diff --git a/autointent/_dump_tools.py b/autointent/_dump_tools.py
index fbd982f5a..645e2e2c0 100644
--- a/autointent/_dump_tools.py
+++ b/autointent/_dump_tools.py
@@ -7,8 +7,16 @@
 import joblib
 import numpy as np
 import numpy.typing as npt
+from peft import PeftModel
 from pydantic import BaseModel
 from sklearn.base import BaseEstimator
+from transformers import (  # type: ignore[attr-defined]
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    PreTrainedModel,
+    PreTrainedTokenizer,
+    PreTrainedTokenizerFast,
+)
 
 from autointent import Embedder, Ranker, VectorIndex
 from autointent.configs import CrossEncoderConfig, EmbedderConfig
@@ -34,6 +42,7 @@ class Dumper:
     pydantic_models: str = "pydantic"
     hf_models = "hf_models"
     hf_tokenizers = "hf_tokenizers"
+    ptuning_models = "ptuning_models"
 
     @staticmethod
     def make_subdirectories(path: Path, exists_ok: bool = False) -> None:
@@ -52,6 +61,7 @@ def make_subdirectories(path: Path, exists_ok: bool = False) -> None:
             path / Dumper.pydantic_models,
             path / Dumper.hf_models,
             path / Dumper.hf_tokenizers,
+            path / Dumper.ptuning_models,
         ]
         for subdir in subdirectories:
             subdir.mkdir(parents=True, exist_ok=exists_ok)
@@ -101,25 +111,38 @@ def dump(obj: Any, path: Path, exists_ok: bool = False, exclude: list[type[Any]]
                 except Exception as e:
                     msg = f"Error dumping pydantic model {key}: {e}"
                     logging.exception(msg)
-            elif (key == "_model" or "model" in key.lower()) and hasattr(val, "save_pretrained"):
+            elif isinstance(val, PeftModel):
+                # dumping peft models is a nightmare...
+                # this might break with new versions of peft
+                try:
+                    if val._is_prompt_learning:  # noqa: SLF001
+                        # strategy to save prompt learning models: save prompt encoder and bert classifier separately
+                        model_path = path / Dumper.ptuning_models / key
+                        model_path.mkdir(parents=True, exist_ok=True)
+                        val.save_pretrained(str(model_path / "peft"))
+                        val.base_model.save_pretrained(model_path / "base_model")  # type: ignore[attr-defined]
+                    else:
+                        # strategy to save lora models: merge adapters and save as usual hugging face model
+                        model_path = path / Dumper.hf_models / key
+                        model_path.mkdir(parents=True, exist_ok=True)
+                        merged_model: PreTrainedModel = val.merge_and_unload()
+                        merged_model.save_pretrained(model_path)  # type: ignore[attr-defined]
+                except Exception as e:
+                    msg = f"Error dumping PeftModel {key}: {e}"
+                    logger.exception(msg)
+            elif isinstance(val, PreTrainedModel):
                 model_path = path / Dumper.hf_models / key
                 model_path.mkdir(parents=True, exist_ok=True)
                 try:
-                    val.save_pretrained(model_path)
-                    class_info = {"module": val.__class__.__module__, "name": val.__class__.__name__}
-                    with (model_path / "class_info.json").open("w") as f:
-                        json.dump(class_info, f)
+                    val.save_pretrained(model_path)  # type: ignore[attr-defined]
                 except Exception as e:
                     msg = f"Error dumping HF model {key}: {e}"
                     logger.exception(msg)
-            elif (key == "_tokenizer" or "tokenizer" in key.lower()) and hasattr(val, "save_pretrained"):
+            elif isinstance(val, PreTrainedTokenizer | PreTrainedTokenizerFast):
                 tokenizer_path = path / Dumper.hf_tokenizers / key
                 tokenizer_path.mkdir(parents=True, exist_ok=True)
                 try:
-                    val.save_pretrained(tokenizer_path)
-                    class_info = {"module": val.__class__.__module__, "name": val.__class__.__name__}
-                    with (tokenizer_path / "class_info.json").open("w") as f:
-                        json.dump(class_info, f)
+                    val.save_pretrained(tokenizer_path)  # type: ignore[union-attr]
                 except Exception as e:
                     msg = f"Error dumping HF tokenizer {key}: {e}"
                     logger.exception(msg)
@@ -202,29 +225,25 @@ def load(  # noqa: C901, PLR0912, PLR0915
                         msg = f"Error loading Pydantic model from {model_dir}: {e}"
                         logger.exception(msg)
                         continue
+            elif child.name == Dumper.ptuning_models:
+                for model_dir in child.iterdir():
+                    try:
+                        model = AutoModelForSequenceClassification.from_pretrained(model_dir / "base_model")
+                        hf_models[model_dir.name] = PeftModel.from_pretrained(model, model_dir / "peft")
+                    except Exception as e:  # noqa: PERF203
+                        msg = f"Error loading PeftModel {model_dir.name}: {e}"
+                        logger.exception(msg)
             elif child.name == Dumper.hf_models:
                 for model_dir in child.iterdir():
                     try:
-                        with (model_dir / "class_info.json").open("r") as f:
-                            class_info = json.load(f)
-
-                        module = __import__(class_info["module"], fromlist=[class_info["name"]])
-                        model_class = getattr(module, class_info["name"])
-
-                        hf_models[model_dir.name] = model_class.from_pretrained(model_dir)
+                        hf_models[model_dir.name] = AutoModelForSequenceClassification.from_pretrained(model_dir)
                     except Exception as e:  # noqa: PERF203
                         msg = f"Error loading HF model {model_dir.name}: {e}"
                         logger.exception(msg)
             elif child.name == Dumper.hf_tokenizers:
                 for tokenizer_dir in child.iterdir():
                     try:
-                        with (tokenizer_dir / "class_info.json").open("r") as f:
-                            class_info = json.load(f)
-
-                        module = __import__(class_info["module"], fromlist=[class_info["name"]])
-                        tokenizer_class = getattr(module, class_info["name"])
-
-                        hf_tokenizers[tokenizer_dir.name] = tokenizer_class.from_pretrained(tokenizer_dir)
+                        hf_tokenizers[tokenizer_dir.name] = AutoTokenizer.from_pretrained(tokenizer_dir)
                     except Exception as e:  # noqa: PERF203
                         msg = f"Error loading HF tokenizer {tokenizer_dir.name}: {e}"
                         logger.exception(msg)
diff --git a/autointent/context/_context.py b/autointent/context/_context.py
index f4cc05bca..c3e35f87f 100644
--- a/autointent/context/_context.py
+++ b/autointent/context/_context.py
@@ -7,7 +7,7 @@
 
