diff --git a/src/axolotl/cli/__init__.py b/src/axolotl/cli/__init__.py
index d07b10ce3..278a67474 100644
--- a/src/axolotl/cli/__init__.py
+++ b/src/axolotl/cli/__init__.py
@@ -202,7 +202,7 @@ def do_inference(
         )
     elif cfg.chat_template:
         chat_template_str = get_chat_template(cfg.chat_template)
-    elif cfg.datasets[0].type == "chat_template":
+    elif cfg.datasets and cfg.datasets[0].type == "chat_template":
         chat_template_str = get_chat_template_from_config(
             cfg=cfg, ds_cfg=cfg.datasets[0], tokenizer=tokenizer
         )
diff --git a/src/axolotl/cli/integrations/convert_diff_transformer.py b/src/axolotl/cli/integrations/convert_diff_transformer.py
index 03126f3bf..ecde82251 100644
--- a/src/axolotl/cli/integrations/convert_diff_transformer.py
+++ b/src/axolotl/cli/integrations/convert_diff_transformer.py
@@ -87,8 +87,6 @@ def convert_diff_transformer(cfg, cli_args, config_path):
             zero_init=cli_args.zero_init,
             sublayer_norm=cli_args.sublayer_norm,
             split_heads=cli_args.split_heads,
-            init_scale=cli_args.init_scale,
-            reinit_lambda_init=cli_args.reinit_lambda_init,
         )
         model = LlamaDifferentialForCausalLM.from_llama(model, config)
         model.to(cfg.device, dtype=cfg.torch_dtype)
diff --git a/src/axolotl/common/cli.py b/src/axolotl/common/cli.py
index 8f0d6bb77..ebe098ca6 100644
--- a/src/axolotl/common/cli.py
+++ b/src/axolotl/common/cli.py
@@ -54,10 +54,8 @@ class ConvertDiffTransformerCliArgs:
 
     debug: bool = field(default=False)
     zero_init: bool = field(default=False)
-    sublayer_norm: bool = field(default=False)
+    sublayer_norm: bool = field(default=True)
     split_heads: bool = field(default=False)
-    init_scale: float = field(default=1e-6)
-    reinit_lambda_init: bool = field(default=True)
 
 
 def load_model_and_tokenizer(
diff --git a/src/axolotl/integrations/diff_transformer/__init__.py b/src/axolotl/integrations/diff_transformer/__init__.py
index 1dbae22c4..e0ebec818 100644
--- a/src/axolotl/integrations/diff_transformer/__init__.py
+++ b/src/axolotl/integrations/diff_transformer/__init__.py
@@ -1,8 +1,13 @@
 """Definition of differential transformer plugin."""
 
 import logging
+from typing import List
+
+from transformers import PreTrainedModel, TrainerCallback
 
 from axolotl.integrations.base import BasePlugin
+from axolotl.utils.callbacks.differential import DifferentialAttentionMonitorCallback
+from axolotl.utils.dict import DictDefault
 
 LOG = logging.getLogger(__name__)
 
@@ -10,10 +15,41 @@ LOG = logging.getLogger(__name__)
 class DifferentialTransformerPlugin(BasePlugin):
     """Plugin for differential transformer integration with Axolotl."""
 
-    def __init__(self):
+    def __init__(self) -> None:
+        """
+        Constructor for differential transformers plugin. Calls `register_diff_attn`
+        to register differential attention custom modeling implementation to `AutoConfig`
+        and `AutoModel`.
+        """
         from .modeling_diff_attn import register_diff_attn
 
         register_diff_attn()
 
-    def get_input_args(self):
+    def get_input_args(self) -> str:
+        """Returns module path to diff transformer plugin args for `axolotl` config."""
         return "axolotl.integrations.diff_transformer.args.DifferentialTransformerArgs"
+
+    def add_callbacks_pre_trainer(
+        self, cfg: DictDefault, model: PreTrainedModel
+    ) -> List[TrainerCallback]:
+        """
+        Returns `DifferentialAttentionMonitorCallback` to be added to the list of
+        callbacks for the `axolotl` trainer if wandb usage is enabled.
+
+        Parameters:
+            cfg: Dictionary mapping `axolotl` config keys to values.
+            model: The loaded mfodel.
+
+        Returns:
+            A list (possibly) containing an instantiated `DifferentialAttentionMonitorCallback`.
+        """
+        callbacks = []
+        if cfg.use_wandb:
+            callbacks.append(
+                DifferentialAttentionMonitorCallback(
+                    log_every=cfg.diff_attn_log_every,
+                    num_monitor_layers=cfg.diff_attn_num_monitor_layers,
+                )
+            )
+
+        return callbacks
diff --git a/src/axolotl/integrations/diff_transformer/args.py b/src/axolotl/integrations/diff_transformer/args.py
index 47c1fe110..fe3e7d977 100644
--- a/src/axolotl/integrations/diff_transformer/args.py
+++ b/src/axolotl/integrations/diff_transformer/args.py
@@ -12,3 +12,5 @@ class DifferentialTransformerArgs(BaseModel):
     """Input args for differential transformer."""
 
