diff --git a/unsloth/__init__.py b/unsloth/__init__.py
index 745b210208..7e5e7192ff 100644
--- a/unsloth/__init__.py
+++ b/unsloth/__init__.py
@@ -55,7 +55,7 @@ else:
 pass
 
 # Reduce VRAM usage by reducing fragmentation
-os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True,roundup_power2_divisions:[64:64,256:32,>:16]"
 
 # Hugging Face Hub faster downloads
 if "HF_HUB_ENABLE_HF_TRANSFER" not in os.environ:
diff --git a/unsloth/models/llama.py b/unsloth/models/llama.py
index afff9ad9eb..cf1b5e4b9a 100644
--- a/unsloth/models/llama.py
+++ b/unsloth/models/llama.py
@@ -917,6 +917,9 @@ def LlamaModel_fast_forward_inference(
 pass
 
 
+from cut_cross_entropy import linear_cross_entropy
+
+
 def CausalLM_fast_forward(fast_forward_inference):
     def _CausalLM_fast_forward(
         self,
@@ -968,62 +971,75 @@ def CausalLM_fast_forward(fast_forward_inference):
             )
         pass
         hidden_states = outputs[0]
-        bsz, q_len, hd = hidden_states.shape
-        lm_head = self.lm_head.weight
-        if bsz == 1 and q_len == 1:
-            logits = torch.mv(lm_head, hidden_states.ravel().to(lm_head.dtype))
-            logits = logits.unsqueeze(0).unsqueeze(0)
-        elif num_logits_to_keep != 0:
-            logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :].to(lm_head.dtype))
-        else:
-            logits = self.lm_head(hidden_states.to(lm_head.dtype))
-        pass
 
-        torch_dtype = __DTYPE_MAP.get(self.config.torch_dtype, None)
-        if torch_dtype is not None:
-            logits = logits.to(torch_dtype)
-        else:
-            raise TypeError("Unsloth: torch_dtype for models is not bfloat16, float16 or float32!")
-        pass
+        loss = linear_cross_entropy(
+            hidden_states,
+            self.lm_head.weight,
+            targets = labels,
+            ignore_index = -100,
+            softcap = None,
+            reduction = "sum",
+            shift = True,
+            filter_eps = "auto",
+        ) / kwargs.get("num_items_in_batch", None) or kwargs.get("n_items", None)
+        logits = None
 
-        loss = None
-        logit_softcapping = getattr(self.config, "final_logit_softcapping", 0)
-        logit_scaling     = getattr(self.config, "logit_scale", 0)
-        if labels is not None:
-            shift_logits = logits
-            if not hasattr(self, "extra_ignored_labels"):
-                # Fixes https://github.com/unslothai/unsloth/issues/10
-                self.extra_ignored_labels = torch.full((self.max_seq_length, 1), -100, device = "cuda:0")
-            pass
+        # bsz, q_len, hd = hidden_states.shape
+        # lm_head = self.lm_head.weight
+        # if bsz == 1 and q_len == 1:
+        #     logits = torch.mv(lm_head, hidden_states.ravel().to(lm_head.dtype))
+        #     logits = logits.unsqueeze(0).unsqueeze(0)
+        # elif num_logits_to_keep != 0:
+        #     logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :].to(lm_head.dtype))
+        # else:
+        #     logits = self.lm_head(hidden_states.to(lm_head.dtype))
+        # pass
 
-            shift_labels = torch.hstack((labels[..., 1:], self.extra_ignored_labels[:labels.shape[0]]))
-            loss = fast_cross_entropy_loss(
-                logits = shift_logits,
-                labels = shift_labels,
-                logit_softcapping = logit_softcapping,
-                logit_scaling     = logit_scaling,
-                n_items           = kwargs.get("num_items_in_batch", None) or kwargs.get("n_items", None),
-            )
-        else:
-            if logit_scaling != 0:
-                if logits.requires_grad:
-                    logits = logit_scaling * logits
-                else:
-                    logits *= logit_scaling
-                pass
-            pass
-            if logit_softcapping != 0:
-                if logits.requires_grad:
-                    logits = (1.0 / logit_softcapping) * logits
-                    logits = torch.tanh(logits)
-                    logits = logit_softcapping * logits
-                else:
-                    logits *= (1.0 / logit_softcapping)
-                    torch.tanh(logits, out = logits)
-                    logits *= logit_softcapping
-                pass
-            pass
-        pass
+        # torch_dtype = __DTYPE_MAP.get(self.config.torch_dtype, None)
+        # if torch_dtype is not None:
+        #     logits = logits.to(torch_dtype)
+        # else:
+        #     raise TypeError("Unsloth: torch_dtype for models is not bfloat16, float16 or float32!")
+        # pass
+
+        # loss = None
+        # logit_softcapping = getattr(self.config, "final_logit_softcapping", 0)
+        # logit_scaling     = getattr(self.config, "logit_scale", 0)
+        # if labels is not None:
+        #     shift_logits = logits
+        #     if not hasattr(self, "extra_ignored_labels"):
+        #         # Fixes https://github.com/unslothai/unsloth/issues/10
+        #         self.extra_ignored_labels = torch.full((self.max_seq_length, 1), -100, device = "cuda:0")
+        #     pass
+
+        #     shift_labels = torch.hstack((labels[..., 1:], self.extra_ignored_labels[:labels.shape[0]]))
+        #     loss = fast_cross_entropy_loss(
+        #         logits = shift_logits,
+        #         labels = shift_labels,
+        #         logit_softcapping = logit_softcapping,
+        #         logit_scaling     = logit_scaling,
+        #         n_items           = kwargs.get("num_items_in_batch", None) or kwargs.get("n_items", None),
+        #     )
+        # else:
+        #     if logit_scaling != 0:
+        #         if logits.requires_grad:
+        #             logits = logit_scaling * logits
+        #         else:
+        #             logits *= logit_scaling
+        #         pass
+        #     pass
+        #     if logit_softcapping != 0:
+        #         if logits.requires_grad:
+        #             logits = (1.0 / logit_softcapping) * logits
+        #             logits = torch.tanh(logits)
+        #             logits = logit_softcapping * logits
+        #         else:
+        #             logits *= (1.0 / logit_softcapping)
+        #             torch.tanh(logits, out = logits)
+        #             logits *= logit_softcapping
+        #         pass
+        #     pass
+        # pass
 
         if not return_dict:
             output = (logits,) + outputs[1:]