continue to support scripts/finetune.py

scripts/finetune.py

@@ -0,0 +1,52 @@
+"""Prepare and train a model on a dataset. Can also infer from a model or merge lora"""
+import logging
+from pathlib import Path
+
+import fire
+import transformers
+
+from axolotl.cli import (
+    check_accelerate_default_config,
+    do_inference,
+    do_merge_lora,
+    load_cfg,
+    load_datasets,
+    print_axolotl_text_art,
+)
+from axolotl.cli.shard import shard
+from axolotl.common.cli import TrainerCliArgs
+from axolotl.train import train
+
+LOG = logging.getLogger("axolotl.scripts.finetune")
+
+
+def do_cli(config: Path = Path("examples/"), **kwargs):
+    print_axolotl_text_art()
+    LOG.warning(
+        str(
+            PendingDeprecationWarning(
+                "scripts/finetune.py will me replaced with calling axolotl.cli.train"
+            )
+        )
+    )
+    parsed_cfg = load_cfg(config, **kwargs)
+    check_accelerate_default_config()
+    parser = transformers.HfArgumentParser((TrainerCliArgs))
+    parsed_cli_args, _ = parser.parse_args_into_dataclasses(
+        return_remaining_strings=True
+    )
+    if parsed_cli_args.inference:
+        do_inference(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    elif parsed_cli_args.merge_lora:
+        do_merge_lora(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    elif parsed_cli_args.shard:
+        shard(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    else:
+        dataset_meta = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
+        if parsed_cli_args.prepare_ds_only:
+            return
+        train(cfg=parsed_cfg, cli_args=parsed_cli_args, dataset_meta=dataset_meta)
+
+
+if __name__ == "__main__":
+    fire.Fire(do_cli)

src/axolotl/cli/train.py

@@ -6,16 +6,21 @@
 import fire
 import transformers
 
-from axolotl.cli import load_cfg, load_datasets, print_axolotl_text_art, check_accelerate_default_config
+from axolotl.cli import (
+    check_accelerate_default_config,
+    load_cfg,
+    load_datasets,
+    print_axolotl_text_art,
+)
 from axolotl.common.cli import TrainerCliArgs
 from axolotl.train import train
 
 
 def do_cli(config: Path = Path("examples/"), **kwargs):
     # pylint: disable=duplicate-code
     print_axolotl_text_art()
-    check_accelerate_default_config()
     parsed_cfg = load_cfg(config, **kwargs)
+    check_accelerate_default_config()
     parser = transformers.HfArgumentParser((TrainerCliArgs))
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(
         return_remaining_strings=True