     diff_attention: Optional[bool] = None
+    diff_attn_log_every: Optional[int] = 100
+    diff_attn_num_monitor_layers: Optional[int] = 3
diff --git a/src/axolotl/integrations/diff_transformer/diff_attn.py b/src/axolotl/integrations/diff_transformer/diff_attn.py
index a03a3fb00..cc3e8f90c 100644
--- a/src/axolotl/integrations/diff_transformer/diff_attn.py
+++ b/src/axolotl/integrations/diff_transformer/diff_attn.py
@@ -1,9 +1,10 @@
-"""Re-implemention of differential attention."""
+"""Re-implemention of differential attention from the Differential Transformer paper
+(https://arxiv.org/abs/2410.05258)."""
 # pylint: disable=invalid-name
 
 import logging
 import math
-from typing import Any, Optional, Tuple
+from typing import Any
 
 import torch
 import torch.nn.functional as F
@@ -27,6 +28,18 @@ except ImportError:
 
 
 def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    Repeats key/value heads to match the number of query heads in multi-head attention.
+
+    Args:
+        x: Input tensor of shape `(batch_size, num_kv_heads, seq_len, head_dim)`.
+        n_rep: Number of times to repeat each head.
+
+    Returns:
+        Tensor with repeated heads of shape `(batch_size, num_kv_heads * n_rep,
+            seq_len, head_dim)`.
+        If `n_rep` is 1, returns the input tensor unchanged.
+    """
     batch_size, n_kv_heads, slen, head_dim = x.shape
     if n_rep == 1:
         return x
@@ -37,14 +50,48 @@ def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
     )
 
 
-def lambda_init_fn(depth):
+def lambda_init_fn(depth: int) -> float:
+    """
+    Lambda mixing parameter init function from the "Differential Transformer" paper.
+
+    Args:
+        depth: Index of layer to init lambda parameter.
+
+    Returns:
+        Lambda initialization value (decreasing with `depth`).
+    """
     return 0.8 - 0.6 * math.exp(-0.3 * depth)
 
 
 class LlamaDifferentialAttentionBase(nn.Module):
-    """Base class for differential attention implementations."""
+    """
+    Base class for differential attention implementations.
 
-    def __init__(self, config: Any, layer_idx: int):
+    This class implements the core differential attention mechanism used in Llama models.
+    It supports both split heads and double projection modes for attention computation.
+    """
+
+    def __init__(self, config: Any, layer_idx: int) -> None:
+        """
+        Initializes the differential attention module.
+
+        Args:
+            config: Model configuration object containing hyperparameters, including:
+                - hidden_size: The size of hidden states
+                - num_attention_heads: Number of attention heads
+                - num_key_value_heads: Number of key/value heads
+                - attention_bias: Whether to use bias in attention projections
+                - split_heads: Whether to use split heads mode
+                - rms_norm_eps: Epsilon for RMS normalization
+            layer_idx: The index of this layer in the model
+
+        Note:
+            The initialization process consists of four steps:
+            1. Configuration initialization (`_init_config`)
+            2. Projection layers initialization (`_init_projections`)
+            3. Differential parameters initialization (`_init_differential_params`)
+            4. Normalization layers initialization (`_init_normalization`)
+        """
         super().__init__()
 
         self.config = config
@@ -53,8 +100,24 @@ class LlamaDifferentialAttentionBase(nn.Module):
         self._init_differential_params()
         self._init_normalization()
 
-    def _init_config(self, layer_idx: int):
-        """Initialize configuration parameters."""
+        # For logging
+        self.attn1 = None
+        self.attn2 = None
+        self.lambda_full = None
+
+    def _init_config(self, layer_idx: int) -> None:
+        """
+        Initializes configuration parameters for the attention layer. Sets up various
+        dimension sizes and head counts based on the provided config. Handles both
+        split heads and double projection modes.
+
+        Args:
+            layer_idx: Index of the current layer.
+
+        Note:
+            In split heads mode, the number of heads is divided by 2 (rounding down),
+            which differs from the original implementation that required an even number.
+        """
         self.head_dim = self.config.hidden_size // self.config.num_attention_heads
         self.base_num_heads = self.config.num_attention_heads
         self.base_num_kv_heads = self.config.num_key_value_heads
@@ -62,26 +125,26 @@ class LlamaDifferentialAttentionBase(nn.Module):
         self.layer_idx = layer_idx
 
         if self.config.split_heads:
-            # Split heads mode - single projections
-            # NOTE: This rounds down `base_num_heads / 2` as opposed to the original
-            # implementation, which asserts `self.base_num_heads` is even
             self.heads_per_component = self.base_num_heads // 2
             self.kv_heads_per_component = self.base_num_kv_heads // 2
             self.value_head_dim = 2 * self.head_dim
         else:
-            # Double projection mode
             self.heads_per_component = self.base_num_heads
             self.kv_heads_per_component = self.base_num_kv_heads
             self.value_head_dim = self.head_dim
 
-    def _init_projections(self):
-        """Initialize Q, K, V projections."""
+    def _init_projections(self) -> None:
+        """
+        Initializes the query, key, value, and output projection layers.
+
+        Creates linear transformations for Q, K, V projections with dimensions
+        depending on whether split heads or double projection mode is used.
+        The output projection combines the attention heads back to model dimension.
+        """
         if self.config.split_heads:
-            # Split heads mode - single projections
             q_out_dim = self.config.hidden_size
             k_out_dim = self.head_dim * self.base_num_kv_heads
         else:
-            # Double projection mode
             q_out_dim = self.config.hidden_size * 2
             k_out_dim = self.head_dim * self.base_num_kv_heads * 2
 
