diff --git a/tests/cli/test_classification.py b/tests/cli/test_classification.py
index 8de6b4b..115e475 100755
--- a/tests/cli/test_classification.py
+++ b/tests/cli/test_classification.py
@@ -1,26 +1,24 @@
 from pathlib import Path
 
+import pytest
 from typer.testing import CliRunner
 
 from tests.constants import FIXTURES_PATH
 from turbo_alignment.cli import app
+from turbo_alignment.settings.pipelines.train.classification import (
+    ClassificationTrainExperimentSettings,
+)
 
 runner = CliRunner()
 
 
-def test_classification_train():
-    result = runner.invoke(
-        app,
-        [
-            'train_classification',
-            '--experiment_settings_path',
-            FIXTURES_PATH / 'configs/train/classification/base.json',
-        ],
-        catch_exceptions=False,
-    )
+@pytest.mark.parametrize(
+    'config_path',
+    [
+        FIXTURES_PATH / 'configs/train/classification/base.json',
+    ],
+)
+def test_classification_train(config_path: Path):
+    result = runner.invoke(app, ['train_classification', '--experiment_settings_path', str(config_path)])
     assert result.exit_code == 0
-    assert Path('test_train_classification_output').is_dir()
-
-
-if __name__ == '__main__':
-    test_classification_train()
+    assert ClassificationTrainExperimentSettings.parse_file(config_path).log_path.is_dir()
diff --git a/tests/cli/test_dpo_train.py b/tests/cli/test_dpo_train.py
index f652a64..23bd4af 100755
--- a/tests/cli/test_dpo_train.py
+++ b/tests/cli/test_dpo_train.py
@@ -1,26 +1,26 @@
-# from pathlib import Path
+from pathlib import Path
 
-# import pytest
-# from typer.testing import CliRunner
+import pytest
+from typer.testing import CliRunner
 
-# from tests.constants import FIXTURES_PATH
-# from turbo_alignment.cli import app
-# from turbo_alignment.settings.pipelines.train.dpo import DPOTrainExperimentSettings
+from tests.constants import FIXTURES_PATH
+from turbo_alignment.cli import app
+from turbo_alignment.settings.pipelines.train.dpo import DPOTrainExperimentSettings
 
-# runner = CliRunner()
+runner = CliRunner()
 
 
-# @pytest.mark.parametrize(
-#     'config_path',
-#     [
-#         FIXTURES_PATH / 'configs/train/dpo/base.json',
-#         FIXTURES_PATH / 'configs/train/dpo/simpo.json',
-#     ],
-# )
-# def test_dpo_train(config_path: Path):
-#     result = runner.invoke(
-#         app,
-#         ['train_dpo', '--experiment_settings_path', str(config_path)],
-#     )
-#     assert result.exit_code == 0
-#     assert DPOTrainExperimentSettings.parse_file(config_path).log_path.is_dir()
+@pytest.mark.parametrize(
+    'config_path',
+    [
+        FIXTURES_PATH / 'configs/train/dpo/base.json',
+        FIXTURES_PATH / 'configs/train/dpo/simpo.json',
+    ],
+)
+def test_dpo_train(config_path: Path):
+    result = runner.invoke(
+        app,
+        ['train_dpo', '--experiment_settings_path', str(config_path)],
+    )
+    assert result.exit_code == 0
+    assert DPOTrainExperimentSettings.parse_file(config_path).log_path.is_dir()
diff --git a/tests/cli/test_kto_train.py b/tests/cli/test_kto_train.py
index b7ddb7d..18cb27d 100755
--- a/tests/cli/test_kto_train.py
+++ b/tests/cli/test_kto_train.py
@@ -1,23 +1,23 @@
-# from pathlib import Path
+from pathlib import Path
 
-# import pytest
-# from typer.testing import CliRunner
+import pytest
+from typer.testing import CliRunner
 
-# from tests.constants import FIXTURES_PATH
-# from turbo_alignment.cli import app
-# from turbo_alignment.settings.pipelines.train.kto import KTOTrainExperimentSettings
+from tests.constants import FIXTURES_PATH
+from turbo_alignment.cli import app
+from turbo_alignment.settings.pipelines.train.kto import KTOTrainExperimentSettings
 
-# runner = CliRunner()
+runner = CliRunner()
 
 
-# @pytest.mark.parametrize(
-#     'config_path',
-#     [FIXTURES_PATH / 'configs/train/kto/base.json'],
-# )
-# def test_dpo_train(config_path: Path):
-#     result = runner.invoke(
-#         app,
-#         ['train_kto', '--experiment_settings_path', str(config_path)],
-#     )
-#     assert result.exit_code == 0
-#     assert KTOTrainExperimentSettings.parse_file(config_path).log_path.is_dir()
+@pytest.mark.parametrize(
+    'config_path',
+    [FIXTURES_PATH / 'configs/train/kto/base.json'],
+)
+def test_dpo_train(config_path: Path):
+    result = runner.invoke(
+        app,
+        ['train_kto', '--experiment_settings_path', str(config_path)],
+    )
+    assert result.exit_code == 0
+    assert KTOTrainExperimentSettings.parse_file(config_path).log_path.is_dir()
diff --git a/tests/conftest.py b/tests/conftest.py
index da98770..371a7f6 100755
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,96 +1,96 @@
-# import json
+import json
 
-# from pytest import fixture
-# from transformers import AutoTokenizer
+from pytest import fixture
+from transformers import AutoTokenizer
 
-# from turbo_alignment.dataset.registry import DatasetRegistry
-# from turbo_alignment.settings.datasets.base import (
-#     DatasetSourceSettings,
-#     DatasetStrategy,
-#     DatasetType,
-# )
-# from turbo_alignment.settings.datasets.chat import (
-#     ChatDatasetSettings,
-#     ChatPromptTemplate,
-# )
-# from turbo_alignment.settings.datasets.pair_preference import (
-#     PairPreferenceDatasetSettings,
-# )
+from turbo_alignment.dataset.registry import DatasetRegistry
+from turbo_alignment.settings.datasets.base import (
+    DatasetSourceSettings,
+    DatasetStrategy,
+    DatasetType,
+)
+from turbo_alignment.settings.datasets.chat import (
+    ChatDatasetSettings,
+    ChatPromptTemplate,
+)
+from turbo_alignment.settings.datasets.pair_preference import (
+    PairPreferenceDatasetSettings,
+)
 
