Finetuning llama-2 for classification

used AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
    token=token
    )

and

bnb_config = BitsAndBytesConfig(
    load_in_4bit = True,
    bnb_4bit_qunat_type = "nf4",
    bnb_4bit_compute_dtype = torch.float16,
)

for loading the model and finetuned this model by using LoRA

peft_config = LoraConfig(
        r=16,
        lora_alpha=64,
        lora_dropout=0.1,
        bias="none",
        task_type='SEQ_CLS',
)

and saved as “tuned_model”
while loading the model:

from transformers import pipeline
pipe = pipeline('text-classification',
                tuned_model,
                device_map="auto")
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
[<ipython-input-19-fd1b8ca97698>](https://localhost:8080/#) in <cell line: 2>()
      1 from transformers import pipeline
----> 2 pipe = pipeline('text-classification',tuned_model, device_map="auto")

5 frames
[/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py](https://localhost:8080/#) in <dictcomp>(.0)
   3809                 p: {"safetensors_file": f, "weight_name": p, "dtype": str_dtype}
   3810                 for p, f in weight_map.items()
-> 3811                 if param_device_map[p] == "disk"
   3812             }
   3813 

KeyError: 'lm_head.weight'

Can any one suggest me how to load this tuned_model ?