初始化项目，由ModelHub XC社区提供模型

Model: sabari2005/cyberslm-instruct
Source: Original Platform

cyberslm_sft/model/__init__.py

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+from .cyberslm import CyberSLM
+
+__all__ = ["CyberSLM"]

cyberslm_sft/model/cyberslm.py

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+import sys
+from pathlib import Path
+
+# Add the root directory (which contains 'cyberslm') to sys.path
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent))
+
+from cyberslm.model.model import CyberSLM as Stage1CyberSLM
+from cyberslm.model.config import CyberSLMConfig
+
+class CyberSLM(Stage1CyberSLM):
+    """
+    Adapter class to bridge SFT's ModelConfig to Stage 1's CyberSLMConfig,
+    allowing us to use the original Stage 1 model architecture verbatim.
+    """
+    def __init__(self, cfg):
+        # Convert SFT ModelConfig to Stage 1 CyberSLMConfig
+        stage1_cfg = CyberSLMConfig(
+            vocab_size=cfg.vocab_size,
+            hidden_dim=cfg.hidden_size,
+            num_layers=cfg.num_layers,
+            num_heads=cfg.num_heads,
+            head_dim=cfg.head_dim,
+            ffn_hidden_dim=cfg.ffn_size,
+            max_seq_len=cfg.max_seq_len,
+            tie_weights=cfg.weight_tying,
+            bias=cfg.bias,
+            dropout=cfg.dropout,
+            norm_eps=cfg.norm_eps,
+        )
+        super().__init__(stage1_cfg)
+
+    def forward(self, input_ids, attention_mask=None, **kwargs):
+        """
+        Wrapper around Stage 1's forward pass.
+        Stage 1 returns (logits, all_attn_weights); the SFT trainer expects
+        just the logits Tensor. The key-padding ``attention_mask`` (1=keep,
+        0=pad) is forwarded so right-padded batches do not corrupt real tokens.
+        """
+        logits, _ = super().forward(input_ids, attention_mask=attention_mask)
+        return logits