 
-# @fixture(scope='session')
-# def tokenizer_llama2():
-#     tokenizer_path = 'tests/fixtures/models/llama2_classification/tokenizer'
-#     tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
-#     return tokenizer
+@fixture(scope='session')
+def tokenizer_llama2():
+    tokenizer_path = 'tests/fixtures/models/llama2_classification/tokenizer'
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+    return tokenizer
 
 
-# @fixture(scope='session')
-# def tokenizer_gptj():
-#     tokenizer_path = 'tests/fixtures/models/gptj_tiny_for_seq_cls'
-#     tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
-#     return tokenizer
+@fixture(scope='session')
+def tokenizer_gptj():
+    tokenizer_path = 'tests/fixtures/models/gptj_tiny_for_seq_cls'
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+    return tokenizer
 
 
-# @fixture(scope='session')
-# def chat_dataset_settings():
-#     chat_dataset_settings = ChatDatasetSettings(
-#         prompt_template=ChatPromptTemplate(
-#             prefix_template='<S>',
-#             suffix_template='</S>',
-#             role_tag_mapping={'user': 'USER', 'bot': 'BOT', 'system': 'SYSTEM'},
-#         ),
-#         max_tokens_count=8256,
-#     )
-#     return chat_dataset_settings
+@fixture(scope='session')
+def chat_dataset_settings():
+    chat_dataset_settings = ChatDatasetSettings(
+        prompt_template=ChatPromptTemplate(
+            prefix_template='<S>',
+            suffix_template='</S>',
+            role_tag_mapping={'user': 'USER', 'bot': 'BOT', 'system': 'SYSTEM'},
+        ),
+        max_tokens_count=8256,
+    )
+    return chat_dataset_settings
 
 
-# @fixture(scope='session')
-# def classification_dataset_path() -> str:
-#     return 'tests/fixtures/datasets/classification/train_classification.jsonl'
+@fixture(scope='session')
+def classification_dataset_path() -> str:
+    return 'tests/fixtures/datasets/classification/train_classification.jsonl'
 
 
-# @fixture(scope='session')
-# def pair_preferences_dataset_path() -> str:
-#     return 'tests/fixtures/datasets/rm/train_preferences.jsonl'
+@fixture(scope='session')
+def pair_preferences_dataset_path() -> str:
+    return 'tests/fixtures/datasets/rm/train_preferences.jsonl'
 
 
-# @fixture(scope='session')
-# def kto_dataset_path() -> str:
-#     return 'tests/fixtures/datasets/rm/train_kto.jsonl'
+@fixture(scope='session')
+def kto_dataset_path() -> str:
+    return 'tests/fixtures/datasets/rm/train_kto.jsonl'
 
 
-# def load_dataset_source(dataset_path: str) -> tuple[DatasetSourceSettings, list[dict]]:
-#     with open(dataset_path, 'r', encoding='utf-8') as f:
-#         data_dicts = [json.loads(line) for line in f]
+def load_dataset_source(dataset_path: str) -> tuple[DatasetSourceSettings, list[dict]]:
+    with open(dataset_path, 'r', encoding='utf-8') as f:
+        data_dicts = [json.loads(line) for line in f]
 
-#     source = DatasetSourceSettings(name='dataset_for_test', records_path=dataset_path, sample_rate=1)
+    source = DatasetSourceSettings(name='dataset_for_test', records_path=dataset_path, sample_rate=1)
 
-#     return source, data_dicts
+    return source, data_dicts
 
 
-# @fixture(scope='session')
-# def classification_dataset_source(classification_dataset_path) -> tuple[DatasetSourceSettings, list[dict]]:
-#     return load_dataset_source(classification_dataset_path)
+@fixture(scope='session')
+def classification_dataset_source(classification_dataset_path) -> tuple[DatasetSourceSettings, list[dict]]:
+    return load_dataset_source(classification_dataset_path)
 
 
-# @fixture(scope='session')
-# def pair_preferences_dataset_source(pair_preferences_dataset_path) -> tuple[DatasetSourceSettings, list[dict]]:
-#     return load_dataset_source(pair_preferences_dataset_path)
+@fixture(scope='session')
+def pair_preferences_dataset_source(pair_preferences_dataset_path) -> tuple[DatasetSourceSettings, list[dict]]:
+    return load_dataset_source(pair_preferences_dataset_path)
 
 
-# @fixture(scope='session')
-# def kto_dataset_source(kto_dataset_path) -> tuple[DatasetSourceSettings, list[dict]]:
-#     return load_dataset_source(kto_dataset_path)
+@fixture(scope='session')
+def kto_dataset_source(kto_dataset_path) -> tuple[DatasetSourceSettings, list[dict]]:
+    return load_dataset_source(kto_dataset_path)
 
 
-# @fixture(scope='session')
-# def dpo_dataset(pair_preferences_dataset_source, tokenizer_llama2, chat_dataset_settings):
-#     source, _ = pair_preferences_dataset_source
+@fixture(scope='session')
+def dpo_dataset(pair_preferences_dataset_source, tokenizer_llama2, chat_dataset_settings):
+    source, _ = pair_preferences_dataset_source
 
-#     dataset_cls = DatasetRegistry.by_name(DatasetType.PAIR_PREFERENCES).by_name(DatasetStrategy.TRAIN)
+    dataset_cls = DatasetRegistry.by_name(DatasetType.PAIR_PREFERENCES).by_name(DatasetStrategy.TRAIN)
 
-#     dataset_settings = PairPreferenceDatasetSettings(chat_settings=chat_dataset_settings)
-#     dataset = dataset_cls(tokenizer=tokenizer_llama2, source=source, settings=dataset_settings)
+    dataset_settings = PairPreferenceDatasetSettings(chat_settings=chat_dataset_settings)
+    dataset = dataset_cls(tokenizer=tokenizer_llama2, source=source, settings=dataset_settings)
 
