Test

test.py

@@ -0,0 +1,65 @@
+import torch
+import torch.nn as nn
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "Qwen/Qwen3.5-0.8B"
+cache_path = "./.cache"
+
+# Load tokenizer + model
+tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir=cache_path)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    cache_dir=cache_path,
+)
+
+model.eval()
+print(model)
+
+# -----------------------------
+# 1. Replace ALL linear layers
+# -----------------------------
+def replace_weights_with_random(module):
+    for name, child in module.named_children():
+
+        # If it's a Linear layer, replace weights
+        if isinstance(child, nn.Linear):
+            with torch.no_grad():
+                child.weight.copy_(
+                    torch.randn_like(child.weight)
+                )
+                if child.bias is not None:
+                    child.bias.copy_(
+                        torch.randn_like(child.bias)
+                    )
+
+        else:
+            # recurse into submodules
+            replace_weights_with_random(child)
+
+replace_weights_with_random(model)
+
+print("All linear layer weights replaced with random tensors.")
+
+# -----------------------------
+# 2. Run inference
+# -----------------------------
+prompt = "Explain tensor networks in simple terms."
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    output = model.generate(
+        **inputs,
+        max_new_tokens=500,
+        temperature=1.0,
+        do_sample=True
+    )
+
+print("\n=== OUTPUT ===\n")
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+
+model.save_pretrained("./.cache/qwen_random")
+tokenizer.save_pretrained("./.cache/qwen_random")