@@ -102,8 +165,15 @@ class LlamaDifferentialAttentionBase(nn.Module):
             bias=self.config.attention_bias,
         )
 
-    def _init_differential_params(self):
-        """Initialize differential attention parameters."""
+    def _init_differential_params(self) -> None:
+        """
+        Initializes parameters specific to differential attention.
+
+        Creates learnable parameters for the differential attention mechanism:
+        - Lambda parameters for queries and keys
+        - Initial lambda value based on layer index
+        - Rotary position embedding layer
+        """
         self.lambda_init = nn.Parameter(
             torch.full((), lambda_init_fn(self.layer_idx)),
             requires_grad=False,
@@ -122,19 +192,42 @@ class LlamaDifferentialAttentionBase(nn.Module):
         )
         self.rotary_emb = LlamaRotaryEmbedding(config=self.config)
 
-    def _init_normalization(self):
-        """Initialize normalization layers."""
+    def _init_normalization(self) -> None:
+        """
+        Initializes normalization layers for the attention mechanism.
+
+        Sets up either RMS normalization or identity transformation based on config.
+        The normalization is applied to the sublayer output if enabled.
+        """
         sublayer_norm = getattr(self.config, "sublayer_norm", True)
         if sublayer_norm:
             self.subln = LlamaRMSNorm(self.value_head_dim, eps=self.config.rms_norm_eps)
         else:
             self.subln = nn.Identity()
 
-    def _prepare_attention_inputs(self, hidden_states: torch.Tensor):
-        """Prepare inputs for attention computation."""
+    def _prepare_attention_inputs(
+        self, hidden_states: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Prepares input tensors for attention computation.
+
+        Projects input hidden states to query, key, and value spaces, then reshapes
+        them for multi-head attention processing.
+
+        Args:
+            hidden_states: Input tensor of shape `(batch_size, seq_len,
+            hidden_size)`.
+
+        Returns:
+            tuple: Tuple containing:
+                - q1: Positive attention query component
+                - q2: Negative attention query component
+                - k1: Positive attention key component
+                - k2: Negative attention key component
+                - v: Value tensor
+        """
         bsz, q_len, _ = hidden_states.size()
 
-        # Project and split
         q = self.q_proj(hidden_states)
         k = self.k_proj(hidden_states)
         v = self.v_proj(hidden_states)
@@ -158,9 +251,41 @@ class LlamaDifferentialAttentionBase(nn.Module):
         return q1, q2, k1, k2, v
 
     def _apply_rotary_embeddings(
-        self, q1, q2, k1, k2, position_ids, position_embeddings
-    ):
-        """Apply rotary embeddings to queries and keys."""
+        self,
+        q1: torch.Tensor,
+        q2: torch.Tensor,
+        k1: torch.Tensor,
+        k2: torch.Tensor,
+        position_ids: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None,
+    ) -> tuple[
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+    ]:
+        """
+        Applies rotary positional embeddings to queries and keys.
+
+        Args:
+            q1: Positive attention query component.
+            q2: Negative attention query component.
+            k1: Positive attention key component.
+            k2: Negative attention key component.
+            position_ids: Token position indices.
+            position_embeddings: Pre-computed rotary embeddings (cos, sin).
+
+        Returns:
+            tuple: Tuple containing:
+                - q1: Positive attention query with positional encoding.
+                - q2: Negative attention query with positional encoding.
+                - k1: Positive attention key with positional encoding.
+                - k2: Negative attention key with positional encoding.
+                - cos: Cosine part of rotary embeddings.
+                - sin: Sine part of rotary embeddings.
+        """
         if position_embeddings is None:
             LOG.warning(
                 "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
@@ -177,14 +302,36 @@ class LlamaDifferentialAttentionBase(nn.Module):
 
         return q1, q2, k1, k2, cos, sin
 
-    def _handle_cache(self, k1, k2, v, past_key_value, cache_kwargs):
-        """Handle caching for autoregressive generation."""
+    def _handle_cache(
+        self,
+        k1: torch.Tensor,
+        k2: torch.Tensor,
+        v: torch.Tensor,
+        past_key_value: Cache | None,
+        cache_kwargs: dict,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Handles key-value caching for autoregressive generation and the repetition of
+        key-value heads to match the number of query heads.
+
+        Args:
+            k1: Positive attention key component.
+            k2: Negative attention key component.
+            v: Value tensor.
+            past_key_value: Cache object for storing previous key-value pairs.
+            cache_kwargs: Additional arguments for cache handling.
+
+        Returns:
+            tuple: Tuple containing:
+                - k1: Processed positive attention key component.
+                - k2: Processed negative attention key component.
+                - v: Processed value tensor.
+        """
         if past_key_value is not None:
             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 number of query heads
         k1 = repeat_kv(k1, self.num_key_value_groups)
         k2 = repeat_kv(k2, self.num_key_value_groups)
         v = repeat_kv(v, self.num_key_value_groups)
@@ -193,39 +340,90 @@ class LlamaDifferentialAttentionBase(nn.Module):
 
         return k1, k2, v
 
-    def _compute_lambda(self, q1):
-        """Compute lambda values for differential attention."""
+    def _compute_lambda(self, q1: torch.Tensor) -> torch.Tensor:
+        """
+        Computes lambda values for differential attention.
+
+        The lambda value is computed as λ₁ - λ₂ + λ_init, where λ₁ and λ₂ are
+        computed from the learned parameters.
+
+        Args:
+            q1: Positive attention query component, used for type casting.
+
+        Returns:
+            Computed lambda value for differential attention.
+        """
         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)
+
         return lambda_1 - lambda_2 + self.lambda_init
 
