[Feature request] Support GPTQ quantization

So I have a GPTQ llama model I downloaded (from TheBloke), and it's already 4 bit quantized. I have to pass in False for the load_in_4bit parameter of:

model, tokenizer = FastLlamaModel.from_pretrained(

because if I don't, I get an error thrown saying:

The model is already quantized with gptq. You can't quantize it again with bitsandbytes

But, if I pass in False for load_in_4bit, this code makes bnb_config be None:

        bnb_config = None
        if load_in_4bit:
            bnb_config = BitsAndBytesConfig(
                load_in_4bit              = True,
                bnb_4bit_use_double_quant = True,
                bnb_4bit_quant_type       = "nf4",
                bnb_4bit_compute_dtype    = dtype,
            )

and that makes quantization_config be None as well:

quantization_config = bnb_config,

and that crashes here:

        if hasattr(self, "quantization_config"):
            output["quantization_config"] = (
                self.quantization_config.to_dict()

with the error message:

'NoneType' object has no attribute 'to_dict'

So I'm not sure how to LoRA train this llama model. Any thoughts?