 from autointent import Dataset
 from autointent._callbacks import CallbackHandler, get_callbacks
-from autointent.configs import CrossEncoderConfig, DataConfig, EmbedderConfig, LoggingConfig
+from autointent.configs import CrossEncoderConfig, DataConfig, EmbedderConfig, HFModelConfig, LoggingConfig
 
 from .data_handler import DataHandler
 from .optimization_info import OptimizationInfo
@@ -49,7 +49,7 @@ def configure_logging(self, config: LoggingConfig) -> None:
         self.callback_handler = get_callbacks(config.report_to)
         self.optimization_info = OptimizationInfo()
 
-    def configure_transformer(self, config: EmbedderConfig | CrossEncoderConfig) -> None:
+    def configure_transformer(self, config: EmbedderConfig | CrossEncoderConfig | HFModelConfig) -> None:
         """Configure the vector index client and embedder.
 
         Args:
@@ -59,6 +59,8 @@ def configure_transformer(self, config: EmbedderConfig | CrossEncoderConfig) ->
             self.embedder_config = config
         elif isinstance(config, CrossEncoderConfig):
             self.cross_encoder_config = config
+        elif isinstance(config, HFModelConfig):
+            self.transformer_config = config
 
     def set_dataset(self, dataset: Dataset, config: DataConfig) -> None:
         """Set the datasets for training, validation and testing.
@@ -133,31 +135,40 @@ def has_saved_modules(self) -> bool:
     def resolve_embedder(self) -> EmbedderConfig:
         """Resolve the embedder configuration.
 
