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40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
# Copied from https://github.com/johnsmith0031/alpaca_lora_4bit
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from pathlib import Path
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import alpaca_lora_4bit.autograd_4bit as autograd_4bit
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from alpaca_lora_4bit.amp_wrapper import AMPWrapper
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from alpaca_lora_4bit.autograd_4bit import (
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Autograd4bitQuantLinear,
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load_llama_model_4bit_low_ram
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)
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from alpaca_lora_4bit.models import Linear4bitLt
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from alpaca_lora_4bit.monkeypatch.peft_tuners_lora_monkey_patch import (
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replace_peft_model_with_int4_lora_model
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)
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from modules import shared
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from modules.GPTQ_loader import find_quantized_model_file
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replace_peft_model_with_int4_lora_model()
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def load_model_llama(model_name):
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config_path = str(Path(f'{shared.args.model_dir}/{model_name}'))
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model_path = str(find_quantized_model_file(model_name))
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model, tokenizer = load_llama_model_4bit_low_ram(config_path, model_path, groupsize=shared.args.groupsize, is_v1_model=False)
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for _, m in model.named_modules():
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if isinstance(m, Autograd4bitQuantLinear) or isinstance(m, Linear4bitLt):
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if m.is_v1_model:
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m.zeros = m.zeros.half()
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m.scales = m.scales.half()
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m.bias = m.bias.half()
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autograd_4bit.auto_switch = True
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model.half()
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wrapper = AMPWrapper(model)
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wrapper.apply_generate()
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return model, tokenizer
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