modified

app.py

@@ -20,7 +20,7 @@ CLEARML_API_HOST     = os.environ["CLEARML_API_HOST"]
 CLEARML_WEB_HOST     = os.environ["CLEARML_WEB_HOST"]
 CLEARML_FILES_HOST   = os.environ["CLEARML_FILES_HOST"]
 CLEARML_ACCESS_KEY   = os.environ["CLEARML_API_ACCESS_KEY"]
-CLEARML_SECRET_KEY   = os.environ["CLEARML_API_SECRET_KEY"]
+CLEARML_SECRET_KEY   = os.environ["CLEARML_SECRET_KEY"]
 
 # Apply to SDK
 Task.set_credentials(
@@ -69,8 +69,10 @@ print("Base model architecture and tokenizer loaded.")
 
 # Download the fine-tuned weights via ClearML using your injected creds
 task = Task.get_task(task_id="2d65a9e213ea49a9b37e1cc89a2b7ff0")
-finetuned_weights_path = task.artifacts["lora-adapter"].get_local_copy()
-print(f"Fine-tuned adapter weights downloaded to directory: {os.path.dirname(finetuned_weights_path)}")
+extracted_adapter_dir = task.artifacts["lora-adapter"].get_local_copy() # This is the directory path
+actual_weights_file_path = os.path.join(extracted_adapter_dir, "pytorch_model.bin") # Path to the actual model file
+print(f"Fine-tuned adapter weights downloaded and extracted to directory: {extracted_adapter_dir}")
+print(f"Loading fine-tuned adapter weights from file: {actual_weights_file_path}")
 
 # Create LoRA configuration matching the fine-tuned checkpoint
 lora_cfg = LoraConfig(
@@ -84,7 +86,7 @@ lora_cfg = LoraConfig(
 # Wrap base model with PEFT LoRA
 peft_model = get_peft_model(base_model, lora_cfg)
 # Load adapter-only weights and merge into base
-adapter_state = torch.load(finetuned_weights_path, map_location="cpu")
+adapter_state = torch.load(actual_weights_file_path, map_location="cpu") # Use the correct file path
 peft_model.load_state_dict(adapter_state, strict=False)
 model = peft_model.merge_and_unload()
 print("Merged base model with LoRA adapters.")