-#     return dataset
+    return dataset
diff --git a/tests/fixtures/configs/train/classification/base.json b/tests/fixtures/configs/train/classification/base.json
index 960188b..eb396fe 100755
--- a/tests/fixtures/configs/train/classification/base.json
+++ b/tests/fixtures/configs/train/classification/base.json
@@ -79,11 +79,18 @@
             "problem_type": "single_label_classification"
         },
         "peft_settings": {
-            "r": 8,
-            "lora_alpha": 16,
-            "lora_dropout": 0.05,
-            "target_modules": ["q_proj", "v_proj"],
-            "task_type": "SEQ_CLS"
+            "name": "LORA",
+            "config": {
+                "r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "target_modules": [
+                    "q_proj",
+                    "v_proj"
+                ],
+                "task_type": "CAUSAL_LM",
+                "modules_to_save": ["embed_tokens", "lm_head"]
+            }
         }
     },
     "tokenizer_settings": {},
diff --git a/tests/fixtures/configs/train/multimodal/llama_c_abs_clip_pickle.json b/tests/fixtures/configs/train/multimodal/llama_c_abs_clip_pickle.json
index ffb2b13..6a03d75 100644
--- a/tests/fixtures/configs/train/multimodal/llama_c_abs_clip_pickle.json
+++ b/tests/fixtures/configs/train/multimodal/llama_c_abs_clip_pickle.json
@@ -76,19 +76,18 @@
       "model_type": "causal",
       "transformers_settings": {},
       "peft_settings": {
-        "r": 16,
-        "lora_alpha": 16,
-        "lora_dropout": 0.05,
-        "target_modules": [
-          "q_proj",
-          "k_proj"
-        ],
-        "task_type": "CAUSAL_LM",
-        "modules_to_save": [
-          "embed_tokens",
-          "lm_head"
-        ],
-        "name": "LORA"
+            "name": "LORA",
+            "config": {
+                "r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "target_modules": [
+                    "q_proj",
+                    "v_proj"
+                ],
+                "task_type": "CAUSAL_LM",
+                "modules_to_save": ["embed_tokens", "lm_head"]
+            }
       },
       "embeddings_initialization_strategy": {
         "<RS>": "bot",
diff --git a/tests/fixtures/configs/train/multimodal/llama_llava_base_clip.json b/tests/fixtures/configs/train/multimodal/llama_llava_base_clip.json
index ef90914..cd1b153 100644
--- a/tests/fixtures/configs/train/multimodal/llama_llava_base_clip.json
+++ b/tests/fixtures/configs/train/multimodal/llama_llava_base_clip.json
@@ -76,19 +76,18 @@
       "model_type": "causal",
       "transformers_settings": {},
       "peft_settings": {
-        "r": 16,
-        "lora_alpha": 16,
-        "lora_dropout": 0.05,
-        "target_modules": [
-          "q_proj",
-          "k_proj"
-        ],
-        "task_type": "CAUSAL_LM",
-        "modules_to_save": [
-          "embed_tokens",
-          "lm_head"
-        ],
-        "name": "LORA"
+          "name": "LORA",
+          "config": {
+              "r": 8,
+              "lora_alpha": 16,
+              "lora_dropout": 0.05,
+              "target_modules": [
+                  "q_proj",
+                  "v_proj"
+              ],
+              "task_type": "CAUSAL_LM",
+              "modules_to_save": ["embed_tokens", "lm_head"]
+          }
       },
       "embeddings_initialization_strategy": {
         "<RS>": "bot",
diff --git a/tests/fixtures/configs/train/multimodal/llama_llava_clip_pickle.json b/tests/fixtures/configs/train/multimodal/llama_llava_clip_pickle.json
index eae875a..1dc8f63 100644
--- a/tests/fixtures/configs/train/multimodal/llama_llava_clip_pickle.json
+++ b/tests/fixtures/configs/train/multimodal/llama_llava_clip_pickle.json
@@ -76,19 +76,18 @@
       "model_type": "causal",
       "transformers_settings": {},
       "peft_settings": {
-        "r": 16,
-        "lora_alpha": 16,
-        "lora_dropout": 0.05,
-        "target_modules": [
-          "q_proj",
-          "k_proj"
-        ],
-        "task_type": "CAUSAL_LM",
-        "modules_to_save": [
-          "embed_tokens",
-          "lm_head"
-        ],
-        "name": "LORA"
+        "name": "LORA",
+        "config": {
+            "r": 8,
+            "lora_alpha": 16,
+            "lora_dropout": 0.05,
+            "target_modules": [
+                "q_proj",
+                "v_proj"
+            ],
+            "task_type": "CAUSAL_LM",
+            "modules_to_save": ["embed_tokens", "lm_head"]
+        }
       },
       "embeddings_initialization_strategy": {
         "<RS>": "bot",
diff --git a/tests/fixtures/configs/train/rag/base.json b/tests/fixtures/configs/train/rag/base.json
index b64b826..d5f1e4c 100755
--- a/tests/fixtures/configs/train/rag/base.json
+++ b/tests/fixtures/configs/train/rag/base.json
@@ -54,16 +54,18 @@
         "<system>": "system"
       },
       "peft_settings": {
-        "r": 16,
-        "lora_alpha": 16,
-        "lora_dropout": 0.05,
-        "target_modules": ["q_proj", "v_proj", "k_proj", "o_proj"],
-        "task_type": "CAUSAL_LM",
-        "modules_to_save": [
-          "embed_tokens",
-          "lm_head"
-        ],
-        "name": "LORA"
+            "name": "LORA",
+            "config": {
+                "r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "target_modules": [
+                    "q_proj",
+                    "v_proj"
+                ],
+                "task_type": "CAUSAL_LM",
+                "modules_to_save": ["embed_tokens", "lm_head"]
+            }
       }
     },
     "question_encoder_settings": {
diff --git a/tests/fixtures/configs/train/rm/base.json b/tests/fixtures/configs/train/rm/base.json
index c2dd749..c9a3a6b 100755
--- a/tests/fixtures/configs/train/rm/base.json
+++ b/tests/fixtures/configs/train/rm/base.json
@@ -81,11 +81,18 @@
             "return_dict": true
         },
         "peft_settings": {
-            "r": 8,
-            "lora_alpha": 16,
-            "lora_dropout": 0.05,
-            "target_modules": ["q_proj", "v_proj"],
-            "task_type": "SEQ_CLS"
+            "name": "LORA",
+            "config": {
+                "r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "target_modules": [
+                    "q_proj",
+                    "v_proj"
+                ],
+                "task_type": "SEQ_CLS",
+                "modules_to_save": ["embed_tokens", "lm_head"]
+            }
         }
     },
     "tokenizer_settings": {},
diff --git a/tests/fixtures/configs/train/sft/base.json b/tests/fixtures/configs/train/sft/base.json
index 99c1ac0..c83f08b 100755
--- a/tests/fixtures/configs/train/sft/base.json
+++ b/tests/fixtures/configs/train/sft/base.json
@@ -47,16 +47,18 @@
         "transformers_settings": {
         },
         "peft_settings": {
-            "r": 8,
-            "lora_alpha": 16,
-            "lora_dropout": 0.05,
-            "target_modules": [
-                "q_proj",
-                "v_proj"
-            ],
-            "task_type": "CAUSAL_LM",
-            "modules_to_save": ["embed_tokens", "lm_head"],
-            "name": "LORA"
+            "name": "LORA",
+            "config": {
+                "r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "target_modules": [
+                    "q_proj",
+                    "v_proj"
+                ],
+                "task_type": "CAUSAL_LM",
+                "modules_to_save": ["embed_tokens", "lm_head"]
+            }
         },
         "embeddings_initialization_strategy": {
             "<RS>": "<s>",
diff --git a/tests/fixtures/configs/train/sft/prompt_tuning.json b/tests/fixtures/configs/train/sft/prompt_tuning.json
index 920d1f3..fc58e42 100755
--- a/tests/fixtures/configs/train/sft/prompt_tuning.json
+++ b/tests/fixtures/configs/train/sft/prompt_tuning.json
@@ -46,9 +46,11 @@
         "model_type": "causal",
     "transformers_settings": {},
     "peft_settings": {
-        "task_type": "CAUSAL_LM",
         "name": "PROMPT_TUNING",
-        "num_virtual_tokens": 32
+        "config": {
+            "task_type": "CAUSAL_LM",
+            "num_virtual_tokens": 32
+        }
     },
     "embeddings_initialization_strategy": {
       "<RS>": "<s>",
diff --git a/tests/fixtures/configs/train/sft/sft_with_rm_metric.json b/tests/fixtures/configs/train/sft/sft_with_rm_metric.json
index 8807122..ec6526c 100755
--- a/tests/fixtures/configs/train/sft/sft_with_rm_metric.json
+++ b/tests/fixtures/configs/train/sft/sft_with_rm_metric.json
@@ -47,16 +47,18 @@
         "transformers_settings": {
         },
         "peft_settings": {
-            "r": 8,
-            "lora_alpha": 16,
-            "lora_dropout": 0.05,
-            "target_modules": [
-                "q_proj",
-                "v_proj"
-            ],
-            "task_type": "CAUSAL_LM",
-            "modules_to_save": ["embed_tokens", "lm_head"],
-            "name": "LORA"
+            "name": "LORA",
+            "config": {
+                "r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "target_modules": [
+                    "q_proj",
+                    "v_proj"
+                ],
+                "task_type": "CAUSAL_LM",
+                "modules_to_save": ["embed_tokens", "lm_head"]
+            }
         },
         "embeddings_initialization_strategy": {
             "<RS>": "<s>",
diff --git a/tests/integration/test_trainers.py b/tests/integration/test_trainers.py
index 56941bb..97f8aa8 100644
--- a/tests/integration/test_trainers.py
+++ b/tests/integration/test_trainers.py
@@ -1,69 +1,69 @@
-# from tempfile import TemporaryDirectory
+from tempfile import TemporaryDirectory
 