-        Returns the best embedder configuration or default configuration.
-
-        Raises:
-            RuntimeError: If embedder configuration cannot be resolved.
+        This method returns the configuration with the following priorities:
+        - the best embedder configuration obtained during embedding node optimization
+        - default configuration preset by user with :py:meth:`Context.configure_transformer`
+        - default configuration preset by AutoIntent in :py:class:`autointent.configs.EmbedderConfig`
         """
         try:
             return self.optimization_info.get_best_embedder()
-        except ValueError as e:
+        except ValueError:
             if hasattr(self, "embedder_config"):
                 return self.embedder_config
-            msg = (
-                "Embedder could't be resolved. Either include embedding node into the "
-                "search space or set default config with Context.configure_transformer."
-            )
-            raise RuntimeError(msg) from e
+            return EmbedderConfig()
 
     def resolve_ranker(self) -> CrossEncoderConfig:
         """Resolve the cross-encoder configuration.
 
-        Returns default config if set.
-
-        Raises:
-            RuntimeError: If cross-encoder configuration cannot be resolved.
+        This method returns the configuration with the following priorities:
+        - default configuration preset by user with :py:meth:`Context.configure_transformer`
+        - default configuration preset by AutoIntent in :py:class:`autointent.configs.CrossEncoderConfig`
         """
         if hasattr(self, "cross_encoder_config"):
             return self.cross_encoder_config
-        msg = "Cross-encoder could't be resolved. Set default config with Context.configure_transformer."
-        raise RuntimeError(msg)
+        return CrossEncoderConfig()
+
+    def resolve_transformer(self) -> HFModelConfig:
+        """Resolve the transformer configuration.
+
+        This method returns the configuration with the following priorities:
+        - the best transformer configuration obtained during embedding node optimization
+        - default configuration preset by user with :py:meth:`Context.configure_transformer`
+        - default configuration preset by AutoIntent in :py:class:`autointent.configs.HFModelConfig`
+        """
+        try:
+            return self.optimization_info.get_best_embedder()
+        except ValueError:
+            if hasattr(self, "transformer_config"):
+                return self.transformer_config
+            return HFModelConfig()
diff --git a/autointent/modules/base/_base.py b/autointent/modules/base/_base.py
index 71343e1d6..9cda8b5a3 100644
--- a/autointent/modules/base/_base.py
+++ b/autointent/modules/base/_base.py
@@ -138,9 +138,16 @@ def from_context(cls, context: Context, **kwargs: dict[str, Any]) -> "BaseModule
             Initialized module
         """
 
-    def get_embedder_config(self) -> dict[str, Any] | None:
-        """Get the config of the embedder."""
-        return None
+    @abstractmethod
+    def get_implicit_initialization_params(self) -> dict[str, Any]:
+        """Return default params used in ``__init__`` method.
+
+        Some parameters of the module may be inferred using context rather from ``__init__`` method.
+        But they need to be logged for reproducibility during loading from disk.
+
+        Returns:
+            Dictionary of default params
+        """
 
     @staticmethod
     def score_metrics_ho(params: tuple[Any, Any], metrics_dict: dict[str, Any]) -> dict[str, float]:
diff --git a/autointent/modules/base/_decision.py b/autointent/modules/base/_decision.py
index e9487ed31..eec8e9349 100644
--- a/autointent/modules/base/_decision.py
+++ b/autointent/modules/base/_decision.py
@@ -18,6 +18,9 @@
 class BaseDecision(BaseModule, ABC):
     """Base class for decision modules."""
 
+    def get_implicit_initialization_params(self) -> dict[str, Any]:
+        return {}
+
     @abstractmethod
     def fit(
         self,
diff --git a/autointent/modules/base/_embedding.py b/autointent/modules/base/_embedding.py
index 376fe5a82..22b845eb5 100644
--- a/autointent/modules/base/_embedding.py
+++ b/autointent/modules/base/_embedding.py
@@ -1,6 +1,7 @@
 """Base class for embedding modules."""
 
 from abc import ABC
+from typing import Any
 
 from autointent import Context
 from autointent.custom_types import ListOfLabels
@@ -10,6 +11,9 @@
 class BaseEmbedding(BaseModule, ABC):
     """Base class for embedding modules."""
 
