36 lines
1.1 KiB
Python
Vendored
36 lines
1.1 KiB
Python
Vendored
from collections import OrderedDict
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import os
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import sys
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from typing import Dict
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import typing
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import torch
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import bitsandbytes as bnb
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from argparse import ArgumentParser
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parser = ArgumentParser()
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parser.add_argument("--base_model", default="", type=str)
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parser.add_argument("--state_checkpoint", default="", type=str)
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parser.add_argument("--output", default="", type=str)
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# parser.add_argument("--quant", default="none", type=str)
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parser.add_argument("--device", default="cuda", type=str)
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# parser.add_argument("--lora_alpha", default=16, type=int)
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args = parser.parse_args()
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device= args.device
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base_model = args.base_model
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state= args.state_checkpoint
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output= args.output
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with torch.no_grad():
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w: Dict[str, torch.Tensor] = torch.load(base_model, map_location='cpu')
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# merge LoRA-only slim checkpoint into the main weights
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w_state: Dict[str, torch.Tensor] = torch.load(state, map_location='cpu')
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for k in w_state.keys():
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print(k)
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w[k] = w_state[k]
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# merge LoRA weights
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for k in w.keys():
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print(k)
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torch.save(w, output) |