diff --git a/fast_llm/config.py b/fast_llm/config.py
index f1c88965..a5e4803f 100644
--- a/fast_llm/config.py
+++ b/fast_llm/config.py
@@ -125,6 +125,8 @@ def __init__(
         # Should raise an Exception in case of failure, and return the validated value.
         # Run before the default validation (type check).
         valid: typing.Optional[typing.Callable[[typing.Any], typing.Any]] = None,
+        # Option to skip (postpone) instantiation of a `Config` field.
+        auto_instantiate: bool = True,
         default=dataclasses.MISSING,
         default_factory=dataclasses.MISSING,
         init: bool = True,
@@ -152,6 +154,7 @@ def __init__(
         self.doc = doc
         self.hint = hint
         self.valid = valid
+        self.auto_instantiate = auto_instantiate
 
 
 class FieldUpdate(dict):
@@ -265,6 +268,10 @@ def wrap(cls):
     return wrap(cls)
 
 
+# A marker to prevent auto instantiation of a config.
+NoAutoInstantiate = object()
+
+
 @dataclasses.dataclass()
 class Config:
     """
@@ -712,10 +719,16 @@ def _from_dict(
                     continue
                 if flat:
                     if isinstance(field.type, type) and issubclass(field.type, Config):
-                        if flat:
-                            out_arg_dict[name] = field.type._from_dict(default, False, True)
+                        assert isinstance(field.default_factory, type) and issubclass(
+                            field.default_factory, field.type
+                        )
+                        if field.auto_instantiate:
+                            if flat:
+                                out_arg_dict[name] = field.default_factory._from_dict(default, False, True)
+                            else:
+                                out_arg_dict[name] = field.default_factory._from_dict(default.pop(name, {}), strict)
                         else:
-                            out_arg_dict[name] = field.type._from_dict(default.pop(name, {}), strict)
+                            out_arg_dict[name] = default.pop(name, {})
                     elif name in default:
                         out_arg_dict[name] = default.pop(name)
                 else:
diff --git a/fast_llm/engine/multi_stage/config.py b/fast_llm/engine/multi_stage/config.py
index d6997105..a816467c 100644
--- a/fast_llm/engine/multi_stage/config.py
+++ b/fast_llm/engine/multi_stage/config.py
@@ -248,44 +248,22 @@ def get_base_model_config_class(cls) -> type[BaseModelConfig]:
     def from_pretrained(
         cls,
         pretrained: CheckpointLoadMetadataConfig,
-        default: typing.Self | None = None,
+        *updates: dict[str | tuple[str, ...], typing.Any] | None,
     ) -> typing.Self:
-        # TODO: Add *updates?
         assert pretrained.path is not None
-        metadata = cls.load_metadata(pretrained)
-        return cls.from_metadata(pretrained, metadata, default)
+        return cls.from_metadata(cls.load_metadata(pretrained), *updates)
 
     @classmethod
     def from_metadata(
         cls,
-        pretrained: CheckpointLoadMetadataConfig,
         metadata: "CheckpointMetadata",
-        default: typing.Self | None = None,
-        updates: dict[str | tuple[str, ...], typing.Any] | None = None,
+        *updates: dict[str | tuple[str, ...], typing.Any] | None,
     ) -> typing.Self:
         # TODO: Standardize to *updates?
         # TODO v0.3: Update, remove support for older checkpoints.
         if metadata.fast_llm_version.major != 0 or metadata.fast_llm_version.minor not in (0, 1, 2):
             raise ValueError(f"Invalid checkpoint version: {metadata.fast_llm_version}")
-        pretrained_config = cls.from_dict(metadata.config)
-        if not pretrained.load_config.load_architecture:
-            assert default is not None
-            config = default.to_copy()
-            config.base_model.compare_architecture(pretrained_config.base_model, pretrained.compare_log_fn)
-        elif pretrained.load_config.load_fast_llm:
-            config = pretrained_config
-        else:
-            with NoAutoValidate():
-                config = cls() if default is None else default.to_copy()
-            if pretrained.load_config.load_base_model:
-                config.base_model = pretrained_config.base_model
-            else:
-                config.base_model = config.base_model.to_copy(pretrained_config.base_model.get_architecture())
-            config.validate()
-
-        if updates:
-            config = config.to_copy(updates)
-        return config
+        return cls.from_dict(metadata.config, *updates)
 
     @classmethod
     def load_metadata(cls, config: CheckpointLoadMetadataConfig) -> "CheckpointMetadata":
@@ -315,7 +293,10 @@ class PretrainedFastLLMModelConfig(Config):
     _abstract = True
     # This configs may be overridden with the pretrained config during validation, so we should be careful about accessing them before.
     model: FastLLMModelConfig = Field(
-        default_factory=FastLLMModelConfig, desc="Configuration for the Fast-LLM model.", hint=FieldHint.core
+        default_factory=FastLLMModelConfig,
+        desc="Configuration for the Fast-LLM model.",
+        hint=FieldHint.core,
+        auto_instantiate=False,
     )
     pretrained: CheckpointLoadConfig = Field(
         default_factory=CheckpointLoadConfig,
@@ -327,8 +308,10 @@ def _validate(self) -> None:
         assert self.model is not None
         self.pretrained.setup(self.model)
         self.pretrained.validate()
-        if self.pretrained.path is not None:
-            self.model = self.model.from_pretrained(self.pretrained, default=self.model)
+        if self.pretrained.path is None:
+            self.model = self.get_field("model").default_factory.from_dict(self.model)
+        else:
+            self.model = self.model.from_pretrained(self.pretrained, self.model)
         self._setup()
         super()._validate()
 
diff --git a/fast_llm/models/custom/config.py b/fast_llm/models/custom/config.py
index b86722f8..1daaf2c8 100644
--- a/fast_llm/models/custom/config.py
+++ b/fast_llm/models/custom/config.py
@@ -55,7 +55,7 @@ def get_huggingface_model_class(cls) -> type["HuggingfaceCustomModelForCausalLM"
 
 @config_class()
 class PretrainedCustomModelConfig(PretrainedGPTModelConfig):
-    model: CustomModelConfig = FieldUpdate(default_factory=CustomModelConfig)
+    model: CustomModelConfig = FieldUpdate()
 
 
 @config_class()