-# import torch
-# from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-# from turbo_alignment.common.tf.callbacks.sync_ref_model import SyncRefModelSettings
-# from turbo_alignment.dataset.pair_preferences import PairPreferenceDataCollator
-# from turbo_alignment.settings.pipelines.train.dpo import (
-#     DPOLossesType,
-#     SigmoidLossSettings,
-# )
-# from turbo_alignment.trainers.dpo import DPOTrainer, DPOTrainingArguments
+from turbo_alignment.common.tf.callbacks.sync_ref_model import SyncRefModelSettings
+from turbo_alignment.dataset.pair_preferences import PairPreferenceDataCollator
+from turbo_alignment.settings.pipelines.train.utils import (
+    DPOLossesType,
+    SigmoidLossSettings,
+)
+from turbo_alignment.trainers.dpo import DPOTrainer, DPOTrainingArguments
 
 
-# def test_dpo_trainer(dpo_dataset):
-#     model_path = 'tests/fixtures/models/llama2_tiny'
+def test_dpo_trainer(dpo_dataset):
+    model_path = 'tests/fixtures/models/llama2_tiny'
 
-#     model = AutoModelForCausalLM.from_pretrained(model_path)
+    model = AutoModelForCausalLM.from_pretrained(model_path)
 
-#     ref_model = AutoModelForCausalLM.from_pretrained(model_path)
+    ref_model = AutoModelForCausalLM.from_pretrained(model_path)
 
-#     with TemporaryDirectory() as tmp_dir:
-#         args = DPOTrainingArguments(
-#             do_train=True,
-#             loss_settings=SigmoidLossSettings(loss_type=DPOLossesType.SIGMOID).dict(),
-#             sync_ref_settings=SyncRefModelSettings().dict(),
-#             do_eval=False,
-#             learning_rate=1.0e-4,
-#             use_cpu=True,
-#             num_train_epochs=10,
-#             report_to=[],
-#             remove_unused_columns=False,
-#             output_dir=tmp_dir,
-#         )
+    with TemporaryDirectory() as tmp_dir:
+        args = DPOTrainingArguments(
+            do_train=True,
+            loss_settings=SigmoidLossSettings(loss_type=DPOLossesType.SIGMOID).dict(),
+            sync_ref_settings=SyncRefModelSettings().dict(),
+            do_eval=False,
+            learning_rate=1.0e-4,
+            use_cpu=True,
+            num_train_epochs=10,
+            report_to=[],
+            remove_unused_columns=False,
+            output_dir=tmp_dir,
+        )
 
-#         tokenizer = AutoTokenizer.from_pretrained(model_path)
+        tokenizer = AutoTokenizer.from_pretrained(model_path)
 
-#         data_collator = PairPreferenceDataCollator(tokenizer=tokenizer)
+        data_collator = PairPreferenceDataCollator(tokenizer=tokenizer)
 
-#         trainer = DPOTrainer(
-#             model=model,
-#             ref_model=ref_model,
-#             args=args,
-#             train_dataset=dpo_dataset,
-#             eval_dataset=dpo_dataset,
-#             data_collator=data_collator,
-#         )
+        trainer = DPOTrainer(
+            model=model,
+            ref_model=ref_model,
+            args=args,
+            train_dataset=dpo_dataset,
+            eval_dataset=dpo_dataset,
+            data_collator=data_collator,
+        )
 
-#         batch = data_collator(list(dpo_dataset))
+        batch = data_collator(list(dpo_dataset))
 
-#         loss_before, _ = trainer.get_batch_metrics(model, batch, 'train')
-#         trainer.train()
-#         loss_after, _ = trainer.get_batch_metrics(trainer.model, batch, 'train')
+        loss_before, _ = trainer.get_batch_metrics(model, batch, 'train')
+        trainer.train()
+        loss_after, _ = trainer.get_batch_metrics(trainer.model, batch, 'train')
 