+    def get_implicit_initialization_params(self) -> dict[str, Any]:
+        return {}
+
     def get_train_data(self, context: Context) -> tuple[list[str], ListOfLabels]:
         """Get train data.
 
diff --git a/autointent/modules/base/_regex.py b/autointent/modules/base/_regex.py
index 34656b799..cdd9f389e 100644
--- a/autointent/modules/base/_regex.py
+++ b/autointent/modules/base/_regex.py
@@ -1,9 +1,13 @@
 """Base class for embedding modules."""
 
 from abc import ABC
+from typing import Any
 
 from autointent.modules.base import BaseModule
 
 
 class BaseRegex(BaseModule, ABC):
     """Base class for rule-based modules."""
+
+    def get_implicit_initialization_params(self) -> dict[str, Any]:
+        return {}
diff --git a/autointent/modules/scoring/_bert.py b/autointent/modules/scoring/_bert.py
index be29935de..37331c28c 100644
--- a/autointent/modules/scoring/_bert.py
+++ b/autointent/modules/scoring/_bert.py
@@ -26,8 +26,8 @@ class BertScorer(BaseScorer):
     name = "bert"
     supports_multiclass = True
     supports_multilabel = True
-    _model: Any
-    _tokenizer: Any
+    _model: Any  # transformers AutoModel factory returns Any
+    _tokenizer: Any  # transformers AutoTokenizer factory returns Any
 
     def __init__(
         self,
@@ -56,7 +56,7 @@ def from_context(
         seed: int = 0,
     ) -> "BertScorer":
         if classification_model_config is None:
-            classification_model_config = context.resolve_embedder()
+            classification_model_config = context.resolve_transformer()
 
         report_to = context.logging_config.report_to
 
@@ -69,14 +69,14 @@ def from_context(
             report_to=report_to,
         )
 
-    def get_embedder_config(self) -> dict[str, Any]:
-        return self.classification_model_config.model_dump()
+    def get_implicit_initialization_params(self) -> dict[str, Any]:
+        return {"classification_model_config": self.classification_model_config.model_dump()}
 
-    def __initialize_model(self) -> None:
+    def _initialize_model(self) -> Any:  # noqa: ANN401
         label2id = {i: i for i in range(self._n_classes)}
         id2label = {i: i for i in range(self._n_classes)}
 
-        self._model = AutoModelForSequenceClassification.from_pretrained(
+        return AutoModelForSequenceClassification.from_pretrained(
             self.classification_model_config.model_name,
             trust_remote_code=self.classification_model_config.trust_remote_code,
             num_labels=self._n_classes,
@@ -96,7 +96,7 @@ def fit(
 
         self._tokenizer = AutoTokenizer.from_pretrained(self.classification_model_config.model_name)
 
-        self.__initialize_model()
+        self._model = self._initialize_model()
 
         use_cpu = self.classification_model_config.device == "cpu"
 
@@ -126,7 +126,7 @@ def tokenize_function(examples: dict[str, Any]) -> dict[str, Any]:
                 save_strategy="no",
                 logging_strategy="steps",
                 logging_steps=10,
-                report_to=self.report_to,
+                report_to=self.report_to if self.report_to is not None else "none",
                 use_cpu=use_cpu,
             )
 
diff --git a/autointent/modules/scoring/_description/description.py b/autointent/modules/scoring/_description/description.py
index 657de93ac..2ef7d4b51 100644
--- a/autointent/modules/scoring/_description/description.py
+++ b/autointent/modules/scoring/_description/description.py
@@ -76,9 +76,9 @@ def from_context(
         Returns:
             Initialized DescriptionScorer instance
         """
-        if embedder_config is None:
+        if embedder_config is None and encoder_type == "bi":
             embedder_config = context.resolve_embedder()
-        if cross_encoder_config is None:
+        if cross_encoder_config is None and encoder_type == "cross":
             cross_encoder_config = context.resolve_ranker()
 
         return cls(
@@ -88,21 +88,13 @@ def from_context(
             encoder_type=encoder_type,
         )
 
