diff --git a/.python-version b/.python-version
index e4fba21..24ee5b1 100644
--- a/.python-version
+++ b/.python-version
@@ -1 +1 @@
-3.12
+3.13
diff --git a/pyproject.toml b/pyproject.toml
index b31b7ed..cb842a0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,11 +3,12 @@ name = "complexity-aware-fine-tuning"
 version = "0.1.0"
 description = "Add your description here"
 readme = "README.md"
-requires-python = ">=3.12"
+requires-python = ">=3.13"
 dependencies = [
     "accelerate==1.3.0",
     "datasets>=3.6.0",
     "ipywidgets>=8.1.7",
+    "loguru>=0.7.3",
     "matplotlib>=3.10.1",
     "mistral-common>=1.9.1",
     "mistralai>=1.6.0",
@@ -20,7 +21,7 @@ dependencies = [
     "pydraconf>=0.1.0",
     "scikit-learn>=1.6.1",
     "seaborn>=0.13.2",
-    "sentencepiece>=0.2.0",
+    "sentencepiece>=0.2.1",
     "torch>=2.6.0",
     "transformers==4.52.3",
 ]
diff --git a/src/core/training/base_trainer.py b/src/core/training/base_trainer.py
new file mode 100644
index 0000000..1a32f29
--- /dev/null
+++ b/src/core/training/base_trainer.py
@@ -0,0 +1,169 @@
+import json
+import subprocess
+from pathlib import Path
+from typing import Any
+
+from pydantic import BaseModel
+from pydraconf import PydraConfig
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    DataCollatorForTokenClassification,
+    PreTrainedTokenizer,
+    Seq2SeqTrainingArguments,
+)
+from transformers.trainer_seq2seq import Seq2SeqTrainer
+
+from core.datasets.abstract_dataset_adapter import AbstractDatasetAdapter
+from core.training.callbacks.save_by_schedule import SaveByScheduleCallback
+from core.utils.last_checkpoint_dir import get_last_checkpoint_dir
+from core.utils.logger import logger
+from core.utils.seed import set_seed
+
+
+class BaseTrainingArgs(BaseModel):
+    num_train_epochs: int
+    effective_train_batch_size: int = 256
+    per_device_train_batch_size: int
+
+    # Sane defaults for SFT fine-tuning
+    learning_rate: float = 2e-5
+    lr_scheduler_type: str = "cosine"
+    warmup_ratio: float = 0.03
+    weight_decay: float = 0.1
+    max_grad_norm: float = 1.0
+    optim: str = "adamw_torch"
+    gradient_checkpointing: bool = True
+    bf16: bool = True
+    report_to: str = "none"
+    seed: int = 42
+    data_seed: int = 42
+    torch_compile: bool = True
+    save_strategy: str = "epoch"
+    logging_steps: int = 10
+    logging_first_step: bool = True
+
+
+class BaseTrainerConfig[TTrainingArgs: BaseTrainingArgs = BaseTrainingArgs](PydraConfig):
+    out_path: str
+    model_id: str
+    train_dataset: AbstractDatasetAdapter
+    training_args: TTrainingArgs
+    save_schedule: list[int] | None = None
+
+
+class BaseTrainer[TConfig: BaseTrainerConfig[Any] = BaseTrainerConfig]:
+    def __init__(self, config: TConfig, tokenizer: PreTrainedTokenizer | None = None):
+        self.config = config
+        self._tokenizer: PreTrainedTokenizer | None = tokenizer
+
+    def train(self):
+        if not self._directory_is_empty(self.config.out_path, self.config.training_args.num_train_epochs):
+            logger.error("BaseTrainerConfig.train -> out_path not empty", self.config.out_path)
+            return None
+
+        set_seed()
+
+        logger.info(subprocess.run(["nvidia-smi"], capture_output=True, text=True).stdout)
+
+        train_ds = self._prepare_data()
+        self._run_training(train_ds)
+
+        return get_last_checkpoint_dir(self.config.out_path)
+
+    @property
+    def tokenizer(self):
+        if not self._tokenizer:
+            self._tokenizer = AutoTokenizer.from_pretrained(self.config.model_id)
+
+        assert isinstance(self._tokenizer, PreTrainedTokenizer), (
+            "Tokenizer must be a PreTrainedTokenizer, but got {}".format(type(self._tokenizer))
+        )
+
+        if self._tokenizer.pad_token is None:
