merge_peft_adapter.py

from dataclasses import dataclass, field
from typing import Optional

import torch
from peft import PeftConfig, PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


@dataclass
class ScriptArguments:
    """The input names representing the Adapter and Base model fine-tuned with
    PEFT, and the output name representing the merged model."""

    adapter_model_name: Optional[str] = field(
        default=None, metadata={'help': 'the adapter name'})
    base_model_name: Optional[str] = field(
        default=None, metadata={'help': 'the base model name'})
    output_name: Optional[str] = field(
        default=None, metadata={'help': 'the merged model name'})


parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
assert script_args.adapter_model_name is not None, 'please provide the name of the Adapter you would like to merge'  # noqa: E501
assert script_args.base_model_name is not None, 'please provide the name of the Base model'  # noqa: E501
assert script_args.output_name is not None, 'please provide the output name of the merged model'  # noqa: E501

peft_config = PeftConfig.from_pretrained(script_args.adapter_model_name)
model = AutoModelForCausalLM.from_pretrained(
    script_args.base_model_name,
    return_dict=True,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True)

tokenizer = AutoTokenizer.from_pretrained(
    script_args.base_model_name, trust_remote_code=True)

# Load the PEFT model
model = PeftModel.from_pretrained(model, script_args.adapter_model_name)
model.eval()

model = model.merge_and_unload()

model.save_pretrained(f'{script_args.output_name}')
tokenizer.save_pretrained(f'{script_args.output_name}')