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import importlib |
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import logging |
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import os |
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import pathlib |
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import random |
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import signal |
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import sys |
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from pathlib import Path |
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from typing import Optional |
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import fire |
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import torch |
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import transformers |
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import yaml |
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from attrdict import AttrDefault |
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from axolotl.utils.tokenization import check_dataset_labels |
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project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) |
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src_dir = os.path.join(project_root, "src") |
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sys.path.insert(0, src_dir) |
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from axolotl.utils.data import load_prepare_datasets |
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from axolotl.utils.models import load_model |
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from axolotl.utils.trainer import setup_trainer |
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from axolotl.utils.wandb import setup_wandb_env_vars |
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logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO")) |
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DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared" |
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def choose_device(cfg): |
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def get_device(): |
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if torch.cuda.is_available(): |
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return "cuda" |
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else: |
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try: |
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if torch.backends.mps.is_available(): |
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return "mps" |
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except: |
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return "cpu" |
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cfg.device = get_device() |
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if cfg.device == "cuda": |
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cfg.device_map = {"": cfg.local_rank} |
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else: |
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cfg.device_map = {"": cfg.device} |
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def get_multi_line_input() -> Optional[str]: |
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print("Give me an instruction (Ctrl + D to finish): ") |
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instruction = "" |
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for line in sys.stdin: |
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instruction += line |
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return instruction |
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def do_inference(cfg, model, tokenizer, prompter="AlpacaPrompter"): |
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tokenizer.add_special_tokens({"unk_token": "<unk>"}) |
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tokenizer.add_special_tokens({"bos_token": "<s>"}) |
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tokenizer.add_special_tokens({"eos_token": "</s>"}) |
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prompter_module = getattr(importlib.import_module("axolotl.prompters"), prompter) |
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while True: |
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instruction = get_multi_line_input() |
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if not instruction: |
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return |
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prompt = prompter_module().build_prompt(instruction=instruction) |
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batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True) |
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model.eval() |
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with torch.no_grad(): |
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generated = model.generate( |
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inputs=batch["input_ids"].to(cfg.device), |
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do_sample=True, |
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use_cache=True, |
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repetition_penalty=1.1, |
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max_new_tokens=100, |
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temperature=0.9, |
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top_p=0.95, |
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top_k=40, |
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return_dict_in_generate=True, |
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output_attentions=False, |
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output_hidden_states=False, |
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output_scores=False, |
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) |
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print(tokenizer.decode(generated["sequences"].cpu().tolist()[0])) |
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def choose_config(path: Path): |
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yaml_files = [file for file in path.glob("*.yml")] |
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if not yaml_files: |
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raise ValueError( |
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"No YAML config files found in the specified directory. Are you using a .yml extension?" |
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) |
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print("Choose a YAML file:") |
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for idx, file in enumerate(yaml_files): |
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print(f"{idx + 1}. {file}") |
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chosen_file = None |
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while chosen_file is None: |
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try: |
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choice = int(input("Enter the number of your choice: ")) |
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if 1 <= choice <= len(yaml_files): |
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chosen_file = yaml_files[choice - 1] |
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else: |
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print("Invalid choice. Please choose a number from the list.") |
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except ValueError: |
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print("Invalid input. Please enter a number.") |
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return chosen_file |
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def train( |
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config: Path = Path("configs/"), |
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prepare_ds_only: bool = False, |
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**kwargs, |
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): |
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if Path(config).is_dir(): |
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config = choose_config(config) |
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with open(config, "r") as f: |
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cfg: AttrDefault = AttrDefault(lambda: None, yaml.load(f, Loader=yaml.Loader)) |
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cfg_keys = dict(cfg).keys() |
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for k in kwargs: |
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if k in cfg_keys: |
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if isinstance(cfg[k], bool): |
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cfg[k] = bool(kwargs[k]) |
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else: |
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cfg[k] = kwargs[k] |
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cfg.gradient_accumulation_steps = cfg.batch_size // cfg.micro_batch_size |
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cfg.world_size = int(os.environ.get("WORLD_SIZE", 1)) |
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cfg.local_rank = int(os.environ.get("LOCAL_RANK", 0)) |
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choose_device(cfg) |
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cfg.ddp = cfg.ddp if cfg.ddp is not None else cfg.world_size != 1 |
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if cfg.ddp: |
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cfg.device_map = {"": int(os.environ.get("LOCAL_RANK", 0))} |
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cfg.gradient_accumulation_steps = ( |
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cfg.gradient_accumulation_steps // cfg.world_size |
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) |
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setup_wandb_env_vars(cfg) |
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if cfg.device == "mps": |
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cfg.load_in_8bit = False |
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cfg.tf32 = False |
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if cfg.bf16: |
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cfg.fp16 = True |
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cfg.bf16 = False |
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logging.info("loading model, tokenizer, and peft_config...") |
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model, tokenizer, peft_config = load_model( |
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cfg.base_model, |
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cfg.base_model_config, |
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cfg.model_type, |
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cfg.tokenizer_type, |
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cfg, |
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adapter=cfg.adapter, |
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inference=("inference" in kwargs), |
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) |
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if "inference" in kwargs: |
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logging.info("calling do_inference function") |
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do_inference(cfg, model, tokenizer) |
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return |
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if "shard" in kwargs: |
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model.save_pretrained(cfg.output_dir) |
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return |
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train_dataset, eval_dataset = load_prepare_datasets( |
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tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH |
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) |
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if prepare_ds_only: |
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logging.info("Finished preparing dataset. Exiting...") |
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return |
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if cfg.debug: |
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logging.info("check_dataset_labels...") |
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check_dataset_labels( |
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train_dataset.select( |
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[random.randrange(0, len(train_dataset) - 1) for i in range(5)] |
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), |
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tokenizer, |
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) |
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trainer = setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer) |
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model.config.use_cache = False |
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if torch.__version__ >= "2" and sys.platform != "win32": |
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logging.info("Compiling torch model") |
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model = torch.compile(model) |
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if peft_config: |
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logging.info(f"Pre-saving adapter config to {cfg.output_dir}") |
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peft_config.save_pretrained(cfg.output_dir) |
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if cfg.local_rank == 0: |
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signal.signal( |
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signal.SIGINT, |
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lambda signal, frame: (model.save_pretrained(cfg.output_dir), exit(0)), |
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) |
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logging.info("Starting trainer...") |
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resume_from_checkpoint = cfg.resume_from_checkpoint |
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if cfg.resume_from_checkpoint is None and cfg.auto_resume_from_checkpoints: |
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possible_checkpoints = [ |
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str(cp) for cp in Path(cfg.output_dir).glob("checkpoint-*") |
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] |
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if len(possible_checkpoints) > 0: |
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sorted_paths = sorted( |
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possible_checkpoints, key=lambda path: int(path.split("-")[-1]) |
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) |
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resume_from_checkpoint = sorted_paths[-1] |
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logging.info( |
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f"Using Auto-resume functionality to start with checkpoint at {resume_from_checkpoint}" |
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) |
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trainer.train(resume_from_checkpoint=resume_from_checkpoint) |
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logging.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}") |
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model.save_pretrained(cfg.output_dir) |
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if __name__ == "__main__": |
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fire.Fire(train) |
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