+            logger.warning("Tokenizer has no pad token, setting it to eos token")
+            self._tokenizer.pad_token = self._tokenizer.eos_token
+
+        return self._tokenizer
+
+    @property
+    def model(self):
+        if not self._model:
+            self._model = AutoModelForCausalLM.from_pretrained(self.config.model_id)
+
+        assert self._model is not None, "Model should be initialized"
+        return self._model
+
+    @property
+    def data_collator(self):
+        return DataCollatorForTokenClassification(
+            tokenizer=self.tokenizer, padding=True, pad_to_multiple_of=8, return_tensors="pt"
+        )
+
+    @property
+    def training_args(self):
+        return Seq2SeqTrainingArguments(
+            **self.config.training_args.model_dump(),
+            **self._batch_size_config(
+                self.config.training_args.effective_train_batch_size,
+                self.config.training_args.per_device_train_batch_size,
+            ),
+            output_dir=self.config.out_path,
+        )
+
+    def _prepare_data(self):
+        train_ds = self.config.train_dataset.process_dataset()
+        logger.info("Dataset samples")
+        logger.info("Train")
+        logger.info(f"Input: {self.tokenizer.decode(train_ds[0]['input_ids'])}")
+        logger.info(f"Labels: {self.tokenizer.decode(train_ds[0]['labels'])}")
+
+        return train_ds
+
+    def _run_training(self, train_ds):
+        trainer = Seq2SeqTrainer(
+            model=self.model,
+            args=self.training_args,
+            train_dataset=train_ds,
+            data_collator=self.data_collator,
+            processing_class=self.tokenizer,
+        )
+
+        if self.config.save_schedule is not None:
+            trainer.add_callback(SaveByScheduleCallback(schedule=self.config.save_schedule))
+
+        trainer.train(resume_from_checkpoint=True)
+
+    def _directory_is_empty(self, directory: str, expected_epochs: int) -> bool:
+        p = Path(directory)
+        if not p.exists():
+            return True
+        if not p.is_dir():
+            raise Exception("Not a directory!")
+
+        checkpoint_dirs = list(p.glob("checkpoint-*"))
+        if not checkpoint_dirs:
+            return True
+
+        checkpoint_dirs.sort(key=lambda x: int(x.name.split("-")[1]))
+        last_checkpoint = checkpoint_dirs[-1] if checkpoint_dirs else None
+
+        if last_checkpoint:
+            state_file = last_checkpoint / "trainer_state.json"
+            if state_file.exists():
+                with open(state_file, "r") as f:
+                    state = json.load(f)
+                    if int(state.get("epoch", 0)) == expected_epochs:
+                        return False
+
+        return True
+
+    def _batch_size_config(self, effective_batch_size: int, per_device_train_batch_size: int):
+        gradient_accumulation_steps = effective_batch_size // per_device_train_batch_size
+        assert effective_batch_size % per_device_train_batch_size == 0, (
+            f"Effective batch size {effective_batch_size} is not divisible by per device batch size {per_device_train_batch_size}"
+        )
+        return {
+            "per_device_train_batch_size": per_device_train_batch_size,
+            "gradient_accumulation_steps": gradient_accumulation_steps,
+        }
diff --git a/src/core/training/callbacks/eval_every_n_epoch.py b/src/core/training/callbacks/eval_every_n_epoch.py
deleted file mode 100644
index 534cce0..0000000
--- a/src/core/training/callbacks/eval_every_n_epoch.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import math
-from typing import List, Tuple, Optional
-from transformers import TrainerCallback, TrainerControl, TrainerState
-
-class EvalEveryNEpochsCallback(TrainerCallback):
-    """
-    schedule: Optional list of (start_epoch, end_epoch, step) tuples.
