peftmerger.py

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer

model_name = "./llama2-hf"
adapters_name = './checkpoint-760/adapter_model'

print(f"Starting to load the model {model_name} into memory")

m = AutoModelForCausalLM.from_pretrained(
    model_name,
    #load_in_4bit=True,
    torch_dtype=torch.bfloat16,
    #device_map={"": 0},
)
m = PeftModel.from_pretrained(m, adapters_name)
m = m.merge_and_unload()

print(f"Successfully loaded the model {model_name} into memory")

m.save_pretrained("nyc-savvy")