-    def _process_attention_output(self, attn, bsz, q_len):
-        """Process and project attention output."""
+    def _process_attention_output(
+        self, attn: torch.Tensor, bsz: int, q_len: int
+    ) -> torch.Tensor:
+        """
+        Processes and projects the attention output. Applies sublayer normalization,
+        scales by (1 - λ_init), and projects back to model dimension.
+
+        Args:
+            attn: Raw attention output.
+            bsz: Batch size.
+            q_len: Query sequence length.
+
+        Returns:
+            Processed attention output of shape (batch_size, seq_len, hidden_size)
+        """
         attn = self.subln(attn)
         attn = attn * (1 - self.lambda_init)
         attn = attn.transpose(1, 2).reshape(bsz, q_len, self.config.hidden_size)
+
         return self.o_proj(attn)
 
 
 class LlamaDifferentialAttention(LlamaDifferentialAttentionBase):
-    """Standard implementation of differential attention."""
+    """
+    Standard implementation of differential attention.
+
+    This class implements the standard differential attention mechanism using
+    explicit matrix multiplications for the attention computation.
+    """
 
     def forward(
         self,
         hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_value: Cache | None = 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,
+        cache_position: torch.LongTensor | None = None,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
         **kwargs,  # pylint: disable=unused-argument
     ):
+        """
+        Computes differential attention using standard matrix multiplication operations.
+
+        Args:
+            hidden_states: Input tensor containing sequence to attend to.
+            attention_mask: Mask to avoid attention on padding tokens.
+            position_ids: Indices of positions for positional embeddings.
+            past_key_value: Cached key and value tensors for autoregressive decoding.
+            output_attentions: Whether to return attention weights.
+            use_cache: Whether to use cached key/value states.
+            cache_position: Position indices for cached states.
+            position_embeddings: Pre-computed positional embeddings.
+            **kwargs: Additional arguments passed to the forward call.
+
+        Returns:
+            tuple containing:
+                - Output tensor after attention computation.
+                - Attention weights if output_attentions is True, else None.
+                - Updated key-value cache if use_cache is True, else None.
+        """
         bsz, q_len, _ = hidden_states.size()
         q1, q2, k1, k2, v = self._prepare_attention_inputs(hidden_states)
         q1, q2, k1, k2, cos, sin = self._apply_rotary_embeddings(
@@ -255,6 +453,11 @@ class LlamaDifferentialAttention(LlamaDifferentialAttentionBase):
         attn = torch.matmul(attn1, v) - lambda_full * torch.matmul(attn2, v)
         attn = self._process_attention_output(attn, bsz, q_len)
 
+        # Save for logging
+        self.attn1 = attn1
+        self.attn2 = attn2
+        self.lambda_full = lambda_full
+
         if output_attentions:
             attn_weights = attn1 - lambda_full * attn2
             attn_weights = attn_weights.view(bsz, self.heads_per_component, q_len, -1)
@@ -263,27 +466,53 @@ class LlamaDifferentialAttention(LlamaDifferentialAttentionBase):
 
 
 class LlamaDifferentialSdpaAttention(LlamaDifferentialAttentionBase):
-    """SDPA-based implementation of differential attention."""
+    """
+    SDPA-based implementation of differential attention.
+
+    This class implements differential attention using PyTorch's scaled_dot_product_attention
+    for improved performance on supported hardware.
+    """
 
-    # pylint: disable=duplicate-code
     def forward(
         self,
         hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_value: Cache | None = None,
         output_attentions: bool = False,
         use_cache: bool = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        cache_position: torch.LongTensor | None = None,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
         **kwargs,  # pylint: disable=unused-argument
     ):
+        """
+        Computes differential attention using PyTorch's scaled dot product attention.
+
+        Args:
+            hidden_states: Input tensor containing sequence to attend to.
+            attention_mask: Mask to avoid attention on padding tokens.
+            position_ids: Indices of positions for positional embeddings.
+            past_key_value: Cached key and value tensors for autoregressive decoding.
+            output_attentions: Whether to return attention weights.
+            use_cache: Whether to use cached key/value states.
+            cache_position: Position indices for cached states.
+            position_embeddings: Pre-computed positional embeddings.
+            **kwargs: Additional arguments passed to the forward call.
+
+        Returns:
+            tuple containing:
+                - Output tensor after attention computation.
+                - None for attention weights (SDPA doesn't support output_attentions).
+                - Updated key-value cache if use_cache is True, else None.
+        """
         if output_attentions:
             LOG.warning(
                 "LlamaDifferentialModel is using LlamaDifferentialSdpaAttention, but "
                 + "`torch.nn.functional.scaled_dot_product_attention` does not support "
                 + "`output_attentions=True`. Falling back to the eager attention implementation."
             )
+
+            # pylint: disable=duplicate-code
             return LlamaDifferentialAttention.forward(
                 self,
                 hidden_states,
@@ -326,15 +555,35 @@ class LlamaDifferentialSdpaAttention(LlamaDifferentialAttentionBase):
 
