U4R

/

ChartVLM-base

like 5

Follow

Alpha-Innovator Lab 33

• "_codecs.encode",
• "torch._utils._rebuild_tensor_v2",
• "numpy.core.multiarray._reconstruct",
• "collections.OrderedDict",
• "numpy.ndarray",
• "numpy.dtype",
• "torch.ByteStorage"

How to fix it?

• "_codecs.encode",
• "torch._utils._rebuild_tensor_v2",
• "numpy.core.multiarray._reconstruct",
• "collections.OrderedDict",
• "numpy.ndarray",
• "numpy.dtype",
• "torch.ByteStorage"

How to fix it?

• "_codecs.encode",
• "torch._utils._rebuild_tensor_v2",
• "numpy.core.multiarray._reconstruct",
• "collections.OrderedDict",
• "numpy.ndarray",
• "numpy.dtype",
• "torch.ByteStorage"

How to fix it?

• "_codecs.encode",
• "torch._utils._rebuild_tensor_v2",
• "numpy.core.multiarray._reconstruct",
• "collections.OrderedDict",
• "numpy.ndarray",
• "numpy.dtype",
• "torch.ByteStorage"

How to fix it?

• "_codecs.encode",
• "torch._utils._rebuild_tensor_v2",
• "numpy.core.multiarray._reconstruct",
• "collections.OrderedDict",
• "numpy.ndarray",
• "numpy.dtype",
• "torch.ByteStorage"

How to fix it?

• "_codecs.encode",
• "torch._utils._rebuild_tensor_v2",
• "numpy.core.multiarray._reconstruct",
• "collections.OrderedDict",
• "numpy.ndarray",
• "numpy.dtype",
• "torch.ByteStorage"

How to fix it?

• "_codecs.encode",
• "torch._utils._rebuild_tensor_v2",
• "numpy.core.multiarray._reconstruct",
• "collections.OrderedDict",
• "numpy.ndarray",
• "numpy.dtype",
• "torch.ByteStorage"

How to fix it?

• "_codecs.encode",
• "torch._utils._rebuild_tensor_v2",
• "numpy.core.multiarray._reconstruct",
• "collections.OrderedDict",
• "numpy.ndarray",
• "numpy.dtype",
• "torch.ByteStorage"

How to fix it?

• "transformers.training_args.OptimizerNames",
• "transformers.trainer_utils.SchedulerType",
• "accelerate.state.PartialState",
• "transformers.trainer_utils.IntervalStrategy",
• "transformers.training_args.TrainingArguments",
• "accelerate.utils.dataclasses.DistributedType",
• "torch.device",
• "transformers.trainer_utils.HubStrategy"

How to fix it?