diff --git a/scripts/extract_lora.py b/scripts/extract_lora.py
deleted file mode 100644
index be88c5705..000000000
--- a/scripts/extract_lora.py
+++ /dev/null
@@ -1,163 +0,0 @@
-# import logging
-# import os
-# import random
-# import signal
-# import sys
-# from pathlib import Path
-
-# import fire
-# import torch
-# import yaml
-# from addict import Dict
-
-# from peft import set_peft_model_state_dict, get_peft_model_state_dict
-
-# # add src to the pythonpath so we don't need to pip install this
-# project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-# src_dir = os.path.join(project_root, "src")
-# sys.path.insert(0, src_dir)
-
-# from axolotl.utils.data import load_prepare_datasets
-# from axolotl.utils.models import load_model
-# from axolotl.utils.trainer import setup_trainer
-# from axolotl.utils.wandb import setup_wandb_env_vars
-
-# logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO"))
-
-
-# def choose_device(cfg):
-#     def get_device():
-#         if torch.cuda.is_available():
-#             return "cuda"
-#         else:
-#             try:
-#                 if torch.backends.mps.is_available():
-#                     return "mps"
-#             except:
-#                 return "cpu"
-
-#     cfg.device = get_device()
-#     if cfg.device == "cuda":
-#         cfg.device_map = {"": cfg.local_rank}
-#     else:
-#         cfg.device_map = {"": cfg.device}
-
-
-# def choose_config(path: Path):
-#     yaml_files = [file for file in path.glob("*.yml")]
-
-#     if not yaml_files:
-#         raise ValueError(
-#             "No YAML config files found in the specified directory. Are you using a .yml extension?"
-#         )
-
-#     print("Choose a YAML file:")
-#     for idx, file in enumerate(yaml_files):
-#         print(f"{idx + 1}. {file}")
-
-#     chosen_file = None
-#     while chosen_file is None:
-#         try:
-#             choice = int(input("Enter the number of your choice: "))
-#             if 1 <= choice <= len(yaml_files):
-#                 chosen_file = yaml_files[choice - 1]
-#             else:
-#                 print("Invalid choice. Please choose a number from the list.")
-#         except ValueError:
-#             print("Invalid input. Please enter a number.")
-
-#     return chosen_file
-
-
-# def save_latest_checkpoint_as_lora(
-#     config: Path = Path("configs/"),
-#     prepare_ds_only: bool = False,
-#     **kwargs,
-# ):
-#     if Path(config).is_dir():
-#         config = choose_config(config)
-
-#     # load the config from the yaml file
-#     with open(config, "r") as f:
-#         cfg: Dict = Dict(lambda: None, yaml.load(f, Loader=yaml.Loader))
-#     # if there are any options passed in the cli, if it is something that seems valid from the yaml,
-#     # then overwrite the value
-#     cfg_keys = dict(cfg).keys()
-#     for k in kwargs:
-#         if k in cfg_keys:
-#             # handle booleans
-#             if isinstance(cfg[k], bool):
-#                 cfg[k] = bool(kwargs[k])
-#             else:
-#                 cfg[k] = kwargs[k]
-
-#     # setup some derived config / hyperparams
-#     cfg.gradient_accumulation_steps = cfg.batch_size // cfg.micro_batch_size
-#     cfg.world_size = int(os.environ.get("WORLD_SIZE", 1))
-#     cfg.local_rank = int(os.environ.get("LOCAL_RANK", 0))
-#     assert cfg.local_rank == 0, "Run this with only one device!"
-
-#     choose_device(cfg)
-#     cfg.ddp = False
-
-#     if cfg.device == "mps":
-#         cfg.load_in_8bit = False
-#         cfg.tf32 = False
-#         if cfg.bf16:
-#             cfg.fp16 = True
-#         cfg.bf16 = False
-
-#     # Load the model and tokenizer
-#     logging.info("loading model, tokenizer, and lora_config...")
-#     model, tokenizer, lora_config = load_model(
-#         cfg.base_model,
-#         cfg.base_model_config,
-#         cfg.model_type,
-#         cfg.tokenizer_type,
-#         cfg,
-#         adapter=cfg.adapter,
-#         inference=True,
-#     )
-
-#     model.config.use_cache = False
-
-#     if torch.__version__ >= "2" and sys.platform != "win32":
-#         logging.info("Compiling torch model")
-#         model = torch.compile(model)
-
-#     possible_checkpoints = [str(cp) for cp in Path(cfg.output_dir).glob("checkpoint-*")]
-#     if len(possible_checkpoints) > 0:
-#         sorted_paths = sorted(
-#             possible_checkpoints, key=lambda path: int(path.split("-")[-1])
-#         )
-#         resume_from_checkpoint = sorted_paths[-1]
-#     else:
-#         raise FileNotFoundError("Checkpoints folder not found")
-
-#     pytorch_bin_path = os.path.join(resume_from_checkpoint, "pytorch_model.bin")
-
-#     assert os.path.exists(pytorch_bin_path), "Bin not found"
-
-#     logging.info(f"Loading {pytorch_bin_path}")
-#     adapters_weights = torch.load(pytorch_bin_path, map_location="cpu")
-
-#     # d = get_peft_model_state_dict(model)
-#     print(model.load_state_dict(adapters_weights))
-#     # with open('b.log', "w") as f:
-#     #     f.write(str(d.keys()))
-#     assert False
-
-#     print((adapters_weights.keys()))
-#     with open("a.log", "w") as f:
-#         f.write(str(adapters_weights.keys()))
-#     assert False
-
-#     logging.info("Setting peft model state dict")
-#     set_peft_model_state_dict(model, adapters_weights)
-
-#     logging.info(f"Set Completed!!! Saving pre-trained model to {cfg.output_dir}")
-#     model.save_pretrained(cfg.output_dir)
-
-
-# if __name__ == "__main__":
-#     fire.Fire(save_latest_checkpoint_as_lora)