diff --git a/pyproject.toml b/pyproject.toml
index 95596fa..febe392 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,7 +3,7 @@ name = "turbo-alignment"
 packages = [
     { include = "turbo_alignment" },
 ]
-version = "0.0.2"
+version = "0.0.3"
 description = "turbo-alignment repository"
 authors = ["T Mega Alignment Team <n.surnachev@tcsbank.ru>" ]
 readme = "README.md"
diff --git a/turbo_alignment/cherry_picks/multimodal.py b/turbo_alignment/cherry_picks/multimodal.py
index ba1c536..80c6022 100755
--- a/turbo_alignment/cherry_picks/multimodal.py
+++ b/turbo_alignment/cherry_picks/multimodal.py
@@ -1,10 +1,10 @@
 from typing import Iterable
 
 import torch
-import wandb
 from PIL import Image
 from transformers import PreTrainedTokenizerBase
 
+import wandb
 from turbo_alignment.cherry_picks.base import CherryPickCallbackBase
 from turbo_alignment.dataset.multimodal import InferenceMultimodalDataset
 from turbo_alignment.generators.multimodal import MultimodalGenerator
diff --git a/turbo_alignment/common/logging/weights_and_biases.py b/turbo_alignment/common/logging/weights_and_biases.py
index 3b5bdfd..112e585 100755
--- a/turbo_alignment/common/logging/weights_and_biases.py
+++ b/turbo_alignment/common/logging/weights_and_biases.py
@@ -1,9 +1,9 @@
 from typing import Any
 
-import wandb
 from wandb.sdk.lib.disabled import RunDisabled
 from wandb.sdk.wandb_run import Run
 
+import wandb
 from turbo_alignment.settings.logging.weights_and_biases import WandbSettings
 
 
diff --git a/turbo_alignment/common/tf/callbacks/logging.py b/turbo_alignment/common/tf/callbacks/logging.py
index 646e280..1ad484d 100755
--- a/turbo_alignment/common/tf/callbacks/logging.py
+++ b/turbo_alignment/common/tf/callbacks/logging.py
@@ -3,7 +3,6 @@
 
 import numpy as np
 import pandas as pd
-import wandb
 from clearml import Task
 from transformers import (
     TrainerCallback,
@@ -14,6 +13,7 @@
 from wandb.sdk.lib.disabled import RunDisabled
 from wandb.sdk.wandb_run import Run
 
+import wandb
 from turbo_alignment.common.logging import get_project_logger
 
 logger = get_project_logger()
diff --git a/turbo_alignment/generators/vllm_chat.py b/turbo_alignment/generators/vllm_chat.py
index ff52de3..5a89bb1 100755
--- a/turbo_alignment/generators/vllm_chat.py
+++ b/turbo_alignment/generators/vllm_chat.py
@@ -3,6 +3,7 @@
 import torch
 from transformers import PreTrainedTokenizerBase
 from vllm import LLM, SamplingParams
+from vllm.lora.request import LoRARequest
 
 from turbo_alignment.dataset.chat import ChatDatasetRecord
 from turbo_alignment.generators.base import BaseGenerator
@@ -23,6 +24,7 @@ def __init__(
         tokenizer: PreTrainedTokenizerBase,
         batch: int,
         return_logits: bool = False,
+        lora_request: LoRARequest | None = None,
     ):
         model.set_tokenizer(tokenizer)
         super().__init__(model, tokenizer, batch=batch)
@@ -51,6 +53,7 @@ def __init__(
             max_tokens=transformers_settings.max_new_tokens,
             **beam_search_params,
         )
+        self._lora_request = lora_request
 
         self._return_logits = return_logits
 
@@ -62,6 +65,7 @@ def _generate_from_batch(
             prompts=None,
             prompt_token_ids=input_ids,
             sampling_params=self._sampling_params,
+            lora_request=self._lora_request,
         )
 
         outputs = []
diff --git a/turbo_alignment/pipelines/inference/chat.py b/turbo_alignment/pipelines/inference/chat.py
index a40df0f..93b975c 100755
--- a/turbo_alignment/pipelines/inference/chat.py
+++ b/turbo_alignment/pipelines/inference/chat.py
@@ -8,6 +8,7 @@
 from turbo_alignment.generators.base import BaseGenerator
 from turbo_alignment.generators.chat import ChatGenerator
 from turbo_alignment.pipelines.inference.base import BaseInferenceStrategy
+from turbo_alignment.settings.model import PreTrainedAdaptersModelSettings
 from turbo_alignment.settings.pipelines.inference.chat import (
     ChatInferenceExperimentSettings,
 )
@@ -27,14 +28,24 @@ def _get_single_inference_settings(
 
             if model_inference_settings.use_vllm:
                 import vllm
+                from vllm.lora.request import LoRARequest
 
                 from turbo_alignment.generators.vllm_chat import VLLMChatGenerator
 
+                lora_request: LoRARequest | None = None
+                enable_lora: bool = False
+
+                if isinstance(model_inference_settings.model_settings, PreTrainedAdaptersModelSettings):
+                    lora_request = LoRARequest('adapter', 1, str(model_inference_settings.model_settings.adapter_path))
+                    enable_lora = True
+
                 model = vllm.LLM(
                     model=model_inference_settings.model_settings.model_path.absolute().as_posix(),
                     dtype='bfloat16',
                     tensor_parallel_size=model_inference_settings.tensor_parallel_size,
+                    enable_lora=enable_lora,
                 )
+
             else:
                 model = load_model(model_inference_settings.model_settings, tokenizer)
                 model = (
@@ -57,7 +68,10 @@ def _get_single_inference_settings(
                         accelerator=accelerator,
                     )
                     if not model_inference_settings.use_vllm
-                    else VLLMChatGenerator(**generator_kwargs)
+                    else VLLMChatGenerator(
+                        **generator_kwargs,
+                        lora_request=lora_request,
+                    )
                 )
 
                 parameters_to_save = {