Show how to turn on experiment tracking for fine-tuning (#2742)

Co-authored-by: Morgan McGuire <[email protected]>

README.md

@@ -327,6 +327,7 @@ Tips:
 - If you are using V100 which is not supported by FlashAttention, you can use the [memory-efficient attention](https://arxiv.org/abs/2112.05682) implemented in [xFormers](https://github.com/facebookresearch/xformers). Install xformers and replace `fastchat/train/train_mem.py` above with [fastchat/train/train_xformers.py](fastchat/train/train_xformers.py).
 - If you meet out-of-memory due to "FSDP Warning: When using FSDP, it is efficient and recommended... ", see solutions [here](https://github.com/huggingface/transformers/issues/24724#issuecomment-1645189539).
 - If you meet out-of-memory during model saving, see solutions [here](https://github.com/pytorch/pytorch/issues/98823).
+- To turn on logging to popular experiment tracking tools such as Tensorboard, MLFlow or Weights & Biases, use the `report_to` argument, e.g. pass `--report_to wandb` to turn on logging to Weights & Biases.
 
 ### Other models, platforms and LoRA support
 More instructions to train other models (e.g., FastChat-T5) and use LoRA are in [docs/training.md](docs/training.md).