         lambda_full = self._compute_lambda(q1)
         attn = attn1 - lambda_full * attn2
-
         attn = self._process_attention_output(attn, bsz, q_len)
+
+        # Save for logging
+        self.attn1 = attn1
+        self.attn2 = attn2
+        self.lambda_full = lambda_full
+
         return attn, None, past_key_value
 
 
 class LlamaDifferentialFlashAttention2(LlamaDifferentialAttentionBase):
-    """Flash Attention 2-based implementation of differential attention."""
+    """
+    Flash Attention 2-based implementation of differential attention.
+
+    This class implements differential attention using Flash Attention 2 for maximum
+    performance on supported hardware.
+    """
 
     def __init__(self, *args, **kwargs):
+        """
+        Initializes the Flash Attention 2 differential attention module.
+
+        Args:
+            *args: Positional arguments passed to parent class.
+            **kwargs: Keyword arguments passed to parent class.
+
+        Raises:
+            ImportError: If flash-attn library is not installed.
+        """
         if not FLASH_ATTENTION_AVAILABLE:
             raise ImportError(
                 "LlamaDifferentialFlashAttention2 requires flash-attn library. "
@@ -343,25 +592,46 @@ class LlamaDifferentialFlashAttention2(LlamaDifferentialAttentionBase):
 
         super().__init__(*args, **kwargs)
 
-    # pylint: disable=duplicate-code
     def forward(
         self,
         hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_value: Cache | None = None,
         output_attentions: bool = False,
         use_cache: bool = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        cache_position: torch.LongTensor | None = None,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
         **kwargs,  # pylint: disable=unused-argument
     ):
+        """
+        Computes differential attention using Flash Attention 2.
+
+        Args:
+            hidden_states: Input tensor containing sequence to attend to.
+            attention_mask: Mask to avoid attention on padding tokens.
+            position_ids: Indices of positions for positional embeddings.
+            past_key_value: Cached key and value tensors for autoregressive decoding.
+            output_attentions: Whether to return attention weights.
+            use_cache: Whether to use cached key/value states.
+            cache_position: Position indices for cached states.
+            position_embeddings: Pre-computed positional embeddings.
+            **kwargs: Additional arguments passed to the forward call.
+
+        Returns:
+            tuple containing:
+                - Output tensor after attention computation.
+                - None for attention weights (Flash Attention doesn't support output_attentions).
+                - Updated key-value cache if use_cache is True, else None.
+        """
         if output_attentions:
             LOG.warning(
                 "LlamaDifferentialModel is using LlamaDifferentialFlashAttention2, but "
                 + "flash attenion does not support `output_attentions=True`. Falling back "
                 + "to the eager attention implementation."
             )
+
+            # pylint: disable=duplicate-code
             return LlamaDifferentialAttention.forward(
                 self,
                 hidden_states,
@@ -407,6 +677,11 @@ class LlamaDifferentialFlashAttention2(LlamaDifferentialAttentionBase):
 
         lambda_full = self._compute_lambda(q1)
         attn = attn1 - lambda_full * attn2
-
         attn = self._process_attention_output(attn, bsz, q_len)
+
+        # Save for logging
+        self.attn1 = attn1
+        self.attn2 = attn2
+        self.lambda_full = lambda_full
+
         return attn, None, past_key_value
diff --git a/src/axolotl/integrations/diff_transformer/modeling_diff_attn.py b/src/axolotl/integrations/diff_transformer/modeling_diff_attn.py
index e0f4eebc1..c8a663cb3 100644
--- a/src/axolotl/integrations/diff_transformer/modeling_diff_attn.py
+++ b/src/axolotl/integrations/diff_transformer/modeling_diff_attn.py
@@ -1,7 +1,11 @@
-"""Modeling for differential transformers."""
+"""
+Modeling for differential transformers.
+
+This module implements differential attention variants of the LLaMA model,
+providing various attention implementations for improved performance.
+"""
 
 import logging
-from typing import Optional, Union
 
 import torch
 from transformers import AutoConfig, AutoModel, AutoModelForCausalLM
@@ -18,7 +22,12 @@ logger = logging.getLogger(__name__)
 
 
 class LlamaDifferentialConfig(LlamaConfig):
-    """Configuration class for Differential LLaMA model."""
+    """
+    Configuration class for Differential LLaMA model.
+
+    Extends the base LLaMA configuration with additional parameters for differential
+    attention mechanisms.
+    """
 
     model_type = "llama-differential"
 
@@ -29,6 +38,15 @@ class LlamaDifferentialConfig(LlamaConfig):
         zero_init: bool = False,
         **kwargs,
     ):
+        """
+        Initialize differential LLaMA configuration.
+
+        Args:
+            split_heads: Whether to use split heads mode for attention computation.
+            sublayer_norm: Whether to apply normalization to sublayers.
+            zero_init: Whether to initialize new weights to zero.
+            **kwargs: Additional arguments passed to LlamaConfig.
+        """
         super().__init__(**kwargs)
         self.split_heads = split_heads
         self.sublayer_norm = sublayer_norm
@@ -42,12 +60,26 @@ class LlamaDifferentialConfig(LlamaConfig):
 
 
 class LlamaDifferentialModel(LlamaModel):
-    """LlamaModel with differential attention."""
+    """
+    LlamaModel with differential attention.
+
+    This class extends the base LLaMA model by replacing standard attention with
+    differential attention mechanisms.
+    """
 
     config_class = LlamaDifferentialConfig
     base_model_prefix = "llama_differential"
 
