diff --git a/nanochat/gpt.py b/nanochat/gpt.py
index 5487047f..208acd14 100644
--- a/nanochat/gpt.py
+++ b/nanochat/gpt.py
@@ -416,7 +416,7 @@ class GPT(nn.Module):
         if targets is not None:
             # training: given the targets, compute and return the loss
             # TODO experiment with chunked cross-entropy?
-            loss = F.cross_entropy(logits.reshape(-1, logits.size(-1)), targets.reshape(-1), ignore_index=-1, reduction=loss_reduction)
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1, reduction=loss_reduction)
             return loss
         else:
             # inference: just return the logits directly
diff --git a/nanochat/loss_eval.py b/nanochat/loss_eval.py
index 6f59b110..5a556e6c 100644
--- a/nanochat/loss_eval.py
+++ b/nanochat/loss_eval.py
@@ -32,7 +32,7 @@ def evaluate_bpb(model, batches, steps, token_bytes):
         x, y = next(batch_iter)
         loss2d = model(x, y, loss_reduction='none') # (B, T)
         loss2d = loss2d.view(-1) # flatten
-        y = y.reshape(-1) # flatten
+        y = y.view(-1) # flatten
         if (y.int() < 0).any(): # mps does not currently have kernel for < 0 for int64, only int32
             # slightly more complex code path if some target tokens are ignore_index (e.g. -1)
             # any target token < 0 is to be ignored: do NOT index token_bytes with negatives
diff --git a/scripts/chat_sft.py b/scripts/chat_sft.py
index a783ed21..aad23ce2 100644
--- a/scripts/chat_sft.py
+++ b/scripts/chat_sft.py
@@ -277,8 +277,8 @@ def sft_data_generator_bos_bestfit(split, buffer_size=100):
         # Build tensors
         use_cuda = device_type == "cuda"
         batch_tensor = torch.tensor(rows, dtype=torch.long, pin_memory=use_cuda)
-        inputs = batch_tensor[:, :-1].to(device=device, dtype=torch.int32, non_blocking=use_cuda)
-        targets = batch_tensor[:, 1:].to(device=device, dtype=torch.int64, non_blocking=use_cuda)
+        inputs = batch_tensor[:, :-1].to(device=device, dtype=torch.int32, non_blocking=use_cuda).contiguous()
+        targets = batch_tensor[:, 1:].to(device=device, dtype=torch.int64, non_blocking=use_cuda).contiguous()
 
         # Mask out padding positions in targets (set to -1 = ignore_index)
         # For each row, positions >= (content_length - 1) in targets should be masked