Readme Updated

README.md

@@ -25,26 +25,27 @@ If you want to use the implementation directly from the Hub code repository (wit
 from huggingface_hub import snapshot_download
 import sys, torch
 
+# 1) Download the code+weights for your repo “as is”
 repo_dir = snapshot_download(
-    "amaye15/autoencoder",
+    repo_id="amaye15/autoencoder",
     repo_type="model",
-    allow_patterns=["*.py", "config.json", "*.safetensors"],
+    allow_patterns=["*.py", "config.json", "*.safetensors"],  # note the * wildcards
 )
+
+# 2) Add to import path so plain imports work
 sys.path.append(repo_dir)
 
+# 3) Import your classes from the repo code
 from configuration_autoencoder import AutoencoderConfig
 from modeling_autoencoder import AutoencoderForReconstruction
 
-# Load placeholder weights from the same repo (or your own trained weights)
-model = AutoencoderForReconstruction.from_pretrained(
-    "amaye15/autoencoder",
-    trust_remote_code=True,
-)
+# 4) Load the placeholder weights from the local folder (no internet, no code refresh)
+model = AutoencoderForReconstruction.from_pretrained(repo_dir)
 
-# Quick smoke test
+# 5) Quick smoke test
 x = torch.randn(8, 20)
-outputs = model(input_values=x)
-print("Reconstructed:", tuple(outputs.reconstructed.shape), "Latent:", tuple(outputs.last_hidden_state.shape))
+out = model(input_values=x)
+print("latent:", out.last_hidden_state.shape, "reconstructed:", out.reconstructed.shape)
 ```
 
 ## 🚀 Features