-#         assert torch.greater(loss_before, loss_after)
+        assert torch.greater(loss_before, loss_after)
 
-#         initial_model = AutoModelForCausalLM.from_pretrained(model_path)
+        initial_model = AutoModelForCausalLM.from_pretrained(model_path)
 
-#         trainer.save_model(tmp_dir)
-#         trained_model = AutoModelForCausalLM.from_pretrained(tmp_dir)
+        trainer.save_model(tmp_dir)
+        trained_model = AutoModelForCausalLM.from_pretrained(tmp_dir)
 
-#         initial_state_dict = initial_model.state_dict()
-#         trained_state_dict = trained_model.state_dict()
-#         for k, v in trained_state_dict.items():
-#             assert any(torch.not_equal(v, initial_state_dict[k]).tolist())
+        initial_state_dict = initial_model.state_dict()
+        trained_state_dict = trained_model.state_dict()
+        for k, v in trained_state_dict.items():
+            assert any(torch.not_equal(v, initial_state_dict[k]).tolist())
 
-#         ref_model_state_dict = trainer.ref_model.state_dict()
-#         for k, v in ref_model_state_dict.items():
-#             assert all(torch.eq(v, initial_state_dict[k]).tolist())
+        ref_model_state_dict = trainer.ref_model.state_dict()
+        for k, v in ref_model_state_dict.items():
+            assert all(torch.eq(v, initial_state_dict[k]).tolist())
diff --git a/tests/unit/test_collators.py b/tests/unit/test_collators.py
index 5231b40..150436a 100644
--- a/tests/unit/test_collators.py
+++ b/tests/unit/test_collators.py
@@ -1,28 +1,28 @@
-# from tests.utils import is_sample_build_from_content
-# from turbo_alignment.dataset.kto.collators import KTODataCollator
-# from turbo_alignment.dataset.registry import DatasetRegistry
-# from turbo_alignment.settings.datasets.base import DatasetStrategy, DatasetType
-# from turbo_alignment.settings.datasets.kto import KTODatasetSettings
+from tests.utils import is_sample_build_from_content
+from turbo_alignment.dataset.kto.collators import KTODataCollator
+from turbo_alignment.dataset.registry import DatasetRegistry
+from turbo_alignment.settings.datasets.base import DatasetStrategy, DatasetType
+from turbo_alignment.settings.datasets.kto import KTODatasetSettings
 
 
-# def test_kto_collator(tokenizer_llama2, chat_dataset_settings, kto_dataset_source):
-#     tokenizer = tokenizer_llama2
-#     source, data_dicts = kto_dataset_source
+def test_kto_collator(tokenizer_llama2, chat_dataset_settings, kto_dataset_source):
+    tokenizer = tokenizer_llama2
+    source, data_dicts = kto_dataset_source
 
-#     dataset_cls = DatasetRegistry.by_name(DatasetType.KTO).by_name(DatasetStrategy.TRAIN)
+    dataset_cls = DatasetRegistry.by_name(DatasetType.KTO).by_name(DatasetStrategy.TRAIN)
 
-#     dataset_settings = KTODatasetSettings(chat_settings=chat_dataset_settings)
-#     dataset = dataset_cls(tokenizer=tokenizer, source=source, settings=dataset_settings)
+    dataset_settings = KTODatasetSettings(chat_settings=chat_dataset_settings)
+    dataset = dataset_cls(tokenizer=tokenizer, source=source, settings=dataset_settings)
 
-#     batch_size = min(len(dataset), 8)
-#     examples = list(dataset)[:batch_size]
+    batch_size = min(len(dataset), 8)
+    examples = list(dataset)[:batch_size]
 
-#     collator = KTODataCollator(tokenizer=tokenizer)
-#     batch = collator(examples)
+    collator = KTODataCollator(tokenizer=tokenizer)
+    batch = collator(examples)
 
-#     ignore_index = -100
-#     for answer_labels, is_desirable, raw_data in zip(batch['labels'], batch['is_desirable'], data_dicts):
-#         answer = raw_data['answer']['content']
-#         answer_tokens = answer_labels[answer_labels != ignore_index]
-#         assert is_sample_build_from_content(answer_tokens, [answer], tokenizer)
-#         assert raw_data['is_desirable'] == is_desirable
+    ignore_index = -100
+    for answer_labels, is_desirable, raw_data in zip(batch['labels'], batch['is_desirable'], data_dicts):
+        answer = raw_data['answer']['content']
+        answer_tokens = answer_labels[answer_labels != ignore_index]
+        assert is_sample_build_from_content(answer_tokens, [answer], tokenizer)
+        assert raw_data['is_desirable'] == is_desirable
diff --git a/tests/unit/test_datasets.py b/tests/unit/test_datasets.py
index 089417b..1ecd323 100644
--- a/tests/unit/test_datasets.py
+++ b/tests/unit/test_datasets.py
@@ -1,88 +1,88 @@
-# from tests.utils import is_sample_build_from_content
-# from turbo_alignment.dataset.classification.models import ClassificationDatasetRecord
-# from turbo_alignment.dataset.pair_preferences.models import PairPreferenceRecord
-# from turbo_alignment.dataset.registry import DatasetRegistry
-# from turbo_alignment.settings.datasets.base import DatasetStrategy, DatasetType
-# from turbo_alignment.settings.datasets.classification import (
-#     ClassificationDatasetSettings,
-# )
-# from turbo_alignment.settings.datasets.ddpo import DDPODatasetSettings
-# from turbo_alignment.settings.datasets.pair_preference import (
-#     PairPreferenceDatasetSettings,
-# )
+from tests.utils import is_sample_build_from_content
+from turbo_alignment.dataset.classification.models import ClassificationDatasetRecord
+from turbo_alignment.dataset.pair_preferences.models import PairPreferenceRecord
+from turbo_alignment.dataset.registry import DatasetRegistry
+from turbo_alignment.settings.datasets.base import DatasetStrategy, DatasetType
+from turbo_alignment.settings.datasets.classification import (
+    ClassificationDatasetSettings,
+)
+from turbo_alignment.settings.datasets.ddpo import DDPODatasetSettings
+from turbo_alignment.settings.datasets.pair_preference import (
+    PairPreferenceDatasetSettings,
+)
 
 
-# def test_classification(tokenizer_llama2, chat_dataset_settings, classification_dataset_source):
-#     # load dataset and check that samples have required fields
+def test_classification(tokenizer_llama2, chat_dataset_settings, classification_dataset_source):
+    # load dataset and check that samples have required fields
 