-    If schedule is None, uses default: [(1,5,1), (6,10,2), (11,30,5)]
-    """
-    def __init__(
-        self,
-        schedule: Optional[List[Tuple[int, int, int]]] = None,
-        eval_on_train_begin: bool = False,
-        eval_on_train_end: bool = False,
-    ):
-        if schedule is None:
-            schedule = [(1, 5, 1), (6, 10, 2), (11, 30, 5)]
-
-        # validate schedule
-        for t in schedule:
-            if (
-                not isinstance(t, tuple)
-                or len(t) != 3
-                or not all(isinstance(x, int) for x in t)
-            ):
-                raise ValueError("Each schedule item must be a tuple of 3 integers (start, end, step).")
-            start, end, step = t
-            if start <= 0 or end < start or step <= 0:
-                raise ValueError("Invalid schedule tuple: start>0, end>=start, step>0 required.")
-
-        self.schedule = schedule
-        self.eval_on_train_begin = bool(eval_on_train_begin)
-        self.eval_on_train_end = bool(eval_on_train_end)
-
-    def on_train_begin(self, args, state: TrainerState, control: TrainerControl, **kwargs):
-        control.should_evaluate = bool(self.eval_on_train_begin)
-        return control
-
-    def _should_eval_epoch(self, epoch_num: int) -> bool:
-        """Return True if an evaluation should run on this integer epoch number."""
-        for start, end, step in self.schedule:
-            if start <= epoch_num <= end:
-                return ((epoch_num - start) % step) == 0
-        return False
-
-    def on_epoch_end(self, args, state: TrainerState, control: TrainerControl, **kwargs):
-        epoch_num = int(math.floor(state.epoch)) if state.epoch is not None else 0
-
-        control.should_evaluate = False
-
-        if epoch_num > 0 and self._should_eval_epoch(epoch_num):
-            control.should_evaluate = True
-
-        return control
-
-    def on_train_end(self, args, state: TrainerState, control: TrainerControl, **kwargs):
-        control.should_evaluate = bool(self.eval_on_train_end)
-        return control
diff --git a/src/core/training/callbacks/save_and_log_weights.py b/src/core/training/callbacks/save_and_log_weights.py
deleted file mode 100644
index f184571..0000000
--- a/src/core/training/callbacks/save_and_log_weights.py
+++ /dev/null
@@ -1,61 +0,0 @@
-from pathlib import Path
-from typing import Optional
-
-import json
-import torch
-from transformers import TrainerCallback, TrainerState, TrainerControl
-
-try:
-    from peft import PeftModel
-    _HAS_PEFT = True
-except Exception:
-    _HAS_PEFT = False
-
-class SaveOnEpochEndAndLogWeightsCallback(TrainerCallback):
-    def __init__(self, output_dir: str, save_full_model_for_non_lora: bool = False):
-        self.output_dir = Path(output_dir)
-        self.weights_dir = self.output_dir / "weights_by_epoch"
-        self.weights_dir.mkdir(parents=True, exist_ok=True)
-        self.save_full_model_for_non_lora = save_full_model_for_non_lora
-
-    def _is_lora_model(self, model) -> bool:
-        return _HAS_PEFT and isinstance(model, PeftModel)
-
-    def _save_json_marker(self, epoch_value: float):
-        # mini marker for progress
-        marker = {
-            "epoch": epoch_value,
-        }
-        with open(self.output_dir / "last_completed_epoch.json", "w", encoding="utf-8") as f:
-            json.dump(marker, f)
-
-    def on_epoch_end(self, args, state: TrainerState, control: TrainerControl, **kwargs):
-        # Force savings
-        control.should_save = True
-
-        # 2) Log weights
-        model = kwargs.get("model", None)
-        if model is not None:
-            epoch_int = int(state.epoch) if state.epoch is not None else -1
-            tag = f"epoch_{epoch_int:02d}"
-
-            try:
-                if self._is_lora_model(model):
-                    # Only adapters
-                    epoch_dir = self.weights_dir / tag
-                    epoch_dir.mkdir(parents=True, exist_ok=True)
-                    model.save_pretrained(str(epoch_dir), safe_serialization=True)
-                elif self.save_full_model_for_non_lora:
-                    # Can be big files for full weights, be careful!
