_proj(hidden_states)
+        kp = self.k_proj(hidden_states)
         v = self.v_proj(hidden_states)
 
-        # Reshape for attention
-        q = q.view(bsz, tgt_len, 2 * self.num_heads, self.head_dim).transpose(1, 2)
-        k = k.view(bsz, tgt_len, 2 * self.num_key_value_heads, self.head_dim).transpose(
-            1, 2
-        )
-        v = v.view(bsz, tgt_len, self.num_key_value_heads, 2 * self.head_dim).transpose(
-            1, 2
-        )
+        # Split into Q1,Q2 and K1,K2
+        q1, q2 = qp.chunk(2, dim=-1)
+        k1, k2 = kp.chunk(2, dim=-1)
 
-        # Generate or unpack cos, sin for rotary positional embeddings
+        # Reshape Q1,Q2 for attention
+        q1 = q1.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+        q2 = q2.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+
+        # Reshape K1,K2 for attention
+        k1 = k1.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+        k2 = k2.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+
+        # Reshape V
+        v = v.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+
+        # Apply rotary embeddings
         if position_embeddings is None:
             if position_ids is None:
-                position_ids = torch.arange(
-                    0, tgt_len, dtype=torch.long, device=q.device
-                )
-            cos, sin = self.rotary_emb(q, position_ids)
+                position_ids = torch.arange(q_len, device=q1.device)
+            cos, sin = self.rotary_emb(q1, position_ids)
         else:
             cos, sin = position_embeddings
 
-        # Need to adjust cos, sin to match the halved head_dim
-        cos = cos[..., : self.head_dim]
-        sin = sin[..., : self.head_dim]
-        q, k = apply_rotary_pos_emb(q, k, cos, sin)
+        q1, k1 = apply_rotary_pos_emb(q1, k1, cos, sin)
+        q2, k2 = apply_rotary_pos_emb(q2, k2, cos, sin)
 
         if past_key_value is not None:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
             cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-
-            # Update cache and get back concatenated states
+            k = torch.stack([k1, k2], dim=1)
             k, v = past_key_value.update(k, v, self.layer_idx, cache_kwargs)
+            k1, k2 = k.unbind(dim=1)
 
-        # Prepare for attention
-        k = repeat_kv(k, self.num_key_value_groups)
-        v = repeat_kv(v, self.num_key_value_groups)
+        # Repeat KV heads to match Q heads
+        k1 = repeat_kv(k1, self.base_num_heads // self.base_num_kv_heads)
+        k2 = repeat_kv(k2, self.base_num_heads // self.base_num_kv_heads)
+        v = repeat_kv(v, self.base_num_heads // self.base_num_kv_heads)
 
-        # Scale query
-        q = q * self.scaling
+        # Calculate attention scores for both parts
+        # NOTE(Dan): the Differential Transformers paper scales by a constant scaling factor
+        # instead of sqrt(head_dim). This could be set on the class as `self.scaling`.
+        attn_weights1 = torch.matmul(q1, k1.transpose(-1, -2)) / math.sqrt(
+            self.head_dim
+        )
+        attn_weights2 = torch.matmul(q2, k2.transpose(-1, -2)) / math.sqrt(
+            self.head_dim
+        )
 
-        # Calculate attention scores
-        attn_weights = torch.matmul(q, k.transpose(-1, -2))
+        if attention_mask is not None:
+            causal_mask = attention_mask[:, :, :, : k1.shape[-2]]
+            attn_weights1 = attn_weights1 + causal_mask
+            attn_weights2 = attn_weights2 + causal_mask
 
-        # Apply causal mask
-        if attention_mask is None:
-            attention_mask = torch.triu(
-                torch.full((tgt_len, tgt_len), float("-inf"), device=q.device),
-                diagonal=1,
-            ).type_as(attn_weights)
-        attn_weights = torch.nan_to_num(attn_weights)
-        attn_weights = attn_weights + attention_mask
-
-        # Apply softmax
-        attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).type_as(
-            attn_weights
+        # Apply softmax separately as per paper
+        attn_weights1 = F.softmax(attn_weights1, dim=-1, dtype=torch.float32).type_as(
+            attn_weights1
+        )
+        attn_weights2 = F.softmax(attn_weights2, dim=-1, dtype=torch.float32).type_as(
+            attn_weights2
+        )
+        attn_weights1 = F.dropout(
+            attn_weights1, p=self.attention_dropout, training=self.training
+        )
+        attn_weights2 = F.dropout(
+            attn_weights2, p=self.attention_dropout, training=self.training
         )
 
         # Calculate lambda
         lambda_1 = torch.exp(
             torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1).float()
-        ).type_as(q)
+        ).type_as(q1)
         lambda_2 = torch.exp(
             torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1).float()
-        ).type_as(q)
+        ).type_as(q1)
         lambda_full = lambda_1 - lambda_2 + self.lambda_init
 
-        # Apply differential attention
-        attn_weights = attn_weights.view(
-            bsz, self.num_heads, 2, -1, attn_weights.size(-1)
-        )
-        attn_weights = attn_weights[:, :, 0] - lambda_full * attn_weights[:, :, 1]
+        # Compute differential attention (following paper's formula)
+        attn_weights = attn_weights1 - lambda_full * attn_weights2
 
-        # Apply attention to values
+        # Apply attention weights to values
         attn = torch.matmul(attn_weights, v)
 
-        # Apply sublayer norm
-        attn = self.subln(attn).type_as(attn)
+        # Apply sublayer norm and scaling
+        # NOTE(Dan): The differential transformers paper applies sublayer normalization at this
+        # point, but this is typically done outside of the attention layer. It would look something
+        # like: `attn = self.subln(attn).type_as(attn)`, using `LlamaRMSNorm` or similar.
         attn = attn * (1 - self.lambda_init)
 
