Update modeling.py

modeling.py

@@ -1,54 +1,18 @@
-# modeling.py
-# Safe demonstration: prints/warns when executed, but DOES NOT fetch or execute remote code.
-
-import warnings
-import sys
-
-# 1) Immediate visible print
-print("\n" + "="*70)
-print("⚠️  WARNING: You are loading a model that declares it MAY execute remote code.")
-print("This repository is a demonstration and DOES NOT execute remote code.")
-print("Before setting `trust_remote_code=True` review the model files manually.")
-print("="*70 + "\n")
-
-# 2) Also raise a Python warning (visible in many environments)
-warnings.warn(
-    "This model includes custom Python code and may execute arbitrary logic when loaded. "
-    "Only load it with trust_remote_code=True after inspecting the repository.",
-    UserWarning,
-)
-
-# 3) Minimal HF-compatible model implementation (harmless).
-from transformers import PreTrainedModel, PretrainedConfig
-
-class SimpleWarningConfig(PretrainedConfig):
-    model_type = "simple-warning-model"
-
-    def __init__(self, hidden_size=8, **kwargs):
-        super().__init__(**kwargs)
-        self.hidden_size = hidden_size
-
-class SimpleModel(PreTrainedModel):
-    config_class = SimpleWarningConfig
-    base_model_prefix = "simple_model"
-
-    def __init__(self, config: SimpleWarningConfig):
-        super().__init__(config)
-        # keep internals minimal and harmless
-        try:
-            import torch.nn as nn
-            self.dummy = nn.Linear(config.hidden_size, config.hidden_size)
-        except Exception:
-            # if torch not available, we still want the module importable
-            self.dummy = None
-
-    def forward(self, *args, **kwargs):
-        # harmless placeholder forward
-        try:
-            import torch
-            if self.dummy is None:
-                return torch.zeros(1, self.config.hidden_size)
-            return self.dummy(torch.zeros(1, self.config.hidden_size))
-        except Exception:
-            # if torch is missing, return a plain Python fallback
-            return [[0.0] * self.config.hidden_size]
+# modeling.py
+from transformers import PreTrainedModel
+import torch.nn as nn
+
+class SimpleWarningConfig:
+    def __init__(self, hidden_size=8):
+        self.hidden_size = hidden_size
+
+class SimpleModel(PreTrainedModel):
+    config_class = SimpleWarningConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        print("⚠️ WARNING: You are loading a model with trust_remote_code=True! Be careful with remote code execution!")
+        self.linear = nn.Linear(config.hidden_size, config.hidden_size)
+
+    def forward(self, x):
+        return self.linear(x)