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import torch
from peft import PeftModel
from transformers import LlamaTokenizer, LlamaForCausalLM
from optimum.bettertransformer import BetterTransformer
def load_model(
base,
finetuned,
mode_cpu,
mode_mps,
mode_full_gpu,
mode_8bit,
mode_4bit,
force_download_ckpt
):
tokenizer = LlamaTokenizer.from_pretrained(base)
tokenizer.pad_token_id = 0
tokenizer.padding_side = "left"
if mode_cpu:
print("cpu mode")
model = LlamaForCausalLM.from_pretrained(
base,
device_map={"": "cpu"},
use_safetensors=False
)
if finetuned is not None and \
finetuned != "" and \
finetuned != "N/A":
model = PeftModel.from_pretrained(
model,
finetuned,
device_map={"": "cpu"}
# force_download=force_download_ckpt,
)
else:
model = BetterTransformer.transform(model)
elif mode_mps:
print("mps mode")
model = LlamaForCausalLM.from_pretrained(
base,
device_map={"": "mps"},
torch_dtype=torch.float16,
use_safetensors=False
)
if finetuned is not None and \
finetuned != "" and \
finetuned != "N/A":
model = PeftModel.from_pretrained(
model,
finetuned,
torch_dtype=torch.float16,
device_map={"": "mps"}
# force_download=force_download_ckpt,
)
else:
model = BetterTransformer.transform(model)
else:
print("gpu mode")
print(f"8bit = {mode_8bit}, 4bit = {mode_4bit}")
model = LlamaForCausalLM.from_pretrained(
base,
load_in_8bit=mode_8bit,
load_in_4bit=mode_4bit,
torch_dtype=torch.float16,
device_map="auto",
use_safetensors=False
)
if not mode_8bit and not mode_4bit:
model.half()
if finetuned is not None and \
finetuned != "" and \
finetuned != "N/A":
model = PeftModel.from_pretrained(
model,
finetuned,
# force_download=force_download_ckpt,
)
else:
model = BetterTransformer.transform(model)
return model, tokenizer |