don't dropout in eval mode
diff --git a/model.py b/model.py
index 0858f80a3..287aadd34 100644
--- a/model.py
+++ b/model.py
@@ -69,7 +69,7 @@ def forward(self, x):
         # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
         if self.flash:
             # efficient attention using Flash Attention CUDA kernels
-            y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.dropout, is_causal=True)
+            y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.dropout if self.training else 0, is_causal=True)
         else:
             # manual implementation of attention
             att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))

To give the model more chances to figure out if it's being trained or deployed :) (c.f. https://twitter.com/karpathy/status/1635049541534879745)
