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import paddle
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import torch
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torch_model_dict = torch.load('large-v3-turbo.pt')['model_state_dict']
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paddle_model_state_dict = {}
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for key, val in torch_model_dict.items():
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if key.endswith(
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'weight'
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) and val.ndim == 2 and key != "decoder.token_embedding.weight":
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val = val.T
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paddle_model_state_dict[key] = paddle.to_tensor(
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val.cpu().numpy()).astype("float32")
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# add other params in case if need, such as:
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paddle_model_state_dict['dims'] = torch.load('large-v3-turbo.pt')['dims']
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paddle.save(paddle_model_state_dict, 'weights.params')
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