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- from pathlib import Path
- import modules.shared as shared
- from modules.models import load_model
- from modules.text_generation import clear_torch_cache
- def reload_model():
- shared.model = shared.tokenizer = None
- clear_torch_cache()
- shared.model, shared.tokenizer = load_model(shared.model_name)
- def add_lora_to_model(lora_name):
- from peft import PeftModel
- reload_model()
- if lora_name != "None":
- print(f"Adding the LoRA {lora_name} to the model...")
- params = {}
- if not shared.args.cpu:
- params['dtype'] = shared.model.dtype
- if hasattr(shared.model, "hf_device_map"):
- params['device_map'] = {"base_model.model."+k: v for k, v in shared.model.hf_device_map.items()}
- elif shared.args.load_in_8bit:
- params['device_map'] = {'': 0}
-
- shared.model = PeftModel.from_pretrained(shared.model, Path(f"loras/{lora_name}"), **params)
- if not shared.args.load_in_8bit and not shared.args.cpu:
- shared.model.half()
- if not hasattr(shared.model, "hf_device_map"):
- shared.model.cuda()
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