Docs: 4 bit doc corrections (#24572)

4 bit doc corrections

docs/source/en/perf_infer_gpu_one.md

@@ -67,23 +67,23 @@ You can quickly run a FP4 model on a single GPU by running the following code:
 from transformers import AutoModelForCausalLM
 
 model_name = "bigscience/bloom-2b5"
-model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_4bit=True)
+model_4bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_4bit=True)
 ```
 Note that `device_map` is optional but setting `device_map = 'auto'` is prefered for inference as it will dispatch efficiently the model on the available ressources.
 
 ### Running FP4 models - multi GPU setup
 
-The way to load your mixed 8-bit model in multiple GPUs is as follows (same command as single GPU setup):
+The way to load your mixed 4-bit model in multiple GPUs is as follows (same command as single GPU setup):
 ```py
 model_name = "bigscience/bloom-2b5"
-model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_4bit=True)
+model_4bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_4bit=True)
 ```
 But you can control the GPU RAM you want to allocate on each GPU using `accelerate`. Use the `max_memory` argument as follows:
 
 ```py
 max_memory_mapping = {0: "600MB", 1: "1GB"}
 model_name = "bigscience/bloom-3b"
-model_8bit = AutoModelForCausalLM.from_pretrained(
+model_4bit = AutoModelForCausalLM.from_pretrained(
     model_name, device_map="auto", load_in_4bit=True, max_memory=max_memory_mapping
 )
 ```