-    def get_embedder_config(self) -> dict[str, Any]:
-        """Get the configuration of the embedder.
-
-        Returns:
-            Embedder configuration
-        """
-        return self.embedder_config.model_dump()
-
-    def get_cross_encoder_config(self) -> dict[str, Any]:
-        """Get the configuration of the cross-encoder.
-
-        Returns:
-            Cross-encoder configuration
-        """
-        return self.cross_encoder_config.model_dump()
+    def get_implicit_initialization_params(self) -> dict[str, Any]:
+        res = {}
+        if self._encoder_type == "bi":
+            res["embedder_config"] = self.embedder_config.model_dump()
+        else:
+            res["cross_encoder_config"] = self.cross_encoder_config.model_dump()
+        return res
 
     def fit(
         self,
diff --git a/autointent/modules/scoring/_dnnc/dnnc.py b/autointent/modules/scoring/_dnnc/dnnc.py
index 8c389e5a5..48792dbe9 100644
--- a/autointent/modules/scoring/_dnnc/dnnc.py
+++ b/autointent/modules/scoring/_dnnc/dnnc.py
@@ -101,6 +101,12 @@ def from_context(
             cross_encoder_config=cross_encoder_config,
         )
 
+    def get_implicit_initialization_params(self) -> dict[str, Any]:
+        return {
+            "embedder_config": self.embedder_config.model_dump(),
+            "cross_encoder_config": self.cross_encoder_config.model_dump(),
+        }
+
     def fit(self, utterances: list[str], labels: ListOfLabels) -> None:
         """Fit the scorer by training or loading the vector index.
 
diff --git a/autointent/modules/scoring/_knn/knn.py b/autointent/modules/scoring/_knn/knn.py
index 749bbdc31..bad7a6ef8 100644
--- a/autointent/modules/scoring/_knn/knn.py
+++ b/autointent/modules/scoring/_knn/knn.py
@@ -97,13 +97,8 @@ def from_context(
             weights=weights,
         )
 
-    def get_embedder_config(self) -> dict[str, Any]:
-        """Get the name of the embedder.
-
-        Returns:
-            Embedder name
-        """
-        return self.embedder_config.model_dump()
+    def get_implicit_initialization_params(self) -> dict[str, Any]:
+        return {"embedder_config": self.embedder_config.model_dump()}
 
     def fit(self, utterances: list[str], labels: ListOfLabels, clear_cache: bool = False) -> None:
         """Fit the scorer by training or loading the vector index.
diff --git a/autointent/modules/scoring/_knn/rerank_scorer.py b/autointent/modules/scoring/_knn/rerank_scorer.py
index 656fb09b8..09ecab10b 100644
--- a/autointent/modules/scoring/_knn/rerank_scorer.py
+++ b/autointent/modules/scoring/_knn/rerank_scorer.py
@@ -96,6 +96,12 @@ def from_context(
             cross_encoder_config=cross_encoder_config,
         )
 
+    def get_implicit_initialization_params(self) -> dict[str, Any]:
+        return {
+            "embedder_config": self.embedder_config.model_dump(),
+            "cross_encoder_config": self.cross_encoder_config.model_dump(),
+        }
+
     def fit(self, utterances: list[str], labels: ListOfLabels) -> None:
         """Fit the RerankScorer with utterances and labels.
 
diff --git a/autointent/modules/scoring/_linear.py b/autointent/modules/scoring/_linear.py
index be74ada89..0e9bef76a 100644
--- a/autointent/modules/scoring/_linear.py
+++ b/autointent/modules/scoring/_linear.py
@@ -90,13 +90,8 @@ def from_context(
             embedder_config=embedder_config,
         )
 
-    def get_embedder_config(self) -> dict[str, Any]:
-        """Get the name of the embedder.
-
-        Returns:
-            Embedder name
-        """
-        return self.embedder_config.model_dump()
+    def get_implicit_initialization_params(self) -> dict[str, Any]:
+        return {"embedder_config": self.embedder_config.model_dump()}
 
     def fit(
         self,
diff --git a/autointent/modules/scoring/_lora/lora.py b/autointent/modules/scoring/_lora/lora.py
index 80c24c8cb..124cc4baf 100644
--- a/autointent/modules/scoring/_lora/lora.py
+++ b/autointent/modules/scoring/_lora/lora.py
@@ -1,12 +1,13 @@
 """BertScorer class for transformer-based classification with LoRA."""
 
