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import torch
import transformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
def load_model(
base,
finetuned,
mode_8bit,
mode_4bit,
force_download_ckpt,
model_cls,
tokenizer_cls
):
if tokenizer_cls is None:
tokenizer_cls = AutoTokenizer
else:
tokenizer_cls = eval(tokenizer_cls)
if model_cls is None:
model_cls = AutoModelForCausalLM
else:
model_cls = eval(model_cls)
print(f"tokenizer_cls: {tokenizer_cls}")
print(f"model_cls: {model_cls}")
tokenizer = tokenizer_cls.from_pretrained(base)
tokenizer.padding_side = "left"
model = model_cls.from_pretrained(
base,
load_in_8bit=mode_8bit,
load_in_4bit=mode_4bit,
torch_dtype=torch.float16,
device_map="auto",
)
if finetuned is not None and \
finetuned != "" and \
finetuned != "N/A":
model = PeftModel.from_pretrained(
model,
finetuned,
# force_download=force_download_ckpt,
device_map={'': 0}
)
return model, tokenizer |