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Running
Running
Commit
·
29400c3
1
Parent(s):
6ad80cb
fix attempt for the download weights button
Browse files
vms/ui/project/services/previewing.py
CHANGED
@@ -46,7 +46,7 @@ class PreviewingService:
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if not checkpoints:
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return None
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-
latest_checkpoint = max(checkpoints, key=lambda x: int(x.name.split("
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lora_path = latest_checkpoint / "pytorch_lora_weights.safetensors"
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if lora_path.exists():
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if not checkpoints:
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return None
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latest_checkpoint = max(checkpoints, key=lambda x: int(x.name.split("_")[-1]))
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lora_path = latest_checkpoint / "pytorch_lora_weights.safetensors"
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if lora_path.exists():
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vms/ui/project/services/training.py
CHANGED
@@ -1480,16 +1480,33 @@ class TrainingService:
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self.append_log(f"Error uploading to hub: {str(e)}")
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return False
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-
def get_model_output_safetensors(self) -> str:
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"""Return the path to the model safetensors
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Returns:
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Path to
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"""
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model_output_safetensors_path = self.app.output_path / "pytorch_lora_weights.safetensors"
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-
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def create_training_dataset_zip(self) -> str:
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"""Create a ZIP file containing all training data
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self.append_log(f"Error uploading to hub: {str(e)}")
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return False
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def get_model_output_safetensors(self) -> Optional[str]:
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"""Return the path to the model safetensors
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Returns:
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Path to safetensors file or None if not found
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"""
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# Check if the root level file exists (this should be the primary location)
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model_output_safetensors_path = self.app.output_path / "pytorch_lora_weights.safetensors"
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if model_output_safetensors_path.exists():
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return str(model_output_safetensors_path)
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# If not found in root, log the issue and return None
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logger.warning(f"Model weights not found at expected location: {model_output_safetensors_path}")
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logger.info(f"Checking output directory contents: {list(self.app.output_path.glob('*'))}")
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# Check if there are any checkpoint directories as a fallback
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checkpoints = list(self.app.output_path.glob("finetrainers_step_*"))
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if checkpoints:
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logger.info(f"Found {len(checkpoints)} checkpoint directories, but main weights file is missing")
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latest_checkpoint = max(checkpoints, key=lambda x: int(x.name.split("_")[-1]))
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checkpoint_weights = latest_checkpoint / "pytorch_lora_weights.safetensors"
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if checkpoint_weights.exists():
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logger.info(f"Found weights in latest checkpoint: {checkpoint_weights}")
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return str(checkpoint_weights)
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return None
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def create_training_dataset_zip(self) -> str:
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"""Create a ZIP file containing all training data
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