+from pathlib import Path
 from typing import Any
 
 from peft import LoraConfig, get_peft_model
-from transformers import AutoModelForSequenceClassification
 
 from autointent import Context
 from autointent._callbacks import REPORTERS_NAMES
+from autointent._dump_tools import Dumper
 from autointent.configs import HFModelConfig
 from autointent.modules.scoring._bert import BertScorer
 
@@ -59,10 +60,6 @@ class BERTLoRAScorer(BertScorer):
     """
 
     name = "lora"
-    supports_multiclass = True
-    supports_multilabel = True
-    _model: Any
-    _tokenizer: Any
 
     def __init__(
         self,
@@ -72,7 +69,7 @@ def __init__(
         learning_rate: float = 5e-5,
         seed: int = 0,
         report_to: REPORTERS_NAMES | None = None,  # type: ignore[valid-type]
-        **lora_kwargs: dict[str, Any],
+        **lora_kwargs: Any,  # noqa: ANN401
     ) -> None:
         super().__init__(
             classification_model_config=classification_model_config,
@@ -82,7 +79,7 @@ def __init__(
             seed=seed,
             report_to=report_to,
         )
-        self._lora_config = LoraConfig(**lora_kwargs)  # type: ignore[arg-type]
+        self._lora_config = LoraConfig(**lora_kwargs)
 
     @classmethod
     def from_context(
@@ -93,10 +90,10 @@ def from_context(
         batch_size: int = 8,
         learning_rate: float = 5e-5,
         seed: int = 0,
-        **lora_kwargs: dict[str, Any],
+        **lora_kwargs: Any,  # noqa: ANN401
     ) -> "BERTLoRAScorer":
         if classification_model_config is None:
-            classification_model_config = context.resolve_embedder()
+            classification_model_config = context.resolve_transformer()
         return cls(
             classification_model_config=classification_model_config,
             num_train_epochs=num_train_epochs,
@@ -107,11 +104,9 @@ def from_context(
             **lora_kwargs,
         )
 
-    def __initialize_model(self) -> None:
-        self._model = AutoModelForSequenceClassification.from_pretrained(
-            self.classification_model_config.model_name,
-            num_labels=self._n_classes,
-            problem_type="multi_label_classification" if self._multilabel else "single_label_classification",
-            trust_remote_code=self.classification_model_config.trust_remote_code,
-        )
-        self._model = get_peft_model(self._model, self._lora_config)
+    def _initialize_model(self) -> Any:  # noqa: ANN401
+        model = super()._initialize_model()
+        return get_peft_model(model, self._lora_config)
+
+    def dump(self, path: str) -> None:
+        Dumper.dump(self, Path(path), exclude=[LoraConfig])
diff --git a/autointent/modules/scoring/_mlknn/mlknn.py b/autointent/modules/scoring/_mlknn/mlknn.py
index 306453010..3021d9349 100644
--- a/autointent/modules/scoring/_mlknn/mlknn.py
+++ b/autointent/modules/scoring/_mlknn/mlknn.py
@@ -111,13 +111,8 @@ def from_context(
             ignore_first_neighbours=ignore_first_neighbours,
         )
 
-    def get_embedder_config(self) -> dict[str, Any]:
-        """Get the name of the embedder.
-
-        Returns:
-            Embedder name
-        """
-        return self.embedder_config.model_dump()
+    def get_implicit_initialization_params(self) -> dict[str, Any]:
+        return {"embedder_config": self.embedder_config.model_dump()}
 
     def fit(self, utterances: list[str], labels: ListOfLabels) -> None:
         """Fit the scorer by training or loading the vector index and calculating probabilities.
diff --git a/autointent/modules/scoring/_ptuning/ptuning.py b/autointent/modules/scoring/_ptuning/ptuning.py
index d557dc476..417cb138f 100644
--- a/autointent/modules/scoring/_ptuning/ptuning.py
+++ b/autointent/modules/scoring/_ptuning/ptuning.py
@@ -1,15 +1,13 @@
 """PTuningScorer class for ptuning-based classification."""
 
