Salesforce/SFR-Embedding-Mistral · size mismatch for layers.31.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).

as title
my python==3.7 and 4.30.2
the all err context as follow

Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:06<00:00,  2.10s/it]
Traceback (most recent call last):
  File "inference.py", line 35, in <module>
    model = AutoModel.from_pretrained('Salesforce/SFR-Embedding-Mistral')
  File "/home/wangjh/.conda/envs/wav/lib/python3.7/site-packages/transformers/models/auto/auto_factory.py", line 485, in from_pretrained
    pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs
  File "/home/wangjh/.conda/envs/wav/lib/python3.7/site-packages/transformers/modeling_utils.py", line 2896, in from_pretrained
    keep_in_fp32_modules=keep_in_fp32_modules,
  File "/home/wangjh/.conda/envs/wav/lib/python3.7/site-packages/transformers/modeling_utils.py", line 3278, in _load_pretrained_model
    raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")
RuntimeError: Error(s) in loading state_dict for LlamaModel:
        size mismatch for layers.0.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.0.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.1.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.1.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.2.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.2.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.3.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.3.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.4.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.4.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.5.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.5.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.6.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.6.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.7.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.7.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.8.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.8.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.9.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.9.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.10.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.10.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.11.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.11.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.12.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.12.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.13.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.13.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.14.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.14.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.15.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.15.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.16.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.16.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.17.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.17.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.18.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.18.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.19.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.19.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.20.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.20.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.21.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.21.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.22.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.22.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.23.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.23.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.24.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.24.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.25.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.25.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.26.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.26.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.27.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.27.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.28.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.28.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.29.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.29.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.30.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.30.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.31.self_attn.k_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        size mismatch for layers.31.self_attn.v_proj.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
        You may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method.

and this is my code

def last_token_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
    if left_padding:
        return last_hidden_states[:, -1]
    else:
        sequence_lengths = attention_mask.sum(dim=1) - 1
        batch_size = last_hidden_states.shape[0]
        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]

def get_detailed_instruct(task_description: str, query: str) -> str:
    return f'Instruct: {task_description}\nQuery: {query}'

# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
    get_detailed_instruct(task, 'How to bake a chocolate cake'),
    get_detailed_instruct(task, 'Symptoms of the flu')
]
# No need to add instruction for retrieval documents
passages = [
    "To bake a delicious chocolate cake, you'll need the following ingredients: all-purpose flour, sugar, cocoa powder, baking powder, baking soda, salt, eggs, milk, vegetable oil, and vanilla extract. Start by preheating your oven to 350°F (175°C). In a mixing bowl, combine the dry ingredients (flour, sugar, cocoa powder, baking powder, baking soda, and salt). In a separate bowl, whisk together the wet ingredients (eggs, milk, vegetable oil, and vanilla extract). Gradually add the wet mixture to the dry ingredients, stirring until well combined. Pour the batter into a greased cake pan and bake for 30-35 minutes. Let it cool before frosting with your favorite chocolate frosting. Enjoy your homemade chocolate cake!",
    "The flu, or influenza, is an illness caused by influenza viruses. Common symptoms of the flu include a high fever, chills, cough, sore throat, runny or stuffy nose, body aches, headache, fatigue, and sometimes nausea and vomiting. These symptoms can come on suddenly and are usually more severe than the common cold. It's important to get plenty of rest, stay hydrated, and consult a healthcare professional if you suspect you have the flu. In some cases, antiviral medications can help alleviate symptoms and reduce the duration of the illness."
]

# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('Salesforce/SFR-Embedding-Mistral')
model = AutoModel.from_pretrained('Salesforce/SFR-Embedding-Mistral')