move is_llama_derived_model into normalize_config (#524)

scripts/finetune.py

@@ -24,7 +24,7 @@
 from axolotl.utils.data import prepare_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process
-from axolotl.utils.models import load_model_config, load_tokenizer
+from axolotl.utils.models import load_tokenizer
 from axolotl.utils.tokenization import check_dataset_labels
 from axolotl.utils.wandb import setup_wandb_env_vars
 
@@ -216,15 +216,6 @@ def load_cfg(config: Path = Path("examples/"), **kwargs):
             else:
                 cfg[k] = kwargs[k]
 
-    model_config = load_model_config(cfg)
-
-    # figure out if the model is llama
-    cfg.is_llama_derived_model = (
-        (hasattr(model_config, "model_type") and model_config.model_type == "llama")
-        or cfg.is_llama_derived_model
-        or "llama" in cfg.base_model
-        or (cfg.model_type and "llama" in cfg.model_type.lower())
-    )
     validate_config(cfg)
 
     normalize_config(cfg)

src/axolotl/utils/config.py

@@ -6,6 +6,7 @@
 import torch
 
 from axolotl.utils.bench import log_gpu_memory_usage
+from axolotl.utils.models import load_model_config
 
 LOG = logging.getLogger("axolotl")
 
@@ -69,6 +70,16 @@ def normalize_config(cfg):
     else:
         cfg.torch_dtype = torch.float32
 
+    model_config = load_model_config(cfg)
+
+    # figure out if the model is llama
+    cfg.is_llama_derived_model = (
+        (hasattr(model_config, "model_type") and model_config.model_type == "llama")
+        or cfg.is_llama_derived_model
+        or "llama" in cfg.base_model
+        or (cfg.model_type and "llama" in cfg.model_type.lower())
+    )
+
     log_gpu_memory_usage(LOG, "baseline", cfg.device)