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from pathlib import Path
import torch
from peft import PeftModel
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):
# If a LoRA had been previously loaded, or if we want
# to unload a LoRA, reload the model
if shared.lora_name not in ['None', ''] or lora_name in ['None', '']:
reload_model()
shared.lora_name = lora_name
if lora_name not in ['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"{shared.args.lora_dir}/{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"):
if torch.has_mps:
device = torch.device('mps')
shared.model = shared.model.to(device)
else:
shared.model = shared.model.cuda()
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