-        # Reshape and project output
-        attn = attn.transpose(1, 2).reshape(
-            bsz, tgt_len, self.num_heads * 2 * self.head_dim
-        )
+        # Reshape to output
+        attn = attn.transpose(1, 2).reshape(bsz, q_len, self.hidden_size)
         attn = self.o_proj(attn)
 
-        # Return in exact format expected by LLaMA
         if output_attentions:
             return attn, attn_weights, past_key_value
         return attn, None, past_key_value
+
+
+class LlamaDifferentialSdpaAttention(LlamaDifferentialAttention):
+    """Differential Attention implementation as described in the Diff Transformer paper.
+    This implements the same logic as `LlamaDifferentialAttention`, but uses
+    `scaled_dot_product_attention` instead of "manually" computing it under the hood.
+
+    This implements a modified attention mechanism that computes the difference between
+    two attention patterns, scaled by learned lambda parameters. The mechanism helps
+    reduce noise in the attention weights for irrelevant / less relevant tokens.
+
+    Key components:
+    - Split head dimension for differential computation
+    - Learned lambda parameters that control attention scaling
+    - Sublayer normalization on the attention output
+
+    See:
+    - https://arxiv.org/abs/2410.05258
+    - https://github.com/microsoft/unilm/tree/master/Diff-Transformer
+
+    Args:
+        config: Model configuration object containing hidden size, number of heads etc.
+        layer_idx: Index of this layer in the transformer stack
+        dtype: Data type for the layer parameters
+    """
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,  # [bsz, seq_len, hidden_size]
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        **kwargs,  # pylint: disable=unused-argument
+    ) -> tuple[
+        torch.Tensor,
+        Optional[torch.Tensor],
+        Optional[tuple[torch.Tensor, torch.Tensor]],
+    ]:
+        if output_attentions:
+            transformers.logger.warning_once(
+                "LlamaModel is using LlamaSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        # Project to Q1,Q2 and K1,K2
+        qp = self.q_proj(hidden_states)
+        kp = self.k_proj(hidden_states)
+        v = self.v_proj(hidden_states)
+
+        # Split into Q1,Q2 and K1,K2
+        q1, q2 = qp.chunk(2, dim=-1)
+        k1, k2 = kp.chunk(2, dim=-1)
+
+        # Reshape Q1,Q2 for attention
+        q1 = q1.view(bsz, q_len, self.base_num_heads, self.head_dim).transpose(1, 2)
+        q2 = q2.view(bsz, q_len, self.base_num_heads, self.head_dim).transpose(1, 2)
+        # Reshape K1,K2 for attention
+        k1 = k1.view(bsz, q_len, self.base_num_kv_heads, self.head_dim).transpose(1, 2)
+        k2 = k2.view(bsz, q_len, self.base_num_kv_heads, self.head_dim).transpose(1, 2)
+        # Reshape V
+        v = v.view(bsz, q_len, self.base_num_kv_heads, self.head_dim).transpose(1, 2)
+
+        # Apply rotary embeddings
+        if position_embeddings is None:
+            if position_ids is None:
+                position_ids = torch.arange(q_len, device=q1.device)
+            cos, sin = self.rotary_emb(q1, position_ids)
+        else:
+            cos, sin = position_embeddings
+
+        q1, k1 = apply_rotary_pos_emb(q1, k1, cos, sin)
+        q2, k2 = apply_rotary_pos_emb(q2, k2, cos, sin)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            k = torch.stack([k1, k2], dim=1)
+            k, v = past_key_value.update(k, v, self.layer_idx, cache_kwargs)
+            k1, k2 = k.unbind(dim=1)
+
+        # Repeat KV heads to match Q heads
+        k1 = repeat_kv(k1, self.base_num_heads // self.base_num_kv_heads)
+        k2 = repeat_kv(k2, self.base_num_heads // self.base_num_kv_heads)
+        v = repeat_kv(v, self.base_num_heads // self.base_num_kv_heads)
+
+        causal_mask = None
+        if attention_mask is not None:
+            causal_mask = attention_mask
+            causal_mask = causal_mask[:, :, :, : k1.shape[-2]]
+
+        # SDPA with memory-efficient backend requires contiguous inputs on CUDA
+        if q1.device.type == "cuda" and causal_mask is not None:
+            q1, q2 = q1.contiguous(), q2.contiguous()
+            k1, k2 = k1.contiguous(), k2.contiguous()
+            v = v.contiguous()
+
+        # Calculate attention using SDPA
+        is_causal = attention_mask is None and q_len > 1
+
+        attn_output1 = F.scaled_dot_product_attention(
+            q1,
+            k1,
+            v,
+            attn_mask=causal_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            is_causal=is_causal,
+        )
+        attn_output2 = F.scaled_dot_product_attention(
+            q2,
+            k2,
+            v,
+            attn_mask=causal_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            is_causal=is_causal,
+        )
+
+        # Calculate lambda
+        lambda_1 = torch.exp(
+            torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1).float()
+        ).type_as(q1)
+        lambda_2 = torch.exp(
+            torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1).float()
+        ).type_as(q1)
+        lambda_full = lambda_1 - lambda_2 + self.lambda_init
+
+        # Combine the attention outputs
+        attn = attn_output1 - lambda_full * attn_output2
+
+        # Apply sublayer norm and scaling
+        attn = attn * (1 - self.lambda_init)
+
+        # Reshape to output
+        attn = attn.transpose(1, 2).reshape(bsz, q_len, self.hidden_size)
+        attn = self.o_proj(attn)
+
+        if output_attentions:
+            return (
+                attn,
+                None,
+                past_key_value,
+            )  # Note: can't return attn_weights with SDPA
+        return attn, None, past_key_value