-#     source, data_dicts = classification_dataset_source
+    source, data_dicts = classification_dataset_source
 
-#     dataset_cls = DatasetRegistry.by_name(DatasetType.CLASSIFICATION).by_name(DatasetStrategy.TRAIN)
+    dataset_cls = DatasetRegistry.by_name(DatasetType.CLASSIFICATION).by_name(DatasetStrategy.TRAIN)
 
-#     dataset_settings = ClassificationDatasetSettings(chat_settings=chat_dataset_settings)
+    dataset_settings = ClassificationDatasetSettings(chat_settings=chat_dataset_settings)
 
-#     dataset = dataset_cls(tokenizer=tokenizer_llama2, source=source, settings=dataset_settings)
+    dataset = dataset_cls(tokenizer=tokenizer_llama2, source=source, settings=dataset_settings)
 
-#     assert len(data_dicts) == len(dataset)
+    assert len(data_dicts) == len(dataset)
 
-#     for data_dict, sample in zip(data_dicts, dataset):
-#         record = ClassificationDatasetRecord.model_validate(data_dict)
+    for data_dict, sample in zip(data_dicts, dataset):
+        record = ClassificationDatasetRecord.model_validate(data_dict)
 
-#         assert record.label == sample['labels']
+        assert record.label == sample['labels']
 
-#         assert is_sample_build_from_content(
-#             sample['input_ids'], [m.content for m in record.messages], tokenizer_llama2
-#         )
+        assert is_sample_build_from_content(
+            sample['input_ids'], [m.content for m in record.messages], tokenizer_llama2
+        )
 
 
-# def test_pair_preferences(tokenizer_llama2, chat_dataset_settings, pair_preferences_dataset_source):
-#     # load dataset and check that samples have required fields
+def test_pair_preferences(tokenizer_llama2, chat_dataset_settings, pair_preferences_dataset_source):
+    # load dataset and check that samples have required fields
 
-#     source, data_dicts = pair_preferences_dataset_source
+    source, data_dicts = pair_preferences_dataset_source
 
-#     dataset_cls = DatasetRegistry.by_name(DatasetType.PAIR_PREFERENCES).by_name(DatasetStrategy.TRAIN)
+    dataset_cls = DatasetRegistry.by_name(DatasetType.PAIR_PREFERENCES).by_name(DatasetStrategy.TRAIN)
 
-#     dataset_settings = PairPreferenceDatasetSettings(chat_settings=chat_dataset_settings)
-#     dataset = dataset_cls(tokenizer=tokenizer_llama2, source=source, settings=dataset_settings)
+    dataset_settings = PairPreferenceDatasetSettings(chat_settings=chat_dataset_settings)
+    dataset = dataset_cls(tokenizer=tokenizer_llama2, source=source, settings=dataset_settings)
 
-#     assert len(data_dicts) == len(dataset)
+    assert len(data_dicts) == len(dataset)
 
-#     for data_dict, sample in zip(data_dicts, dataset):
-#         record = PairPreferenceRecord.model_validate(data_dict)
-#         context: list[str] = [c.content for c in record.context]
-#         contents_w = [*context, record.answer_w.content]
-#         assert is_sample_build_from_content(sample['inputs_w']['input_ids'], contents_w, tokenizer_llama2)
+    for data_dict, sample in zip(data_dicts, dataset):
+        record = PairPreferenceRecord.model_validate(data_dict)
+        context: list[str] = [c.content for c in record.context]
+        contents_w = [*context, record.answer_w.content]
+        assert is_sample_build_from_content(sample['inputs_w']['input_ids'], contents_w, tokenizer_llama2)
 
-#         contents_l = [*context, record.answer_l.content]
-#         assert is_sample_build_from_content(sample['inputs_l']['input_ids'], contents_l, tokenizer_llama2)
+        contents_l = [*context, record.answer_l.content]
+        assert is_sample_build_from_content(sample['inputs_l']['input_ids'], contents_l, tokenizer_llama2)
 
 
-# def test_ddpo(tokenizer_llama2, tokenizer_gptj, chat_dataset_settings, pair_preferences_dataset_source):
-#     sft_tokenizer = tokenizer_llama2
-#     rm_tokenizer = tokenizer_gptj
-#     # load dataset and check that samples have required fields
+def test_ddpo(tokenizer_llama2, tokenizer_gptj, chat_dataset_settings, pair_preferences_dataset_source):
+    sft_tokenizer = tokenizer_llama2
+    rm_tokenizer = tokenizer_gptj
+    # load dataset and check that samples have required fields
 
-#     source, data_dicts = pair_preferences_dataset_source
+    source, data_dicts = pair_preferences_dataset_source
 
-#     dataset_cls = DatasetRegistry.by_name(DatasetType.DDPO).by_name(DatasetStrategy.TRAIN)
+    dataset_cls = DatasetRegistry.by_name(DatasetType.DDPO).by_name(DatasetStrategy.TRAIN)
 
-#     pair_preferences_dataset_settings = PairPreferenceDatasetSettings(chat_settings=chat_dataset_settings)
-#     dataset_settings = DDPODatasetSettings(
-#         chat_settings=chat_dataset_settings, pair_preferences=pair_preferences_dataset_settings
-#     )
-#     dataset = dataset_cls(
-#         chat_tokenizer=sft_tokenizer, rm_tokenizer=rm_tokenizer, source=source, settings=dataset_settings
-#     )
+    pair_preferences_dataset_settings = PairPreferenceDatasetSettings(chat_settings=chat_dataset_settings)
+    dataset_settings = DDPODatasetSettings(
+        chat_settings=chat_dataset_settings, pair_preferences=pair_preferences_dataset_settings
+    )
+    dataset = dataset_cls(
+        chat_tokenizer=sft_tokenizer, rm_tokenizer=rm_tokenizer, source=source, settings=dataset_settings
+    )
 
-#     assert len(data_dicts) == len(dataset)
+    assert len(data_dicts) == len(dataset)
 
-#     for data_dict, sample in zip(data_dicts, dataset):
-#         record = PairPreferenceRecord.model_validate(data_dict)
-#         context: list[str] = [c.content for c in record.context]
-#         contents_w = [*context, record.answer_w.content]
-#         assert is_sample_build_from_content(sample['sft_inputs_w']['input_ids'], contents_w, sft_tokenizer)
-#         assert is_sample_build_from_content(sample['rm_inputs_w']['input_ids'], contents_w, rm_tokenizer)
+    for data_dict, sample in zip(data_dicts, dataset):
+        record = PairPreferenceRecord.model_validate(data_dict)
+        context: list[str] = [c.content for c in record.context]
+        contents_w = [*context, record.answer_w.content]
+        assert is_sample_build_from_content(sample['sft_inputs_w']['input_ids'], contents_w, sft_tokenizer)
+        assert is_sample_build_from_content(sample['rm_inputs_w']['input_ids'], contents_w, rm_tokenizer)
 
