Error when trying to run with a Quantized base model

Hello. I have been trying to run the multi task llama7b models with Bloke's llama 7b GPTQ(https://huggingface.co/TheBloke/Llama-2-7B-GPTQ) as the base.

```
def load_model(base_model, peft_model, from_remote=True):

model_name = parse_model_name(base_model, from_remote)
# model = AutoModelForCausalLM.from_pretrained(
# model_name, trust_remote_code=True,
# device_map="auto",
# )
model_name_or_path = "TheBloke/Llama-2-7b-Chat-GPTQ"
model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
model_basename="model",
use_safetensors=True,
trust_remote_code=True,
device="cuda:0",
use_triton="False")
model.model_parallel = True

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

model = PeftModel.from_pretrained(model, peft_model)
model = model.eval()
return model, tokenizer
```
While running the same in google colab, I get the error when trying to load PEFT from pre trained:
```
ValueError: Target modules ['q_proj', 'k_proj', 'v_proj'] not found in the base model. Please check the target modules and try again.
```

After a bit of searching, It says I'll have to re train the PEFT model by using a different config. Is there anything I can do? (other than training)

For debugging purposes, value of 'model' before PEFT is used:
```
LlamaGPTQForCausalLM(
(model): LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(32000, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x LlamaDecoderLayer(
(self_attn): FusedLlamaAttentionForQuantizedModel(
(qkv_proj): GeneralQuantLinear(in_features=4096, out_features=12288, bias=True)
(o_proj): GeneralQuantLinear(in_features=4096, out_features=4096, bias=True)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): FusedLlamaMLPForQuantizedModel(
(gate_proj): GeneralQuantLinear(in_features=4096, out_features=11008, bias=True)
(up_proj): GeneralQuantLinear(in_features=4096, out_features=11008, bias=True)
(down_proj): GeneralQuantLinear(in_features=11008, out_features=4096, bias=True)
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
)
```