diff --git a/llms/mlx_lm/LORA.md b/llms/mlx_lm/LORA.md
index e863abc4..e6f8d3a5 100644
--- a/llms/mlx_lm/LORA.md
+++ b/llms/mlx_lm/LORA.md
@@ -387,6 +387,10 @@ tokens-per-second, using the MLX Example
 [`wikisql`](https://github.com/ml-explore/mlx-examples/tree/main/lora/data)
 data set.
 
+## Logging
+
+You can log training metrics to Weights & Biases by adding the `--report-to-wandb` flag. This requires installing wandb manually with `pip install wandb`. When enabled, all training and validation metrics will be logged to your wandb account.
+
 [^lora]: Refer to the [arXiv paper](https://arxiv.org/abs/2106.09685) for more details on LoRA.
 
 [^qlora]: Refer to the paper [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314)
diff --git a/llms/mlx_lm/examples/lora_config.yaml b/llms/mlx_lm/examples/lora_config.yaml
index 36bc1dff..fe58875a 100644
--- a/llms/mlx_lm/examples/lora_config.yaml
+++ b/llms/mlx_lm/examples/lora_config.yaml
@@ -37,6 +37,9 @@ val_batches: 25
 # Adam learning rate.
 learning_rate: 1e-5
 
+# to report the loggs to WandB
+report_to_wand: true
+
 # Number of training steps between loss reporting.
 steps_per_report: 10