peft: Error while loading from PeftModel.

I am trying the load the finetuned adapter on the llama-2-7b model.But getting the following error.This was working fine till yesterday.Tried to downgrade Peft version to 0.5.0 and transformers to 4.32.0.

Code: finetune_model=PeftModel.from_pretrained(model,"llama-finetuned/llama-7b-hf-4bit-3epochs/",device_map="auto") Error:

KeyError                                  Traceback (most recent call last)
Cell In[3], line 1
----> 1 finetune_model=PeftModel.from_pretrained(model,"llama-finetuned/llama-7b-hf-4bit-3epochs/",device_map="auto")

File ~/anaconda3/envs/python3/lib/python3.10/site-packages/peft/peft_model.py:278, in PeftModel.from_pretrained(cls, model, model_id, adapter_name, is_trainable, config, **kwargs)
    276 else:
    277     model = MODEL_TYPE_TO_PEFT_MODEL_MAPPING[config.task_type](model, config, adapter_name)
--> 278 model.load_adapter(model_id, adapter_name, is_trainable=is_trainable, **kwargs)
    279 return model

File ~/anaconda3/envs/python3/lib/python3.10/site-packages/peft/peft_model.py:557, in PeftModel.load_adapter(self, model_id, adapter_name, is_trainable, **kwargs)
    554 adapters_weights = load_peft_weights(model_id, device=torch_device, **hf_hub_download_kwargs)
    556 # load the weights into the model
--> 557 load_result = set_peft_model_state_dict(self, adapters_weights, adapter_name=adapter_name)
    558 if (
    559     (getattr(self, "hf_device_map", None) is not None)
    560     and (len(set(self.hf_device_map.values()).intersection({"cpu", "disk"})) > 0)
    561     and len(self.peft_config) == 1
    562 ):
    563     device_map = kwargs.get("device_map", "auto")

File ~/anaconda3/envs/python3/lib/python3.10/site-packages/peft/utils/save_and_load.py:135, in set_peft_model_state_dict(model, peft_model_state_dict, adapter_name)
    132 else:
    133     raise NotImplementedError
--> 135 load_result = model.load_state_dict(peft_model_state_dict, strict=False)
    136 if config.is_prompt_learning:
    137     model.prompt_encoder[adapter_name].embedding.load_state_dict(
    138         {"weight": peft_model_state_dict["prompt_embeddings"]}, strict=True
    139     )

File ~/anaconda3/envs/python3/lib/python3.10/site-packages/torch/nn/modules/module.py:2027, in Module.load_state_dict(self, state_dict, strict)
   2020         out = hook(module, incompatible_keys)
   2021         assert out is None, (
   2022             "Hooks registered with ``register_load_state_dict_post_hook`` are not"
   2023             "expected to return new values, if incompatible_keys need to be modified,"
   2024             "it should be done inplace."
   2025         )
-> 2027 load(self, state_dict)
   2028 del load
   2030 if strict:

File ~/anaconda3/envs/python3/lib/python3.10/site-packages/torch/nn/modules/module.py:2015, in Module.load_state_dict.<locals>.load(module, local_state_dict, prefix)
   2013         child_prefix = prefix + name + '.'
   2014         child_state_dict = {k: v for k, v in local_state_dict.items() if k.startswith(child_prefix)}
-> 2015         load(child, child_state_dict, child_prefix)
   2017 # Note that the hook can modify missing_keys and unexpected_keys.
   2018 incompatible_keys = _IncompatibleKeys(missing_keys, unexpected_keys)

File ~/anaconda3/envs/python3/lib/python3.10/site-packages/torch/nn/modules/module.py:2015, in Module.load_state_dict.<locals>.load(module, local_state_dict, prefix)
   2013         child_prefix = prefix + name + '.'
   2014         child_state_dict = {k: v for k, v in local_state_dict.items() if k.startswith(child_prefix)}
-> 2015         load(child, child_state_dict, child_prefix)
   2017 # Note that the hook can modify missing_keys and unexpected_keys.
   2018 incompatible_keys = _IncompatibleKeys(missing_keys, unexpected_keys)

    [... skipping similar frames: Module.load_state_dict.<locals>.load at line 2015 (4 times)]

File ~/anaconda3/envs/python3/lib/python3.10/site-packages/torch/nn/modules/module.py:2015, in Module.load_state_dict.<locals>.load(module, local_state_dict, prefix)
   2013         child_prefix = prefix + name + '.'
   2014         child_state_dict = {k: v for k, v in local_state_dict.items() if k.startswith(child_prefix)}
-> 2015         load(child, child_state_dict, child_prefix)
   2017 # Note that the hook can modify missing_keys and unexpected_keys.
   2018 incompatible_keys = _IncompatibleKeys(missing_keys, unexpected_keys)

File ~/anaconda3/envs/python3/lib/python3.10/site-packages/torch/nn/modules/module.py:2009, in Module.load_state_dict.<locals>.load(module, local_state_dict, prefix)
   2007 def load(module, local_state_dict, prefix=''):
   2008     local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
-> 2009     module._load_from_state_dict(
   2010         local_state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
   2011     for name, child in module._modules.items():
   2012         if child is not None:

File ~/anaconda3/envs/python3/lib/python3.10/site-packages/bitsandbytes/nn/modules.py:256, in Linear4bit._load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
    253     bias_data = state_dict.pop(prefix + "bias", None)
    254     self.bias.data = bias_data.to(self.bias.data.device)
--> 256 self.weight, state_dict = bnb.nn.Params4bit.from_state_dict(
    257                 state_dict, prefix=prefix + "weight" + ".", requires_grad=False
    258             )
    259 unexpected_keys.extend(state_dict.keys())

File ~/anaconda3/envs/python3/lib/python3.10/site-packages/bitsandbytes/nn/modules.py:158, in Params4bit.from_state_dict(cls, state_dict, prefix, requires_grad)
    156 @classmethod
    157 def from_state_dict(cls, state_dict, prefix="", requires_grad=False):
--> 158     data = state_dict.pop(prefix.rstrip('.'))
    160     # extracting components for QuantState from state_dict
    161     qs_dict = {}

KeyError: 'base_model.model.model.layers.0.self_attn.q_proj.weight'

Any help would be appreciated.Thankyou.