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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from optimum.bettertransformer import BetterTransformer
def load_model(
base,
finetuned,
mode_cpu,
mode_mps,
mode_full_gpu,
mode_8bit,
mode_4bit,
force_download_ckpt,
local_files_only
):
tokenizer = AutoTokenizer.from_pretrained(
base, local_files_only=local_files_only
)
tokenizer.padding_side = "left"
if mode_cpu:
print("cpu mode")
model = AutoModelForCausalLM.from_pretrained(
base,
device_map={"": "cpu"},
use_safetensors=False,
local_files_only=local_files_only
)
elif mode_mps:
print("mps mode")
model = AutoModelForCausalLM.from_pretrained(
base,
device_map={"": "mps"},
torch_dtype=torch.float16,
use_safetensors=False,
local_files_only=local_files_only
)
else:
print("gpu mode")
print(f"8bit = {mode_8bit}, 4bit = {mode_4bit}")
model = AutoModelForCausalLM.from_pretrained(
base,
load_in_8bit=mode_8bit,
load_in_4bit=mode_4bit,
device_map="auto",
torch_dtype=torch.float16,
use_safetensors=False,
local_files_only=local_files_only
)
if not mode_8bit and not mode_4bit:
model.half()
# model = BetterTransformer.transform(model)
return model, tokenizer |