diff --git a/docs/source/fsdp_qlora.md b/docs/source/fsdp_qlora.md
index 648e79092..c623fa424 100644
--- a/docs/source/fsdp_qlora.md
+++ b/docs/source/fsdp_qlora.md
@@ -11,7 +11,10 @@ This guide provides a brief guide on how bitsandbytes supports storing quantized
 
 FSDP only supports sharding float data types which can be problematic because quantized weights are typically stored as integer data types (uint8). bitsandbytes doesn't have this problem because it uses `StoreChar` to read and write quantized weights regardless of the data type storage. This makes it simple to add a `quant_storage` parameter to the [`~nn.Linear4bit`] and [`~nn.Params4bit`] classes and set it to `torch.uint8` to maintain backward compatibility with the codebase. With the `quant_storage` parameter, you can select any of the FSDP supported data types to shard [`~nn.Linear4bit`] with such as bfloat16, float16 or float32.
 
-For example, you can configure this option in [`transformers.BitsAndBytesConfig`] by setting the `bnb_4bit_quant_storage` parameter.
+You'll typically access and configure this option from [`transformers.BitsAndBytesConfig`] by setting the `bnb_4bit_quant_storage` parameter. It is very **important** the `quant_storage` data type matches the data types used throughout the model because FSDP can only wrap layers and modules that have the *same floating data type*. Making sure the data types are aligned will ensure the model is correctly sharded.
+
+> [!TIP]
+> The `compute_dtype` is the data type used for computation inside the CUDA kernel, where the 4-bit quantized weights are unpacked from the data type in `quant_storage` and dequantized to `compute_dtype`. We recommend using torch.bfloat16 (if available on your hardware) for better numerical stability.
 
 ```py
 from transformers import BitsAndBytesConfig, AutoModelForCausalLM
@@ -30,9 +33,11 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 ```
 
+Check out this [section](https://hf.co/docs/peft/main/en/accelerate/fsdp#use-peft-qlora-and-fsdp-for-finetuning-large-models-on-multiple-gpus) of the PEFT documentation for the config file and training code to run FSDP-QLoRA training.
+
 ## Training
 
-bitsandbytes is deeply integrated with the Hugging Face ecosystem, making it easy to use with libraries like [Transformers](https://hf/co/docs/transformers), [PEFT](https://hf/co/docs/peft), and [TRL](https://hf/co/docs/trl).
+bitsandbytes is deeply integrated with the Hugging Face ecosystem, making it easy to use with libraries like [Transformers](https://hf.co/docs/transformers), [PEFT](https://hf.co/docs/peft), and [TRL](https://hf.co/docs/trl).
 
 Before you begin, make sure you have the latest libraries installed.