-#         contents_l = [*context, record.answer_l.content]
-#         assert is_sample_build_from_content(sample['sft_inputs_l']['input_ids'], contents_l, sft_tokenizer)
-#         assert is_sample_build_from_content(sample['rm_inputs_l']['input_ids'], contents_l, rm_tokenizer)
+        contents_l = [*context, record.answer_l.content]
+        assert is_sample_build_from_content(sample['sft_inputs_l']['input_ids'], contents_l, sft_tokenizer)
+        assert is_sample_build_from_content(sample['rm_inputs_l']['input_ids'], contents_l, rm_tokenizer)
diff --git a/turbo_alignment/common/tf/loaders/model/model.py b/turbo_alignment/common/tf/loaders/model/model.py
index aba80e9..49a5f11 100755
--- a/turbo_alignment/common/tf/loaders/model/model.py
+++ b/turbo_alignment/common/tf/loaders/model/model.py
@@ -4,7 +4,6 @@
 from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
 
 from turbo_alignment.common.tf.loaders.model.registry import (
-    PeftConfigRegistry,
     TransformersAutoModelRegistry,
 )
 from turbo_alignment.modeling.liger_kernels import apply_liger_kernel_to_gemma2
@@ -18,12 +17,7 @@
 
 
 def _prepare_model_for_peft(model: PreTrainedModel, peft_settings: PEFT_TYPE) -> PeftModel:
-    peft_params = peft_settings.dict()
-    peft_params.pop('name')
-
-    peft_config = PeftConfigRegistry.by_name(peft_settings.name)(**peft_params)
-
-    return get_peft_model(model, peft_config)
+    return get_peft_model(model, peft_settings.config)
 
 
 def _load_pretrained_adapters(
diff --git a/turbo_alignment/common/tf/loaders/model/registry.py b/turbo_alignment/common/tf/loaders/model/registry.py
index e2c5e08..565a61d 100755
--- a/turbo_alignment/common/tf/loaders/model/registry.py
+++ b/turbo_alignment/common/tf/loaders/model/registry.py
@@ -1,10 +1,3 @@
-from peft import (
-    LoraConfig,
-    PeftType,
-    PrefixTuningConfig,
-    PromptEncoderConfig,
-    PromptTuningConfig,
-)
 from transformers import (
     AutoModel,
     AutoModelForCausalLM,
@@ -19,15 +12,6 @@ class TransformersAutoModelRegistry(Registrable):
     ...
 
 
-class PeftConfigRegistry(Registrable):
-    ...
-
-
 TransformersAutoModelRegistry.register(ModelType.CAUSAL)(AutoModelForCausalLM)
 TransformersAutoModelRegistry.register(ModelType.SEQ_CLS)(AutoModelForSequenceClassification)
 TransformersAutoModelRegistry.register(ModelType.ENC)(AutoModel)
-
-PeftConfigRegistry.register(PeftType.LORA)(LoraConfig)
-PeftConfigRegistry.register(PeftType.PREFIX_TUNING)(PrefixTuningConfig)
-PeftConfigRegistry.register(PeftType.PROMPT_TUNING)(PromptTuningConfig)
-PeftConfigRegistry.register(PeftType.P_TUNING)(PromptEncoderConfig)
diff --git a/turbo_alignment/settings/pipelines/train/dpo.py b/turbo_alignment/settings/pipelines/train/dpo.py
index 0270063..795b84b 100755
--- a/turbo_alignment/settings/pipelines/train/dpo.py
+++ b/turbo_alignment/settings/pipelines/train/dpo.py
@@ -17,8 +17,8 @@ class DPOTrainExperimentSettings(BaseTrainExperimentSettings):
 
     cherry_pick_settings: ChatCherryPickSettings
 
-    # training_arguments: DPOTrainingArguments
+    training_arguments: DPOTrainingArguments
 
-    # @field_validator('training_arguments', mode='before')
-    # def convert_generation_settings(cls, values: dict[str, Any]) -> DPOTrainingArguments:
-    # return DPOTrainingArguments(**values, output_dir=TRAINER_LOGS_FOLDER, report_to=[])
+    @field_validator('training_arguments', mode='before')
+    def create_training_arguments(cls, values: dict[str, Any]) -> DPOTrainingArguments:
+        return DPOTrainingArguments(**values, output_dir=TRAINER_LOGS_FOLDER, report_to=[])
diff --git a/turbo_alignment/settings/pipelines/train/kto.py b/turbo_alignment/settings/pipelines/train/kto.py
index 0d459ef..bdf09f3 100755
--- a/turbo_alignment/settings/pipelines/train/kto.py
+++ b/turbo_alignment/settings/pipelines/train/kto.py
@@ -1,3 +1,8 @@
+from typing import Any
+
+from pydantic import field_validator
+
+from turbo_alignment.constants import TRAINER_LOGS_FOLDER
 from turbo_alignment.settings.cherry_pick import ChatCherryPickSettings
 from turbo_alignment.settings.datasets.kto import KTOMultiDatasetSettings
 from turbo_alignment.settings.pipelines.train.base import BaseTrainExperimentSettings
@@ -10,4 +15,8 @@ class KTOTrainExperimentSettings(BaseTrainExperimentSettings):
 
     cherry_pick_settings: ChatCherryPickSettings
 
-    # training_arguments: KTOTrainingArguments
+    training_arguments: KTOTrainingArguments
+
+    @field_validator('training_arguments', mode='before')
+    def create_training_arguments(cls, values: dict[str, Any]) -> KTOTrainingArguments:
+        return KTOTrainingArguments(**values, output_dir=TRAINER_LOGS_FOLDER, report_to=[])
diff --git a/turbo_alignment/settings/s3.py b/turbo_alignment/settings/s3.py
index 2e964fc..22c63ca 100755
--- a/turbo_alignment/settings/s3.py
+++ b/turbo_alignment/settings/s3.py
@@ -1,6 +1,5 @@
 from pathlib import Path
 
-from pydantic import field_validator
 from pydantic_settings import BaseSettings
 
 
@@ -14,13 +13,6 @@ class S3HandlerParameters(BaseSettings):
     aws_access_key_id: str
     aws_secret_access_key: str
 
