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ztO}2Fp^fXZUTX%!=Elov4dMr;eTe$w^>InrsnudHqPZ^^vTXln<7@r!Z4d9`eyKQ< ztn1um%HhgWgLh1@+aW&{c*^Qhwb_sL#Z`O>=(qeD5mZnm`o->s+U`$X z!HEIk61V#dUq<}X`>=O^_ZH8RpLZ9jw0EVxmhS7ZKkt-1&8qxJ*w%mNfQO!T=T9qT zkUw|(vc=2Ya`$@~(kisS*Rg)wyT;vY+UI-j3~$6wzpy`F^?Dz0ZRCk&U~;?8|0?Tw zdyQ)P!~4AJ`sLDEr?#xSo2vW0^VfCtMZlxE>I6Es?A3bjv-?u>WR~p*|D2dIXJYH0 z+R8649mKwv@qTPR(jfEb6hpo03Ol)5x3~AzWEZxYdzWnN4|R(8x1?@r{h>Pc{lIP2 z-KMr-ufFO}`@|A9;fcqkf{3@rm_8n0d62ZTmf!8F-k#J)Z=GHC_FWIyzUSuC7^Vwn z+ErF3PbgyJ_`ZPmd~cxEkF-l~8N{!B-)Op;fA5~j2Y{;p!xw}zrayQuP|+N-`3i6q zZC37^_%qu#Ze&lKyxFYQUjFc$U#v9S7=T=34kSD5%9h`?QOBySceGr)tm*E2N9xg| zAIa6pf?F^D314~h None: + for name in set([k.split(pattern)[0] for k, _ in model.named_modules() if pattern in k]): + parent_name = ".".join(name.split(".")[:-1]) + child_name = name.split(".")[-1] + parent_module = model.get_submodule(parent_name) + child_module = getattr(parent_module, child_name) + base_layer = getattr(child_module, "base_layer") + weight = getattr(base_layer, "weight", None) + bias = getattr(base_layer, "bias", None) + setattr(parent_module, child_name, Shell(weight, bias)) + + print("Model unwrapped.") + + +def quantize_loftq( + model_name_or_path: str, + save_dir: str, + loftq_bits: Optional[int] = 4, + loftq_iter: Optional[int] = 1, + lora_alpha: Optional[int] = None, + lora_rank: Optional[int] = 16, + lora_target: Optional[str] = "q_proj,v_proj" +): + tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True) + model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype="auto") + loftq_config = LoftQConfig(loftq_bits=loftq_bits, loftq_iter=loftq_iter) + lora_config = LoraConfig( + task_type=TaskType.CAUSAL_LM, + inference_mode=True, + r=lora_rank, + lora_alpha=lora_alpha if lora_alpha is not None else lora_rank * 2, + lora_dropout=0.1, + target_modules=[name.strip() for name in lora_target.split(",")], + init_lora_weights="loftq", + loftq_config=loftq_config + ) + + # Init LoftQ model + lora_model = get_peft_model(model, lora_config) + base_model = lora_model.get_base_model() + + # Save LoftQ model + setattr(lora_model.base_model.peft_config["default"], "base_model_name_or_path", save_dir) + setattr(lora_model.base_model.peft_config["default"], "init_lora_weights", True) + lora_model.save_pretrained(os.path.join(save_dir, "adapters")) + + # Save base model + unwrap_model(base_model) + base_model.save_pretrained(save_dir) + tokenizer.save_pretrained(save_dir) + + +if __name__ == "__main__": + fire.Fire(quantize_loftq)