Fix: Add get_input_embeddings and set_input_embeddings for resize support.

modeling_syko.py

@@ -3,7 +3,6 @@ import torch
 import torch.nn as nn
 from torch.nn import functional as F
 from transformers import PretrainedConfig, PreTrainedModel
-# BU SATIR YENİ EKLENDİ:
 from transformers.modeling_outputs import CausalLMOutputWithPast
 
 class SykoConfig(PretrainedConfig):
@@ -104,6 +103,7 @@ class SykoForCausalLM(PreTrainedModel):
         self.n_layer = config.n_layer
         self.dropout = config.dropout
         
+        # Embedding katmanının adı 'token_embedding_table'
         self.token_embedding_table = nn.Embedding(self.vocab_size, self.n_embd)
         self.position_embedding_table = nn.Embedding(self.block_size, self.n_embd)
         self.blocks = nn.Sequential(*[Block(self.n_embd, self.n_head, self.block_size, self.dropout) for _ in range(self.n_layer)])
@@ -112,6 +112,14 @@ class SykoForCausalLM(PreTrainedModel):
         
         self.apply(self._init_weights)
 
+    # --- YENİ EKLENEN KISIM: HF BU FONKSİYONLARI ARIYOR ---
+    def get_input_embeddings(self):
+        return self.token_embedding_table
+
+    def set_input_embeddings(self, new_embeddings):
+        self.token_embedding_table = new_embeddings
+    # -----------------------------------------------------
+
     def _init_weights(self, module):
         if isinstance(module, nn.Linear):
             torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
@@ -140,12 +148,10 @@ class SykoForCausalLM(PreTrainedModel):
             labels_reshaped = labels.view(B*T)
             loss = F.cross_entropy(logits_reshaped, labels_reshaped)
 
-        # --- DÜZELTME BURADA ---
-        # Tuple yerine CausalLMOutputWithPast döndürüyoruz.
         return CausalLMOutputWithPast(
             loss=loss,
             logits=logits,
-            past_key_values=None, # Cache kullanmıyoruz şimdilik
+            past_key_values=None,
             hidden_states=None,
             attentions=None,
         )