+from pathlib import Path
 from typing import Any
 
-import torch
 from peft import PromptEncoderConfig, get_peft_model
-from transformers import (
-    AutoModelForSequenceClassification,
-)
 
 from autointent import Context
 from autointent._callbacks import REPORTERS_NAMES
+from autointent._dump_tools import Dumper
 from autointent.configs import HFModelConfig
 from autointent.modules.scoring._bert import BertScorer
 
@@ -54,10 +52,6 @@ class PTuningScorer(BertScorer):
     """
 
     name = "ptuning"
-    supports_multiclass = True
-    supports_multilabel = True
-    _model: Any
-    _tokenizer: Any
 
     def __init__(
         self,
@@ -67,7 +61,7 @@ def __init__(
         learning_rate: float = 5e-5,
         seed: int = 0,
         report_to: REPORTERS_NAMES | None = None,  # type: ignore[valid-type]
-        **ptuning_kwargs: dict[str, Any],
+        **ptuning_kwargs: Any,  # noqa: ANN401
     ) -> None:
         super().__init__(
             classification_model_config=classification_model_config,
@@ -77,8 +71,7 @@ def __init__(
             seed=seed,
             report_to=report_to,
         )
-        self._ptuning_config = PromptEncoderConfig(**ptuning_kwargs)  # type: ignore[arg-type]
-        torch.manual_seed(seed)
+        self._ptuning_config = PromptEncoderConfig(task_type="SEQ_CLS", **ptuning_kwargs)
 
     @classmethod
     def from_context(
@@ -89,7 +82,7 @@ def from_context(
         batch_size: int = 8,
         learning_rate: float = 5e-5,
         seed: int = 0,
-        **ptuning_kwargs: dict[str, Any],
+        **ptuning_kwargs: Any,  # noqa: ANN401
     ) -> "PTuningScorer":
         """Create a PTuningScorer instance using a Context object.
 
@@ -103,7 +96,7 @@ def from_context(
             **ptuning_kwargs: Arguments for PromptEncoderConfig
         """
         if classification_model_config is None:
-            classification_model_config = context.resolve_embedder()
+            classification_model_config = context.resolve_transformer()
 
         report_to = context.logging_config.report_to
 
@@ -117,14 +110,10 @@ def from_context(
             **ptuning_kwargs,
         )
 
-    def _initialize_model(self) -> None:
+    def _initialize_model(self) -> Any:  # noqa: ANN401
         """Initialize the model with P-tuning configuration."""
-        model_name = self.classification_model_config.model_name
-        self._model = AutoModelForSequenceClassification.from_pretrained(
-            model_name,
-            num_labels=self._n_classes,
-            problem_type="multi_label_classification" if self._multilabel else "single_label_classification",
-            trust_remote_code=self.classification_model_config.trust_remote_code,
-            return_dict=True,
-        )
-        self._model = get_peft_model(self._model, self._ptuning_config)
+        model = super()._initialize_model()
+        return get_peft_model(model, self._ptuning_config)
+
+    def dump(self, path: str) -> None:
+        Dumper.dump(self, Path(path), exclude=[PromptEncoderConfig])
diff --git a/autointent/modules/scoring/_sklearn/sklearn_scorer.py b/autointent/modules/scoring/_sklearn/sklearn_scorer.py
index 8e5ed51c2..62b340f94 100644
--- a/autointent/modules/scoring/_sklearn/sklearn_scorer.py
+++ b/autointent/modules/scoring/_sklearn/sklearn_scorer.py
@@ -104,6 +104,9 @@ def from_context(
             **clf_args,
         )
 