-                    sd = {k: v.detach().cpu() for k, v in model.state_dict().items()}
-                    torch.save(sd, self.weights_dir / f"{tag}_full_model.pt")
-            except Exception as e:
-                print(f"[WARN] Failed to dump weights for {tag}: {e}")
-
-        # Mark of the end of epoch
-        try:
-            self._save_json_marker(state.epoch if state.epoch is not None else -1)
-        except Exception as e:
-            print(f"[WARN] Failed to write last_completed_epoch.json: {e}")
-
-        return control
diff --git a/src/core/training/callbacks/save_by_schedule.py b/src/core/training/callbacks/save_by_schedule.py
new file mode 100644
index 0000000..c542d62
--- /dev/null
+++ b/src/core/training/callbacks/save_by_schedule.py
@@ -0,0 +1,18 @@
+from typing import override
+
+from transformers import TrainerCallback, TrainerControl, TrainerState, TrainingArguments
+
+
+class SaveByScheduleCallback(TrainerCallback):
+    def __init__(self, schedule: list[int]):
+        self.schedule = schedule
+
+    @override
+    def on_epoch_end(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs) -> None:
+        assert state.epoch is not None
+
+        epoch_num = int(state.epoch)
+
+        control.should_save = False
+        if epoch_num in self.schedule:
+            control.should_save = True
diff --git a/src/core/training/callbacks/save_every_n_epoch.py b/src/core/training/callbacks/save_every_n_epoch.py
deleted file mode 100644
index a61b181..0000000
--- a/src/core/training/callbacks/save_every_n_epoch.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import math
-from transformers import TrainerCallback, TrainerControl, TrainerState
-
-class SaveEveryNEpochsCallback(TrainerCallback):
-    def __init__(self, n_epochs: int = 4):
-        if n_epochs <= 0:
-            raise ValueError("n_epochs must be > 0")
-        self.n = n_epochs
-
-    def on_epoch_end(self, args, state: TrainerState, control: TrainerControl, **kwargs):
-        epoch_num = 0
-        if state.epoch is not None:
-            epoch_num = int(math.floor(state.epoch))
-        if epoch_num > 0 and (epoch_num % self.n == 0):
-            control.should_save = True
-        return control
\ No newline at end of file
diff --git a/src/core/training/lora_trainer.py b/src/core/training/lora_trainer.py
new file mode 100644
index 0000000..8169ff6
--- /dev/null
+++ b/src/core/training/lora_trainer.py
@@ -0,0 +1,63 @@
+from typing import Literal, override
+
+from peft import LoraConfig, TaskType, get_peft_model
+from pydantic import BaseModel, Field
+from transformers import AutoModelForCausalLM
+
+from core.training.base_trainer import BaseTrainer, BaseTrainerConfig, BaseTrainingArgs
+
+
+class LoRATrainingArgs(BaseTrainingArgs):
+    # Sane overrides for LoRA SFT fine-tuning
+    effective_train_batch_size: int = 64
+    learning_rate: float = 1e-4
+    warmup_ratio: float = 0.06
+    weight_decay: float = 0.0
+
+
+class LoRASpecificTrainingArgs(BaseModel):
+    r: int = 8
+    alpha: int = 16
+    lora_dropout: float = 0.1
+    init_lora_weights: Literal[
+        "gaussian", "eva", "olora", "pissa", "pissa_niter_[number of iters]", "corda", "loftq", "orthogonal"
+    ] = "gaussian"
+    target_modules: list[str] = Field(
+        default_factory=lambda: [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+            "o_proj",
+            "gate_proj",
+            "up_proj",
+            "down_proj",
