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| # Copied from https://github.com/johnsmith0031/alpaca_lora_4bit | |
| from pathlib import Path | |
| import alpaca_lora_4bit.autograd_4bit as autograd_4bit | |
| from alpaca_lora_4bit.amp_wrapper import AMPWrapper | |
| from alpaca_lora_4bit.autograd_4bit import ( | |
| Autograd4bitQuantLinear, | |
| load_llama_model_4bit_low_ram | |
| ) | |
| from alpaca_lora_4bit.models import Linear4bitLt | |
| from alpaca_lora_4bit.monkeypatch.peft_tuners_lora_monkey_patch import ( | |
| replace_peft_model_with_int4_lora_model | |
| ) | |
| from modules import shared | |
| from modules.GPTQ_loader import find_quantized_model_file | |
| replace_peft_model_with_int4_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 _, 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.auto_switch = True | |
| model.half() | |
| wrapper = AMPWrapper(model) | |
| wrapper.apply_generate() | |
| return model, tokenizer | |