utils.py

import torch

from peft import PeftModel
from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer


def generate_prompt(instruction, input=None):
    if input:
        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Input:
{input}
### Response:"""
    else:
        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:"""


def load_tokenizer_and_model(base_model,adapter_model,load_8bit=False):
    if torch.cuda.is_available():
        device = "cuda"
    else:
        device = "cpu"

    try:
        if torch.backends.mps.is_available():
            device = "mps"
    except:  # noqa: E722
        pass
    tokenizer = LlamaTokenizer.from_pretrained(base_model)
    if device == "cuda":
        model = LlamaForCausalLM.from_pretrained(
            base_model,
            load_in_8bit=load_8bit,
            torch_dtype=torch.float16,
            device_map="auto",
        )
        model = PeftModel.from_pretrained(
            model,
            adapter_model,
            torch_dtype=torch.float16,
        )
    elif device == "mps":
        model = LlamaForCausalLM.from_pretrained(
            base_model,
            device_map={"": device},
            torch_dtype=torch.float16,
        )
        model = PeftModel.from_pretrained(
            model,
            adapter_model,
            device_map={"": device},
            torch_dtype=torch.float16,
        )
    else:
        model = LlamaForCausalLM.from_pretrained(
            base_model, device_map={"": device}, low_cpu_mem_usage=True
        )
        model = PeftModel.from_pretrained(
            model,
            adapter_model,
            device_map={"": device},
        )

    if not load_8bit:
        model.half()  # seems to fix bugs for some users.

    model.eval()
    if torch.__version__ >= "2":
        model = torch.compile(model)

    return tokenizer,model,device