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# Copied from https://github.com/johnsmith0031/alpaca_lora_4bit
import sys
from pathlib import Path
sys.path.insert(0, str(Path("repositories/alpaca_lora_4bit")))
import autograd_4bit
from amp_wrapper import AMPWrapper
from autograd_4bit import (Autograd4bitQuantLinear,
load_llama_model_4bit_low_ram)
from monkeypatch.peft_tuners_lora_monkey_patch import (
Linear4bitLt, replace_peft_model_with_gptq_lora_model)
from modules import shared
from modules.GPTQ_loader import find_quantized_model_file
replace_peft_model_with_gptq_lora_model()
def load_model_llama(model_name):
config_path = str(Path(f'{shared.args.model_dir}/{model_name}'))
model_path = str(find_quantized_model_file(model_name))
model, tokenizer = load_llama_model_4bit_low_ram(config_path, model_path, groupsize=shared.args.groupsize, is_v1_model=False)
for n, m in model.named_modules():
if isinstance(m, Autograd4bitQuantLinear) or isinstance(m, Linear4bitLt):
if m.is_v1_model:
m.zeros = m.zeros.half()
m.scales = m.scales.half()
m.bias = m.bias.half()
autograd_4bit.use_new = True
autograd_4bit.auto_switch = True
model.half()
wrapper = AMPWrapper(model)
wrapper.apply_generate()
return model, tokenizer
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