-    @field_validator('bucket')
-    def bucket_name_biglm_is_not_allowed(cls, values: str) -> str:
-        if values == 'biglm':
-            raise S3HandlerParametersWrongBucketException('Usage of biglm bucket is not allowed')
-
-        return values
-
     class Config:
         env_file: str = '.env'
         env_prefix: str = 'S3_CHECKPOINTS_'
diff --git a/turbo_alignment/settings/tf/peft.py b/turbo_alignment/settings/tf/peft.py
index f479a27..d8aafd1 100755
--- a/turbo_alignment/settings/tf/peft.py
+++ b/turbo_alignment/settings/tf/peft.py
@@ -1,41 +1,40 @@
 from typing import Literal
 
-from peft import PeftType, TaskType
+from peft import (
+    LoraConfig,
+    PeftConfig,
+    PeftType,
+    PrefixTuningConfig,
+    PromptEncoderConfig,
+    PromptTuningConfig,
+)
 
 from turbo_alignment.settings.base import ExtraFieldsNotAllowedBaseModel
 
 
 class BasePeftSettings(ExtraFieldsNotAllowedBaseModel):
     name: PeftType
-    task_type: TaskType = TaskType.CAUSAL_LM
+    config: PeftConfig
 
 
 class LoraSettings(BasePeftSettings):
     name: Literal[PeftType.LORA] = PeftType.LORA
-    r: int = 16
-    lora_alpha: int = 16
-    lora_dropout: float = 0.05
-    bias: str = 'none'
-    target_modules: list[str] = ['q_proj', 'v_proj']
-    modules_to_save: list[str] | None = None
+    config: LoraConfig
 
 
 class PrefixTuningSettings(BasePeftSettings):
     name: Literal[PeftType.PREFIX_TUNING] = PeftType.PREFIX_TUNING
-    encoder_hidden_size: int
-    prefix_projection: bool
+    config: PrefixTuningConfig
 
 
 class PromptTuningSettings(BasePeftSettings):
     name: Literal[PeftType.PROMPT_TUNING] = PeftType.PROMPT_TUNING
-    num_virtual_tokens: int = 32
-    prompt_tuning_init_text: str | None = None
+    config: PromptTuningConfig
 
 
 class PTuningSettings(BasePeftSettings):
     name: Literal[PeftType.P_TUNING] = PeftType.P_TUNING
-    num_virtual_tokens: int = 32
-    encoder_reparameterization_type: str = 'MLP'
+    config: PromptEncoderConfig
 
 
 PEFT_TYPE = PrefixTuningSettings | LoraSettings | PromptTuningSettings | PTuningSettings
diff --git a/turbo_alignment/trainers/classification.py b/turbo_alignment/trainers/classification.py
index 76a26e4..bfc1500 100755
--- a/turbo_alignment/trainers/classification.py
+++ b/turbo_alignment/trainers/classification.py
@@ -60,7 +60,7 @@ def auto_class_weights(dataset: Dataset) -> list[float]:
 class ClassificationTrainer(MultiGPUCherryPicksTrainer):
     def __init__(self, **kwargs) -> None:
         args = kwargs.get('args')
-        self.loss_settings = args.loss_settings
+        self.loss_settings = args.loss_settings  # type: ignore[union-attr]
         super().__init__(
             compute_metrics=compute_clf_metrics,
             **kwargs,
diff --git a/turbo_alignment/trainers/dpo.py b/turbo_alignment/trainers/dpo.py
index a69da2e..744b17b 100755
--- a/turbo_alignment/trainers/dpo.py
+++ b/turbo_alignment/trainers/dpo.py
@@ -1,5 +1,5 @@
 from collections import defaultdict
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from typing import Any, Callable, Literal
 
 import torch
@@ -414,12 +414,8 @@ class DPOTrainingArguments(TrainingArguments):
         | SigmoidLossWithMarginSettings
         | APOZeroLossSettings
         | APODownLossSettings
-    ) = field(
-        default_factory=SigmoidLossSettings(loss_type=DPOLossesType.SIGMOID)
-    )  # type: ignore[call-overload]
-    sync_ref_settings: SyncRefModelSettings = field(  # type: ignore[call-overload]
-        default_factory=SyncRefModelSettings()
-    )
+    ) = SigmoidLossSettings(loss_type=DPOLossesType.SIGMOID)
+    sync_ref_settings: SyncRefModelSettings = SyncRefModelSettings()
     use_ref_model: bool = True
     use_sft_model: bool = False
     average_log_prob: bool = False
diff --git a/turbo_alignment/trainers/kto.py b/turbo_alignment/trainers/kto.py
index 4b35bca..6a11774 100755
--- a/turbo_alignment/trainers/kto.py
+++ b/turbo_alignment/trainers/kto.py
@@ -1,5 +1,5 @@
 from collections import defaultdict
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from typing import Any, Callable, Literal
 
 import torch
@@ -30,9 +30,7 @@
 @dataclass
 class KTOTrainingArguments(TrainingArguments):
     beta: float = 0.1
-    sync_ref_settings: SyncRefModelSettings = field(
-        default_factory=SyncRefModelSettings()
-    )  # type: ignore[call-overload]
+    sync_ref_settings: SyncRefModelSettings = SyncRefModelSettings()
     use_ref_model: bool = True
     average_log_prob: bool = False
     undesirable_weight: float = 1.0
diff --git a/tutorials/multimodal/multimodal.json b/tutorials/multimodal/multimodal.json
index 54b7948..7746c79 100644
--- a/tutorials/multimodal/multimodal.json
+++ b/tutorials/multimodal/multimodal.json
@@ -76,19 +76,18 @@
       "model_type": "causal",
       "transformers_settings": {},
       "peft_settings": {
-        "r": 16,
-        "lora_alpha": 16,
-        "lora_dropout": 0.05,
-        "target_modules": [
-          "q_proj",
-          "k_proj"
-        ],
-        "task_type": "CAUSAL_LM",
-        "modules_to_save": [
-          "embed_tokens",
-          "lm_head"
-        ],
-        "name": "LORA"
+        "name": "LORA",
+        "config": {
+            "r": 8,
+            "lora_alpha": 16,
+            "lora_dropout": 0.05,
+            "target_modules": [
+                "q_proj",
+                "v_proj"
+            ],
+            "task_type": "CAUSAL_LM",
+            "modules_to_save": ["embed_tokens", "lm_head"]
+        }
       },
       "embeddings_initialization_strategy": {
         "<RS>": "bot",