-    def __init__(self, config):
+    def __init__(self, config: LlamaDifferentialConfig):
+        """
+        Initialize a differential LLaMA model.
+
+        Args:
+            config: Configuration object for the model.
+
+        Raises:
+            ValueError: If specified attention implementation is not supported.
+        """
         super().__init__(config)
 
         # Handle attention implementation
@@ -76,11 +108,26 @@ class LlamaDifferentialModel(LlamaModel):
         for idx, layer in enumerate(self.layers):
             layer.self_attn = attn_class(config, idx)
 
-    # pylint: disable=protected-access
     @classmethod
+    # pylint: disable=protected-access
     def _autoset_attn_implementation(
-        cls, config, **kwargs
-    ):  # pylint: disable=unused-argument
+        cls,
+        config: LlamaDifferentialConfig,
+        **kwargs,  # pylint: disable=unused-argument
+    ) -> LlamaDifferentialConfig:
+        """
+        Automatically set the attention implementation based on config.
+
+        Args:
+            config: Model configuration object.
+            **kwargs: Additional arguments (unused).
+
+        Returns:
+            Updated configuration object.
+
+        Raises:
+            ValueError: If specified attention implementation is not supported.
+        """
         config._attn_implementation_autoset = True
         attn_implementation = getattr(config, "_attn_implementation", None)
 
@@ -110,10 +157,23 @@ class LlamaDifferentialModel(LlamaModel):
     @classmethod
     def from_llama(
         cls,
-        model: Union[LlamaModel, LlamaForCausalLM],
-        config: Optional[LlamaDifferentialConfig] = None,
+        model: LlamaModel | LlamaForCausalLM,
+        config: LlamaDifferentialConfig | None = None,
     ) -> "LlamaDifferentialModel":
-        """Convert a LlamaModel to use differential attention."""
+        """
+        Convert a `LlamaModel` to use differential attention.
+
+        Args:
+            model: Base LLaMA model to convert.
+            config: Configuration for differential attention. If `None`, created from
+                base model config.
+
+        Returns:
+            Converted model with differential attention.
+
+        Raises:
+            ValueError: If number of heads is not even when using `split_heads` mode.
+        """
         logger.info(f"Converting {type(model).__name__} to {cls.__name__}")
 
         # Handle LlamaForCausalLM
@@ -182,7 +242,6 @@ class LlamaDifferentialModel(LlamaModel):
 
                 if config.zero_init:
                     logger.debug(f"Layer {layer_idx}: Zero initializing")
-                    # Zero out components as needed
                     with torch.no_grad():
                         new_layer.self_attn.q_proj.weight.data[old_q_size:].zero_()
                         new_layer.self_attn.k_proj.weight.data[old_k_size:].zero_()
@@ -192,45 +251,60 @@ class LlamaDifferentialModel(LlamaModel):
                         new_layer.self_attn.lambda_k2.zero_()
                         new_layer.self_attn.lambda_init.zero_()
                 else:
-                    logger.debug(
-                        f"Layer {layer_idx}: Initializing with scale {config.init_scale}"
+                    # Mirror weights for second component
+                    new_layer.self_attn.q_proj.weight.data[old_q_size:].copy_(
+                        old_layer.self_attn.q_proj.weight.data
+                    )
+                    new_layer.self_attn.k_proj.weight.data[old_k_size:].copy_(
+                        old_layer.self_attn.k_proj.weight.data
                     )
-                    # Initialize with small random values
-                    with torch.no_grad():
-                        new_layer.self_attn.q_proj.weight.data[old_q_size:].normal_(
-                            0, config.init_scale
-                        )
-                        new_layer.self_attn.k_proj.weight.data[old_k_size:].normal_(
-                            0, config.init_scale
-                        )
-                        new_layer.self_attn.lambda_q1.normal_(0, config.init_scale)
-                        new_layer.self_attn.lambda_k1.normal_(0, config.init_scale)
-                        new_layer.self_attn.lambda_q2.normal_(0, config.init_scale)
-                        new_layer.self_attn.lambda_k2.normal_(0, config.init_scale)
-                        if config.reinit_lambda_init:
-                            new_layer.self_attn.lambda_init.normal_(
-                                0, config.init_scale
-                            ).abs_()
 
         logger.info("Conversion complete")
+
         return new_model
 
 
 class LlamaDifferentialForCausalLM(LlamaForCausalLM):
-    """LlamaForCausalLM with differential attention."""
+    """
+    `LlamaForCausalLM` with differential attention.
+
+    This class extends the base LLaMA causal language model by incorporating
+    differential attention mechanisms.
+    """
 
     config_class = LlamaDifferentialConfig
     base_model_prefix = "llama_differential"
 
-    def __init__(self, config):
+    def __init__(self, config: LlamaDifferentialConfig):
+        """
+        Initialize a differential LLaMA model for causal language modeling.
+
+        Args:
+            config: Configuration object for the model.
+        """
         super().__init__(config)
         self.model = LlamaDifferentialModel(config)
 
