add save_only_model arg

src/axolotl/cli/merge_lora.py

@@ -8,7 +8,6 @@ import transformers
 
 from axolotl.cli import do_merge_lora, load_cfg, print_axolotl_text_art
 from axolotl.common.cli import TrainerCliArgs
-from axolotl.utils.dict import DictDefault
 
 
 def do_cli(config: Path = Path("examples/"), **kwargs):
@@ -28,26 +27,21 @@ def do_cli(config: Path = Path("examples/"), **kwargs):
         flash_attention=False,
         **kwargs,
     )
-    cfg = modify_cfg_for_merge(parsed_cfg)
 
-    do_merge_lora(cfg=cfg, cli_args=parsed_cli_args)
+    if not parsed_cfg.lora_model_dir and parsed_cfg.output_dir:
-
+        parsed_cfg.lora_model_dir = parsed_cfg.output_dir
-
+    if not Path(parsed_cfg.lora_model_dir).exists():
-def modify_cfg_for_merge(cfg: DictDefault) -> DictDefault:
-    if not cfg.lora_model_dir and cfg.output_dir:
-        cfg.lora_model_dir = cfg.output_dir
-    if not Path(cfg.lora_model_dir).exists():
         raise ValueError(
-            f"Target directory for merge: `{cfg.lora_model_dir}` does not exist."
+            f"Target directory for merge: `{parsed_cfg.lora_model_dir}` does not exist."
         )
 
-    cfg.load_in_4bit = False
+    parsed_cfg.load_in_4bit = False
-    cfg.load_in_8bit = False
+    parsed_cfg.load_in_8bit = False
-    cfg.flash_attention = False
+    parsed_cfg.flash_attention = False
-    cfg.deepspeed = None
+    parsed_cfg.deepspeed = None
-    cfg.fsdp = None
+    parsed_cfg.fsdp = None
 
-    return cfg
+    do_merge_lora(cfg=parsed_cfg, cli_args=parsed_cli_args)
 
 
 if __name__ == "__main__":

src/axolotl/core/trainer_builder.py

@@ -1058,6 +1058,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.save_safetensors is not None:
             training_arguments_kwargs["save_safetensors"] = self.cfg.save_safetensors
 
+        if self.cfg.save_only_model is not None:
+            training_arguments_kwargs["save_only_model"] = self.cfg.save_only_model
+
         if self.cfg.sample_packing_eff_est:
             training_arguments_kwargs[
                 "sample_packing_efficiency"

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -355,6 +355,7 @@ class ModelOutputConfig(BaseModel):
     hub_model_id: Optional[str] = None
     hub_strategy: Optional[str] = None
     save_safetensors: Optional[bool] = None
+    save_only_model: Optional[bool] = None
 
 
 class MLFlowConfig(BaseModel):

tests/e2e/test_lora_llama.py

@@ -1,16 +1,13 @@
 """
 E2E tests for lora llama
 """
-import json
+
 import logging
 import os
 import unittest
 from pathlib import Path
 
-from transformers.utils import is_torch_bf16_gpu_available
+from axolotl.cli import load_datasets
-
-from axolotl.cli import do_merge_lora, load_datasets
-from axolotl.cli.merge_lora import modify_cfg_for_merge
 from axolotl.common.cli import TrainerCliArgs
 from axolotl.train import train
 from axolotl.utils.config import normalize_config
@@ -42,6 +39,11 @@ class TestLoraLlama(unittest.TestCase):
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
                 "val_set_size": 0.1,
+                "special_tokens": {
+                    "unk_token": "<unk>",
+                    "bos_token": "<s>",
+                    "eos_token": "</s>",
+                },
                 "datasets": [
                     {
                         "path": "mhenrichsen/alpaca_2k_test",
@@ -55,7 +57,6 @@ class TestLoraLlama(unittest.TestCase):
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
-                "max_steps": 10,
             }
         )
         normalize_config(cfg)
@@ -64,67 +65,3 @@ class TestLoraLlama(unittest.TestCase):
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "adapter_model.bin").exists()
-
-    @with_temp_dir
-    def test_lora_merge(self, temp_dir):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
-                "sequence_len": 1024,
-                "load_in_8bit": True,
-                "adapter": "lora",
-                "lora_r": 32,
-                "lora_alpha": 64,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "val_set_size": 0.1,
-                "datasets": [
-                    {
-                        "path": "mhenrichsen/alpaca_2k_test",
-                        "type": "alpaca",
-                    },
-                ],
-                "num_epochs": 2,
-                "micro_batch_size": 8,
-                "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_torch",
-                "lr_scheduler": "cosine",
-                "max_steps": 10,
-                "bf16": "auto",
-            }
-        )
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(temp_dir) / "adapter_model.bin").exists()
-
-        cfg.lora_model_dir = cfg.output_dir
-        cfg.load_in_4bit = False
-        cfg.load_in_8bit = False
-        cfg.flash_attention = False
-        cfg.deepspeed = None
-        cfg.fsdp = None
-
-        cfg = modify_cfg_for_merge(cfg)
-        cfg.merge_lora = True
-
-        cli_args = TrainerCliArgs(merge_lora=True)
-
-        do_merge_lora(cfg=cfg, cli_args=cli_args)
-        assert (Path(temp_dir) / "merged/pytorch_model.bin").exists()
-
-        with open(
-            Path(temp_dir) / "merged/config.json", "r", encoding="utf-8"
-        ) as f_handle:
-            config = f_handle.read()
-        config = json.loads(config)
-        if is_torch_bf16_gpu_available():
-            assert config["torch_dtype"] == "bfloat16"
-        else:
-            assert config["torch_dtype"] == "float16"