+        ]
+    )
+    bias: Literal["none", "all", "lora_only"] = "none"
+    task_type: TaskType = TaskType.CAUSAL_LM
+    use_rslora: bool = False
+
+
+class LoRATrainerConfig(BaseTrainerConfig[LoRATrainingArgs]):
+    lora_training_args: LoRASpecificTrainingArgs = Field(default_factory=LoRASpecificTrainingArgs)
+
+
+class LoRATrainer(BaseTrainer[LoRATrainerConfig]):
+    @property
+    @override
+    def model(self):
+        if not self._model:
+            model = AutoModelForCausalLM.from_pretrained(self.config.model_id)
+            peft_config = LoraConfig(
+                r=self.config.lora_training_args.r,
+                lora_alpha=self.config.lora_training_args.alpha,
+                lora_dropout=self.config.lora_training_args.lora_dropout,
+                init_lora_weights=self.config.lora_training_args.init_lora_weights,
+                target_modules=self.config.lora_training_args.target_modules,
+                bias=self.config.lora_training_args.bias,
+                task_type=self.config.lora_training_args.task_type,
+                use_rslora=self.config.lora_training_args.use_rslora,
+            )
+            self._model = get_peft_model(model, peft_config)
+
+        return self._model
diff --git a/src/core/training/sft_by_complexity_split/sft_by_single_complexity_split.py b/src/core/training/sft_by_complexity_split/sft_by_single_complexity_split.py
index c7218de..54e570b 100644
--- a/src/core/training/sft_by_complexity_split/sft_by_single_complexity_split.py
+++ b/src/core/training/sft_by_complexity_split/sft_by_single_complexity_split.py
@@ -22,7 +22,7 @@
 import core.prompts.mmlu_single_token_answer as prompts
 from core.training.sft_by_complexity_split.cot_eval_trainer import CoTEvalTrainer
 from core.training.callbacks.save_and_log_weights import SaveOnEpochEndAndLogWeightsCallback
-from core.training.callbacks.save_every_n_epoch import SaveEveryNEpochsCallback
+from core.training.callbacks.save_by_schedule import SaveEveryNEpochsCallback
 from core.training.callbacks.eval_every_n_epoch import EvalEveryNEpochsCallback
 
 from core.utils.device import DEVICE_MAP
@@ -96,55 +96,6 @@ def directory_is_empty(directory: str, expected_epochs: int) -> bool:
 
     return True
 
-
-def get_sys_prompt(row):
-    subject = row["base_cluster"]
-    return prompts.single_token_sys_prompt(subject)
-
-
-def get_user_prompt(row):
-    question = row["question"]
-    options = ast.literal_eval(row["options"])
-    return prompts.single_token_answer_prompt(question, options)
-
-
-def get_sys_prompt_thinking(row):
-    """System prompt for training with thinking/reasoning tokens."""
-    subject = row["base_cluster"]
-    return prompts.single_token_sys_prompt_with_thinking(subject)
-
-
-def get_user_prompt_thinking(row):
-    """User prompt — same format as single-token, model produces thinking + answer."""
-    question = row["question"]
-    options = ast.literal_eval(row["options"])
-    return prompts.single_token_answer_prompt(question, options)
-
-
-def get_sys_prompt_cot_eval(row):
-    subject = row["base_cluster"]
-    return cot_prompts.cot_sys_prompt(subject)
-
-
-def get_user_prompt_cot_eval(row):
-    question = row["question"]
-    options = ast.literal_eval(row["options"])
-    return cot_prompts.cot_answer_prompt(question, options)
-
-
-def get_sys_prompt_cot_eval_thinking(row):
-    """System prompt for CoT eval when model was trained with thinking tokens."""
