[fix] transformers-5.x

model/model_minimind.py

@@ -212,6 +212,10 @@ class MiniMindModel(nn.Module):
         past_key_values = past_key_values or [None] * len(self.layers)
         start_pos = past_key_values[0][0].shape[1] if past_key_values[0] is not None else 0
         hidden_states = self.dropout(self.embed_tokens(input_ids))
+        # Recompute RoPE buffers lost during meta-device init (transformers>=5.x)
+        if self.freqs_cos[0, 0] == 0:
+            freqs_cos, freqs_sin = precompute_freqs_cis(dim=self.config.head_dim, end=self.config.max_position_embeddings, rope_base=self.config.rope_theta, rope_scaling=self.config.rope_scaling)
+            self.freqs_cos, self.freqs_sin = freqs_cos.to(hidden_states.device), freqs_sin.to(hidden_states.device)
         position_embeddings = (self.freqs_cos[start_pos:start_pos + seq_length], self.freqs_sin[start_pos:start_pos + seq_length])
         presents = []
         for layer, past_key_value in zip(self.layers, past_key_values):
@@ -229,13 +233,15 @@ class MiniMindModel(nn.Module):
 
 class MiniMindForCausalLM(PreTrainedModel, GenerationMixin):
     config_class = MiniMindConfig
+    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
     def __init__(self, config: MiniMindConfig = None):
         self.config = config or MiniMindConfig()
         super().__init__(self.config)
         self.model = MiniMindModel(self.config)
         self.lm_head = nn.Linear(self.config.hidden_size, self.config.vocab_size, bias=False)
         if self.config.tie_word_embeddings: self.model.embed_tokens.weight = self.lm_head.weight
-    
+        self.post_init()
+
     def forward(self, input_ids, attention_mask=None, past_key_values=None, use_cache=False, logits_to_keep=0, labels=None, **kwargs):
         hidden_states, past_key_values, aux_loss = self.model(input_ids, attention_mask, past_key_values, use_cache, **kwargs)
         slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep