set model merge dtype based on cfg

scripts/finetune.py

@@ -155,6 +155,23 @@ def check_not_in(list1: List[str], list2: Union[Dict[str, Any], List[str]]) -> b
     return not any(el in list2 for el in list1)
 
 
+def merge_lora(model, tokenizer, cfg):
+    LOG.info("running merge of LoRA with base model")
+    model = model.merge_and_unload()
+    model_dtype = torch.bfloat16 if cfg.bf16 or cfg.bfloat16 else torch.float16
+    model.to(dtype=model_dtype)
+    if cfg.hub_model_id:
+        model.push_to_hub("hub_model_id")
+
+    if cfg.local_rank == 0:
+        LOG.info("saving merged model")
+        model.save_pretrained(
+            str(Path(cfg.output_dir) / "merged"),
+            safe_serialization=cfg.save_safetensors is True,
+        )
+        tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
+
+
 def train(
     config: Path = Path("configs/"),
     prepare_ds_only: bool = False,
@@ -214,17 +231,7 @@ def train(
     safe_serialization = cfg.save_safetensors is True
 
     if "merge_lora" in kwargs and cfg.adapter is not None:
-        LOG.info("running merge of LoRA with base model")
-        model = model.merge_and_unload()
-        model.to(dtype=torch.float16)
-
-        if cfg.local_rank == 0:
-            LOG.info("saving merged model")
-            model.save_pretrained(
-                str(Path(cfg.output_dir) / "merged"),
-                safe_serialization=safe_serialization,
-            )
-            tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
+        merge_lora(model, tokenizer, cfg)
         return
 
     if cfg.inference:
@@ -310,6 +317,9 @@ def train(
             model = BetterTransformer.reverse(model)
         model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
 
+    if cfg.adapter is not None:
+        merge_lora(model, tokenizer, cfg)
+
 
 if __name__ == "__main__":
     fire.Fire(train)