-    subject = row["base_cluster"]
-    return prompts.single_token_sys_prompt_with_thinking(subject)
-
-
-def get_user_prompt_cot_eval_thinking(row):
-    """User prompt for CoT eval with thinking — same as training prompt."""
-    question = row["question"]
-    options = ast.literal_eval(row["options"])
-    return prompts.single_token_answer_prompt(question, options)
-
-
 # helper for default LoRA
 def _build_lora_config(model, lora_kwargs: dict | None) -> LoraConfig:
     lora_kwargs = lora_kwargs or {}
diff --git a/src/core/utils/logger.py b/src/core/utils/logger.py
new file mode 100644
index 0000000..11f8071
--- /dev/null
+++ b/src/core/utils/logger.py
@@ -0,0 +1,23 @@
+import re
+
+from loguru import logger as _logger
+
+
+class ArgLogger:
+    def __getattr__(self, level):
+        def log(msg, *args, **kwargs):
+            # Count how many {} placeholders (excluding escaped {{}})
+            placeholders = len(re.findall(r"(?<!\{)\{[^{]*?\}(?!\})", msg))
+            used = args[:placeholders]
+            unused = args[placeholders:]
+
+            formatted = msg.format(*used) if used else msg
+            if unused:
+                formatted += " | " + " | ".join(str(a) for a in unused)
+
+            getattr(_logger.opt(depth=1), level)(formatted, **kwargs)
+
+        return log
+
+
+logger = ArgLogger()
diff --git a/src/experiments/sft_by_complexity_splits/mmlu/llama_3b.py b/src/experiments/sft_by_complexity_splits/mmlu/llama_3b.py
new file mode 100644
index 0000000..8619dd3
--- /dev/null
+++ b/src/experiments/sft_by_complexity_splits/mmlu/llama_3b.py
@@ -0,0 +1,33 @@
+from pathlib import Path
+
+from transformers import AutoTokenizer
+
+from core.datasets.mmlu.mmlu_single_token_response_dataset import MMLUSingleTokenResponseDataset, QADatasetConfig
+from core.datasets.qa_dataset_adapter import QADatasetAdapter
+from core.training.lora_trainer import LoRATrainer, LoRATrainerConfig, LoRATrainingArgs
+
+MODEL_NAME = "meta-llama/Llama-3.2-3B-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+
+trainer = LoRATrainer(
+    config=LoRATrainerConfig(
+        out_path=Path(__file__).parent.joinpath("../../../../artifacts/sft_by_complexity_splits/llama_3b").as_posix(),
+        model_id=MODEL_NAME,
+        train_dataset=QADatasetAdapter(
+            dataset=MMLUSingleTokenResponseDataset(
+                config=QADatasetConfig(
+                    path=Path(__file__)
+                    .parent.joinpath("../../../../data/out/splits/single_token_entropy/qwen_3b/group0_train.parquet")
+                    .as_posix()
+                ),
+                tokenizer=tokenizer,
+            )
+        ),
+        training_args=LoRATrainingArgs(num_train_epochs=20, per_device_train_batch_size=32),
+        save_schedule=[1, 2, 3, 4, 5, 6, 8, 10, 12, 15, 20],
+    ),
+    tokenizer=tokenizer,
+)
+trainer.train()
diff --git a/uv.def b/uv.def
index e9859fa..c86e604 100644
--- a/uv.def
+++ b/uv.def
@@ -1,5 +1,5 @@
 Bootstrap: docker
-From: ghcr.io/astral-sh/uv:python3.12-bookworm-slim
+From: ghcr.io/astral-sh/uv:python3.13-trixie-slim
 
 %post
     # Update and install packages
diff --git a/uv.lock b/uv.lock
index 95d8a43..8fe065d 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,6 +1,6 @@
 version = 1
-revision = 3
-requires-python = ">=3.12"
+revision = 2
+requires-python = ">=3.13"
 resolution-markers = [
     "python_full_version >= '3.14' and sys_platform == 'win32'",
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