import os import sys import torch import transformers from peft import PeftModel from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer from .globals import Global def get_device(): if torch.cuda.is_available(): return "cuda" else: return "cpu" try: if torch.backends.mps.is_available(): return "mps" except: # noqa: E722 pass device = get_device() def get_base_model(): load_base_model() return Global.loaded_base_model def get_model_with_lora(lora_weights: str = "tloen/alpaca-lora-7b"): if device == "cuda": return PeftModel.from_pretrained( get_base_model(), lora_weights, torch_dtype=torch.float16, ) elif device == "mps": return PeftModel.from_pretrained( get_base_model(), lora_weights, device_map={"": device}, torch_dtype=torch.float16, ) else: return PeftModel.from_pretrained( get_base_model(), lora_weights, device_map={"": device}, ) def get_tokenizer(): load_base_model() return Global.loaded_tokenizer def load_base_model(): if Global.loaded_tokenizer is None: Global.loaded_tokenizer = LlamaTokenizer.from_pretrained( Global.base_model) if Global.loaded_base_model is None: if device == "cuda": Global.loaded_base_model = LlamaForCausalLM.from_pretrained( Global.base_model, load_in_8bit=Global.load_8bit, torch_dtype=torch.float16, device_map="auto", ) elif device == "mps": Global.loaded_base_model = LlamaForCausalLM.from_pretrained( Global.base_model, device_map={"": device}, torch_dtype=torch.float16, ) else: model = LlamaForCausalLM.from_pretrained( base_model, device_map={"": device}, low_cpu_mem_usage=True )