+    def get_implicit_initialization_params(self) -> dict[str, Any]:
+        return {"embedder_config": self.embedder_config.model_dump()}
+
     def fit(
         self,
         utterances: list[str],
diff --git a/autointent/nodes/_node_optimizer.py b/autointent/nodes/_node_optimizer.py
index a0b8f71b8..c5f91bced 100644
--- a/autointent/nodes/_node_optimizer.py
+++ b/autointent/nodes/_node_optimizer.py
@@ -129,10 +129,7 @@ def objective(
 
         self._logger.debug("Initializing %s module with config: %s", module_name, json.dumps(config))
         module = self.node_info.modules_available[module_name].from_context(context, **config)
-
-        embedder_config = module.get_embedder_config()
-        if embedder_config is not None:
-            config["embedder_config"] = embedder_config
+        config.update(module.get_implicit_initialization_params())
 
         context.callback_handler.start_module(module_name=module_name, num=self._counter, module_kwargs=config)
 
diff --git a/autointent/utils.py b/autointent/utils.py
index f08de3db5..c46d5e609 100644
--- a/autointent/utils.py
+++ b/autointent/utils.py
@@ -9,17 +9,21 @@
 from autointent.custom_types import SearchSpacePreset
 
 
-def load_search_space(path: Path | str) -> list[dict[str, Any]]:
+def load_search_space(path_or_str: Path | str) -> list[dict[str, Any]]:
     """Load hyperparameters search space from file.
 
     Args:
-        path: Path to the search space file.
+        path_or_str: Path to the search space file or string representation of the search space.
 
     Returns:
         List of dictionaries representing the search space.
     """
-    with Path(path).open() as file:
-        return yaml.safe_load(file)  # type: ignore[no-any-return]
+    if isinstance(path_or_str, Path):
+        with path_or_str.open() as file:
+            return yaml.safe_load(file)  # type: ignore[no-any-return]
+    else:
+        # string representation of the search space
+        return yaml.safe_load(path_or_str)  # type: ignore[no-any-return]
 
 
 def load_preset(name: SearchSpacePreset) -> dict[str, Any]:
diff --git a/tests/assets/configs/multiclass.yaml b/tests/assets/configs/multiclass.yaml
index 6828d6505..6e2390b7d 100644
--- a/tests/assets/configs/multiclass.yaml
+++ b/tests/assets/configs/multiclass.yaml
@@ -48,7 +48,6 @@
       classification_model_config: ["prajjwal1/bert-tiny"]
       num_train_epochs: [1]
       batch_size: [8, 16]
-      task_type: ["SEQ_CLS"]
       num_virtual_tokens: [10, 20]
 - node_type: decision
   target_metric: decision_accuracy
diff --git a/tests/assets/configs/multilabel.yaml b/tests/assets/configs/multilabel.yaml
index 3cfe6f63c..e82db8e47 100644
--- a/tests/assets/configs/multilabel.yaml
+++ b/tests/assets/configs/multilabel.yaml
@@ -36,7 +36,6 @@
       classification_model_config: ["prajjwal1/bert-tiny"]
       num_train_epochs: [1]
       batch_size: [8]
-      task_type: ["SEQ_CLS"]
       num_virtual_tokens: [10, 20]
     - module_name: lora
       classification_model_config:
diff --git a/tests/modules/scoring/test_ptuning.py b/tests/modules/scoring/test_ptuning.py
index d90cc7b13..4d6ff2385 100644
--- a/tests/modules/scoring/test_ptuning.py
+++ b/tests/modules/scoring/test_ptuning.py
@@ -17,7 +17,6 @@ def test_ptuning_scorer_dump_load(dataset):
         classification_model_config="prajjwal1/bert-tiny",
         num_train_epochs=1,
         batch_size=8,
-        task_type="SEQ_CLS",
         num_virtual_tokens=10,
         seed=42,
     )
@@ -38,7 +37,6 @@ def test_ptuning_scorer_dump_load(dataset):
             classification_model_config="prajjwal1/bert-tiny",
             num_train_epochs=1,
             batch_size=8,
-            task_type="SEQ_CLS",
             num_virtual_tokens=10,
             seed=42,
         )
@@ -66,7 +64,6 @@ def test_ptuning_prediction(dataset):
         classification_model_config="prajjwal1/bert-tiny",
         num_train_epochs=1,
         batch_size=8,
-        task_type="SEQ_CLS",
         num_virtual_tokens=10,
         seed=42,
     )
@@ -106,7 +103,6 @@ def test_ptuning_cache_clearing(dataset):
         classification_model_config="prajjwal1/bert-tiny",
         num_train_epochs=1,
         batch_size=8,
-        task_type="SEQ_CLS",
         num_virtual_tokens=20,
         seed=42,
     )