clarify

docs/source/fsdp_qlora.md

@@ -9,21 +9,26 @@ This guide provides a brief guide on how bitsandbytes supports storing quantized
 
 ## Quantized data storage
 
-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.
+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.
 
 ```py
-import torch
-import bitsandbytes as bnb
-
-model = bnb.nn.Linear4bit(
-    input_features,
-    output_features,
-    quant_type="fp4",
-    quant_storage=torch.uint8,
+from transformers import BitsAndBytesConfig, AutoModelForCausalLM
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_quant_storage=torch.bfloat16,
 )
-```
 
-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.
+model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-2-70b",
+    quantization_config=bnb_config,
+    torch_dtype=torch.bfloat16,
+)
+```
 
 ## Training