diff --git a/swanlab/integration/torchtune.py b/swanlab/integration/torchtune.py
new file mode 100644
index 00000000..f49d6ba4
--- /dev/null
+++ b/swanlab/integration/torchtune.py
@@ -0,0 +1,88 @@
+from torchtune.utils._distributed import get_world_size_and_rank
+from torchtune.utils.metric_logging import MetricLoggerInterface
+from typing import Mapping, Optional, Union
+
+from numpy import ndarray
+from omegaconf import DictConfig, OmegaConf
+from torch import Tensor
+
+Scalar = Union[Tensor, ndarray, int, float]
+
+
+class SwanLabLogger(MetricLoggerInterface):
+    """
+    Example:
+        In torchtune config:
+        >>> # Logging
+        >>> metric_logger:
+        >>>     _component_: swanlab.integration.huggingface.SwanLabLogger
+        >>>     project: "gemma-lora-finetune"
+        >>>     experiment_name: "gemma-2b"
+        >>>     log_dir: ${output_dir}
+
+    Raises:
+        ImportError: If ``swanlab`` package is not installed.
+
+    Note:
+        This logger requires the swanlab package to be installed.
+        You can install it with `pip install swanlab`.
+        In order to use the logger, you need to login to your SwanLab account.
+        You can do this by running `swanlab login` in your terminal.
+    """
+
+    def __init__(
+        self,
+        project: str = "torchtune",
+        workspace: Optional[str] = None,
+        experiment_name: Optional[str] = None,
+        description: Optional[str] = None,
+        mode: Optional[str] = None,
+        log_dir: Optional[str] = None,
+        **kwargs,
+    ):
+        try:
+            import swanlab
+        except ImportError as e:
+            raise ImportError(
+                "``swanlab`` package not found. Please install swanlab using `pip install swanlab` to use SwanLabLogger."
+            ) from e
+        self._swanlab = swanlab
+
+        # Use dir if specified, otherwise use log_dir.
+        self.log_dir = kwargs.pop("dir", log_dir)
+
+        _, self.rank = get_world_size_and_rank()
+
+        if self._swanlab.get_run() is None and self.rank == 0:
+            run = self._swanlab.init(
+                project=project,
+                workspace=workspace,
+                name=experiment_name,
+                description=description,
+                mode=mode,
+                logdir=self.log_dir,
+                **kwargs,
+            )
+
+        self.config_allow_val_change = kwargs.get("allow_val_change", False)
+
+    def log_config(self, config: DictConfig) -> None:
+        if self._swanlab.get_run():
+            resolved = OmegaConf.to_container(config, resolve=True)
+            self._swanlab.config.update(resolved, allow_val_change=self.config_allow_val_change)
+
+    def log(self, name: str, data: Scalar, step: int) -> None:
+        if self._swanlab.get_run():
+            self._swanlab.log({name: data}, step=step)
+
+    def log_dict(self, payload: Mapping[str, Scalar], step: int) -> None:
+        if self._swanlab.get_run():
+            self._swanlab.log({**payload}, step=step)
+
+    def __del__(self) -> None:
+        if self._swanlab.get_run():
+            self._swanlab.finish()
+
+    def close(self) -> None:
+        if self._swanlab.get_run():
+            self._swanlab.finish()