diff --git a/docs/source/en/quantization/finegrained_fp8.md b/docs/source/en/quantization/finegrained_fp8.md
index aaed35b4d9..785e5e88e1 100644
--- a/docs/source/en/quantization/finegrained_fp8.md
+++ b/docs/source/en/quantization/finegrained_fp8.md
@@ -39,10 +39,10 @@ pip install --upgrade accelerate torch
 By default, the weights are loaded in full precision (torch.float32) regardless of the actual data type the weights are stored in such as torch.float16. Set `torch_dtype="auto"` to load the weights in the data type defined in a model's `config.json` file to automatically load the most memory-optimal data type.
 
 ```py
-from transformers import FP8Config, AutoModelForCausalLM, AutoTokenizer
+from transformers import FineGrainedFP8Config, AutoModelForCausalLM, AutoTokenizer
 
 model_name = "meta-llama/Meta-Llama-3-8B"
-quantization_config = FP8Config()
+quantization_config = FineGrainedFP8Config()
 quantized_model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto", quantization_config=quantization_config)
 
 tokenizer = AutoTokenizer.from_pretrained(model_name)