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jina-embeddings-v3

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I use the transformers to load the jina-v3, and I can not solve the issue below, can anybody teach me?
A new version of the following files was downloaded from https://huggingface.co/jinaai/xlm-roberta-flash-implementation:

• configuration_xlm_roberta.py
  . Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
  A new version of the following files was downloaded from https://huggingface.co/jinaai/xlm-roberta-flash-implementation:
• rotary.py
  . Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
  A new version of the following files was downloaded from https://huggingface.co/jinaai/xlm-roberta-flash-implementation:
• mha.py
• rotary.py
  . Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
  A new version of the following files was downloaded from https://huggingface.co/jinaai/xlm-roberta-flash-implementation:
• xlm_padding.py
  . Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
  A new version of the following files was downloaded from https://huggingface.co/jinaai/xlm-roberta-flash-implementation:
• stochastic_depth.py
  . Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
  A new version of the following files was downloaded from https://huggingface.co/jinaai/xlm-roberta-flash-implementation:
• mlp.py
  . Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
  A new version of the following files was downloaded from https://huggingface.co/jinaai/xlm-roberta-flash-implementation:
• block.py
• stochastic_depth.py
• mlp.py
  . Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
  A new version of the following files was downloaded from https://huggingface.co/jinaai/xlm-roberta-flash-implementation:
• embedding.py
  . Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
  A new version of the following files was downloaded from https://huggingface.co/jinaai/xlm-roberta-flash-implementation:
• modeling_xlm_roberta.py
• mha.py
• xlm_padding.py
• block.py
• embedding.py
  . Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
  A new version of the following files was downloaded from https://huggingface.co/jinaai/xlm-roberta-flash-implementation:
• modeling_lora.py
• modeling_xlm_roberta.py
  . Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  flash_attn is not installed. Using PyTorch native attention implementation.
  Traceback (most recent call last):
  File "/mnt/work/parallel_dataset/data1228(2)/generate_emb_jinav3_.py", line 91, in
  main()
  File "/mnt/work/parallel_dataset/data1228(2)/generate_emb_jinav3_.py", line 45, in main
  model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True)
  File "/root/.pyenv/versions/3.10.10/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py", line 559, in from_pretrained
  return model_class.from_pretrained(
  File "/mnt/work/huggingface/modules/transformers_modules/jinaai/xlm-roberta-flash-implementation/2b6bc3f30750b3a9648fe9b63448c09920efe9be/modeling_lora.py", line 338, in from_pretrained
  return super().from_pretrained(
  File "/mnt/work/huggingface/modules/transformers_modules/jinaai/xlm-roberta-flash-implementation/2b6bc3f30750b3a9648fe9b63448c09920efe9be/modeling_xlm_roberta.py", line 442, in from_pretrained
  return super().from_pretrained(*args, **kwargs)
  File "/root/.pyenv/versions/3.10.10/lib/python3.10/site-packages/transformers/modeling_utils.py", line 4264, in from_pretrained
  ) = cls._load_pretrained_model(
  File "/root/.pyenv/versions/3.10.10/lib/python3.10/site-packages/transformers/modeling_utils.py", line 4593, in _load_pretrained_model
  for name, param in model.named_parameters():
  File "/mnt/work/huggingface/modules/transformers_modules/jinaai/xlm-roberta-flash-implementation/2b6bc3f30750b3a9648fe9b63448c09920efe9be/modeling_lora.py", line 381, in named_parameters
  for name, param in super().named_parameters(
  TypeError: Module.named_parameters() got an unexpected keyword argument 'remove_duplicate'