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model_v5.py

@@ -232,7 +232,9 @@ class GPTV5(nn.Module):
     @torch.no_grad()
     def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None,
                  repetition_penalty=1.15, no_repeat_ngram_size=3,
-                 max_context=None):
+                 max_context=None, eos_token_id=None):
+        """eos_token_id verildiyse model bu token'i sample ettiginde
+        early stop. SFT modelleri icin EOT (=0) onerilir."""
         if max_context is None:
             max_context = self.rope_cos.size(0)
         for _ in range(max_new_tokens):
@@ -244,6 +246,9 @@ class GPTV5(nn.Module):
                 for b in range(idx.size(0)):
                     seen = set(idx[b].tolist()[-256:])
                     for tok in seen:
+                        # EOS token'a rep penalty UYGULAMA — durmasi gerek
+                        if eos_token_id is not None and tok == eos_token_id:
+                            continue
                         if logits[b, tok] > 0:
                             logits[b, tok] /= repetition_penalty
                         else:
@@ -258,6 +263,9 @@ class GPTV5(nn.Module):
                     for i in range(len(tokens) - n + 1):
                         if tuple(tokens[i:i+n-1]) == prefix:
                             banned.add(tokens[i+n-1])
+                    # EOS asla banlanmasin
+                    if eos_token_id is not None:
+                        banned.discard(eos_token_id)
                     for tok in banned:
                         logits[b, tok] = -float("inf")
 
@@ -267,6 +275,11 @@ class GPTV5(nn.Module):
             probs = F.softmax(logits, dim=-1)
             next_id = torch.multinomial(probs, num_samples=1)
             idx = torch.cat([idx, next_id], dim=1)
+
+            # Early stop — batch'teki TUM ornek EOS'a ulastiysa
+            if eos_token_id is not None:
+                if (next_id == eos_token_id).all():
+                    break
         return idx