-    # pylint: disable=protected-access
     @classmethod
+    # pylint: disable=protected-access
     def _autoset_attn_implementation(
-        cls, config, **kwargs
-    ):  # pylint: disable=unused-argument
+        cls,
+        config: LlamaDifferentialConfig,
+        **kwargs,  # pylint: disable=unused-argument
+    ) -> LlamaDifferentialConfig:
+        """
+        Automatically set the attention implementation based on config.
+
+        Args:
+            config: Model configuration object.
+            **kwargs: Additional arguments (unused).
+
+        Returns:
+            Updated configuration object.
+
+        Raises:
+            ValueError: If specified attention implementation is not supported.
+        """
         config._attn_implementation_autoset = True
         attn_implementation = getattr(config, "_attn_implementation", None)
 
@@ -239,6 +313,7 @@ class LlamaDifferentialForCausalLM(LlamaForCausalLM):
             config._attn_implementation = config._attn_implementations[
                 attn_implementation
             ]
+
             return config
 
         # If no mapping, validate it's a valid differential type
@@ -259,9 +334,22 @@ class LlamaDifferentialForCausalLM(LlamaForCausalLM):
 
     @classmethod
     def from_llama(
-        cls, model: LlamaForCausalLM, config: Optional[LlamaDifferentialConfig] = None
+        cls, model: LlamaForCausalLM, config: LlamaDifferentialConfig | None = None
     ) -> "LlamaDifferentialForCausalLM":
-        """Convert a LlamaForCausalLM to use differential attention."""
+        """
+        Convert a `LlamaForCausalLM` to use differential attention.
+
+        Args:
+            model: Base LLaMA model to convert.
+            config: Configuration for differential attention. If `None`, created from
+                base model config.
+
+        Returns:
+            Converted model with differential attention.
+
+        Raises:
+            ValueError: If number of heads is not even when using `split_heads` mode.
+        """
         if config is None:
             config = LlamaDifferentialConfig(**model.config.__dict__)
 
@@ -285,7 +373,14 @@ class LlamaDifferentialForCausalLM(LlamaForCausalLM):
         return new_model
 
 
-def register_diff_attn():
+def register_diff_attn() -> None:
+    """
+    Register differential attention components with the transformers library.
+
+    This function registers the differential attention configurations and model classes
+    with the Auto* classes from `transformers`, making them available through the
+    standard model loading pipeline.
+    """
     # Register configs
     AutoConfig.register("llama-differential", LlamaDifferentialConfig)
 
