diff --git a/examples/eaft/eaft-example.yml b/examples/eaft/eaft-example.yml
new file mode 100644
index 000000000..fed4179d2
--- /dev/null
+++ b/examples/eaft/eaft-example.yml
@@ -0,0 +1,77 @@
+base_model: google/gemma-3-1b-it
+
+model_type: Gemma3ForCausalLM
+cls_model_config: Gemma3TextConfig
+
+# gemma3 doesn't seem to play nice with ddp
+ddp_find_unused_parameters: true
+
+chat_template: gemma3
+eot_tokens:
+  - <end_of_turn>
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: cgato/SlimOrcaDedupCleaned
+    type: chat_template
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+
+dataset_prepared_path:
+val_set_size: 0
+output_dir: ./outputs/eaft-gemma-3-1b
+
+use_eaft: true
+eaft_alpha: 1.0
+eaft_k: 20
+
+sequence_len: 1024
+sample_packing: false
+
+adapter:
+lora_model_dir:
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+eval_batch_size: 1
+max_steps: 1000
+evaluation_strategy: "no"
+optimizer: adamw_torch_fused
+lr_scheduler: cosine
+learning_rate: 5e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_ratio: 0.1
+weight_decay: 0.0
+debug:
+deepspeed:
+fsdp:
+fsdp_config:
+special_tokens:
diff --git a/src/axolotl/core/builders/causal.py b/src/axolotl/core/builders/causal.py
index 3a9f8ba1b..09bcff450 100644
--- a/src/axolotl/core/builders/causal.py
+++ b/src/axolotl/core/builders/causal.py
@@ -373,6 +373,18 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html
             data_collator_kwargs["pad_to_multiple_of"] = multiple
 
+        if self.cfg.use_eaft:
+            from functools import partial
+
+            from axolotl.monkeypatch.loss.eaft import eaft_loss
+
+            configured_eaft_loss = partial(
+                eaft_loss,
+                alpha=self.cfg.eaft_alpha if self.cfg.eaft_alpha is not None else 1.0,
+                k=self.cfg.eaft_k if self.cfg.eaft_k is not None else 20,
+            )
+            trainer_kwargs["compute_loss_func"] = configured_eaft_loss
+
         trainer_cls = self._get_trainer_cls()
 
         trainer_kwargs, trainer_cls = self.hook_pre_create_trainer(
diff --git a/src/axolotl/monkeypatch/loss/eaft.py b/src/axolotl/monkeypatch/loss/eaft.py
new file mode 100644
index 000000000..150d4a005
--- /dev/null
+++ b/src/axolotl/monkeypatch/loss/eaft.py
@@ -0,0 +1,51 @@
+"""
+eaft (entropy-aware focal training) loss implementation
+weights examples by entropy approximation from top-k logits
+
+Reference: https://github.com/ymxyll/LlamaFactory-EAFT/blob/e2ce19e8efcc226450ee8f2b81dfe4e69f1f945d/src/llamafactory/train/trainer_utils.py
+"""
+
+import torch
+import torch.nn.functional as F
+
+
+def eaft_loss(outputs, labels, num_items_in_batch=None, alpha=1.0, k=20):
+    """
+    compute eaft loss with entropy weighting
+
+    args:
+        outputs: model outputs containing logits
+        labels: target labels for computing loss
+        num_items_in_batch: for sample packing support
+        alpha: exponent for entropy weighting (default 1.0)
+        k: number of top logits for entropy approximation (default 20)
+    """
+    logits = outputs.logits
+
+    shift_logits = logits[..., :-1, :].contiguous()
+    shift_labels = labels[..., 1:].contiguous()
+
+    vocab_size = shift_logits.size(-1)
+    shift_logits_view = shift_logits.view(-1, vocab_size)
+    shift_labels_view = shift_labels.view(-1)
+
+    mask = shift_labels_view != -100
+
+    with torch.no_grad():
+        top_k_logits, _ = torch.topk(
+            shift_logits_view[mask].float(), k=min(k, vocab_size), dim=-1
+        )
+        top_k_probs = F.softmax(top_k_logits, dim=-1)
+        entropy = -(top_k_probs * torch.log(top_k_probs + 1e-10)).sum(dim=-1)
+        weights = torch.pow(entropy, alpha)
+
+    loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
+    per_token_loss = loss_fct(shift_logits_view[mask], shift_labels_view[mask])
+    weighted_loss = per_token_loss * weights
+
+    if num_items_in_batch is not None:
+        loss = weighted_loss.sum() / num_items_in_batch
+    else:
+        loss = weighted_loss.mean()
+
+    return loss
diff --git a/src/axolotl/utils/schemas/config.py b/src/axolotl/utils/schemas/config.py
index da21df7aa..3621c0d89 100644
--- a/src/axolotl/utils/schemas/config.py
+++ b/src/axolotl/utils/schemas/config.py
@@ -676,6 +676,24 @@ class AxolotlInputConfig(
             "description": "Number of chunks to use for chunked cross entropy loss"
         },
     )
+    use_eaft: bool | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Enable Entropy-Aware Focal Training loss (EAFT)"
+        },
+    )
+    eaft_alpha: float | None = Field(
+        default=1.0,
+        json_schema_extra={
+            "description": "Exponent for entropy weighting in EAFT (default: 1.0)"
+        },
+    )
+    eaft_k: int | None = Field(
+        default=20,
+        json_schema_extra={
+            "description": "Number of top logits for entropy approximation (default: 20)"
+        },
+    )
 
     tiled_mlp: bool | None = Field(
         default=None,