diff --git a/src/axolotl/utils/callbacks/differential.py b/src/axolotl/utils/callbacks/differential.py
new file mode 100644
index 000000000..49d9cd7e3
--- /dev/null
+++ b/src/axolotl/utils/callbacks/differential.py
@@ -0,0 +1,171 @@
+"""
+Monitor and log differential attention components during training.
+
+This module provides a callback for tracking the behavior of differential attention
+mechanisms, including lambda parameters and attention statistics.
+"""
+
+from typing import Any
+
+import torch
+import wandb
+from transformers import TrainerCallback
+
+from axolotl.utils.distributed import is_main_process
+
+
+class DifferentialAttentionMonitorCallback(TrainerCallback):
+    """
+    Callback to monitor differential attention components and lambda parameters.
+
+    This callback tracks attention statistics across all layers and provides detailed
+    monitoring for a specified number of layers evenly spaced through the model.
+    """
+
+    def __init__(self, log_every: int = 250, num_monitor_layers: int = 3):
+        """
+        Initialize the differential attention monitor.
+
+        Args:
+            log_every: Number of steps between logging events.
+            num_monitor_layers: Number of individual layers to monitor in detail.
+        """
+        self.log_every = log_every
+        self.num_monitor_layers = num_monitor_layers
+        self.monitor_layers: list[int] | None = None  # Will be set in on_train_begin
+
+    # pylint: disable=unused-argument
+    def on_train_begin(
+        self,
+        args: Any,
+        state: Any,
+        control: Any,
+        model: torch.nn.Module,
+        **kwargs,
+    ) -> None:
+        """
+        Set up layer monitoring at the start of training.
+
+        Args:
+            args: Training arguments.
+            state: Training state.
+            control: Training control object.
+            model: The model being trained.
+            **kwargs: Additional arguments passed by the trainer.
+        """
+        if is_main_process():
+            num_layers = len(model.model.layers)
+            self.num_monitor_layers = min(self.num_monitor_layers, num_layers)
+
+            stride = (
+                (num_layers - 1) / (self.num_monitor_layers - 1)
+                if self.num_monitor_layers > 1
+                else 0
+            )
+            self.monitor_layers = [
+                round(i * stride) for i in range(self.num_monitor_layers)
+            ]
+            print(f"Monitoring layers {self.monitor_layers} in detail")
+
+    # pylint: disable=unused-argument
+    def on_step_end(
+        self, args: Any, state: Any, control: Any, model: torch.nn.Module, **kwargs
+    ) -> None:
+        """
+        Log attention metrics at the end of each step.
+
+        Collects and logs:
+            - Lambda parameter norms and values.
+            - Attention statistics (mean and std).
+            - Both per-layer and aggregate metrics.
+
+        Args:
+            args: Training arguments.
+            state: Training state.
+            control: Training control object.
+            model: The model being trained.
+            **kwargs: Additional arguments passed by the trainer.
+        """
+        if not is_main_process() or state.global_step % self.log_every != 0:
+            return
+
+        assert self.monitor_layers is not None
+
+        # Aggregate stats across all layers
+        all_q1_norms = []
+        all_q2_norms = []
+        all_k1_norms = []
+        all_k2_norms = []
+        all_lambda1 = []
+        all_lambda2 = []
+        all_lambda_full = []
+
+        metrics = {}
+
+        for layer_idx, layer in enumerate(model.model.layers):
+            attn = layer.self_attn
+
+            # Collect stats for aggregation
+            all_q1_norms.append(attn.lambda_q1.norm().item())
+            all_q2_norms.append(attn.lambda_q2.norm().item())
+            all_k1_norms.append(attn.lambda_k1.norm().item())
+            all_k2_norms.append(attn.lambda_k2.norm().item())
+
+            lambda1 = torch.exp(torch.sum(attn.lambda_q1 * attn.lambda_k1)).item()
+            lambda2 = torch.exp(torch.sum(attn.lambda_q2 * attn.lambda_k2)).item()
+            all_lambda1.append(lambda1)
+            all_lambda2.append(lambda2)
+            all_lambda_full.append(attn.lambda_full)
+
+            # Log detailed metrics for monitored layers
+            if layer_idx in self.monitor_layers:
+                metrics.update(
+                    {
+                        f"layer_{layer_idx}/lambda_q1_norm": attn.lambda_q1.norm().item(),
+                        f"layer_{layer_idx}/lambda_k1_norm": attn.lambda_k1.norm().item(),
+                        f"layer_{layer_idx}/lambda_q2_norm": attn.lambda_q2.norm().item(),
+                        f"layer_{layer_idx}/lambda_k2_norm": attn.lambda_k2.norm().item(),
+                        f"layer_{layer_idx}/lambda1": lambda1,
+                        f"layer_{layer_idx}/lambda2": lambda2,
+                        f"layer_{layer_idx}/lambda_init": attn.lambda_init.item(),
+                        f"layer_{layer_idx}/lambda_full": lambda1
+                        - lambda2
+                        + attn.lambda_init.item(),
+                        f"layer_{layer_idx}/attn1_mean": attn.attn1.mean().item(),
+                        f"layer_{layer_idx}/attn2_mean": attn.attn2.mean().item(),
+                        f"layer_{layer_idx}/attn1_std": attn.attn1.std().item(),
+                        f"layer_{layer_idx}/attn2_std": attn.attn2.std().item(),
+                    }
+                )
+
+        # Add aggregate metrics
+        metrics.update(
+            {
+                "aggregate/lambda_q1_norm_mean": torch.tensor(all_q1_norms)
+                .mean()
+                .item(),
+                "aggregate/lambda_q1_norm_std": torch.tensor(all_q1_norms).std().item(),
+                "aggregate/lambda_q2_norm_mean": torch.tensor(all_q2_norms)
+                .mean()
+                .item(),
+                "aggregate/lambda_q2_norm_std": torch.tensor(all_q2_norms).std().item(),
+                "aggregate/lambda_k1_norm_mean": torch.tensor(all_k1_norms)
+                .mean()
+                .item(),
+                "aggregate/lambda_k1_norm_std": torch.tensor(all_k1_norms).std().item(),
+                "aggregate/lambda_k2_norm_mean": torch.tensor(all_k2_norms)
+                .mean()
+                .item(),
+                "aggregate/lambda_k2_norm_std": torch.tensor(all_k2_norms).std().item(),
+                "aggregate/lambda1_mean": torch.tensor(all_lambda1).mean().item(),
+                "aggregate/lambda1_std": torch.tensor(all_lambda1).std().item(),
+                "aggregate/lambda2_mean": torch.tensor(all_lambda2).mean().item(),
+                "aggregate/lambda2_std": torch.tensor(all_lambda2).std().item(),
+                "aggregate/lambda_full_mean": torch.tensor(all_lambda_full)
+                .mean()
+                .item(),
+                "aggregate/lambda_full_std": torch.tensor(all_lambda_full).std().item(),
+            }
+        )
+
+        wandb.log(metrics, step=state.global_step)
diff --git a/tests/e2e/integrations/convert_diff_transformer/test_convert_diff_transformer.py b/tests/e2e/integrations/convert_diff_transformer/test_convert_diff_transformer.py
index 92c8053c0..e1ad31fdd 100644
--- a/tests/e2e/integrations/convert_diff_transformer/test_convert_diff_transformer.py
+++ b/tests/e2e/integrations/convert_diff_transformer/test_convert_diff_transformer.py
@@ -69,7 +69,7 @@ def test_conversion_cli_debug(tmp_path: Path, base_config):
         yaml.dump(base_config, file)
 
     cfg = load_cfg(str(config_path))
-    cli_args = ConvertDiffTransformerCliArgs(debug=True, init_scale=0.1)
+    cli_args = ConvertDiffTransformerCliArgs(debug=True)
     _, debug_info = convert_diff_transformer(cfg, cli_args, str(config_path))
 
     assert not debug_info["generations_match"]