Remove all traces of low_cpu_mem_usage (#38792)

* remove it from all py files

* remove it from the doc

* remove it from examples

* style

* remove traces of _fast_init

* Update test_peft_integration.py

* CIs

docs/source/ko/llm_tutorial_optimization.md

@@ -227,7 +227,7 @@ flush()
 이제 4비트 양자화가 제공하는 최대 GPU 메모리 사용량을 확인해 봅시다. 4비트로 모델을 양자화하려면 이전과 동일한 API를 사용하되 이번에는 `load_in_8bit=True` 대신 `load_in_4bit=True`를 전달하면 됩니다.
 
 ```python
-model = AutoModelForCausalLM.from_pretrained("bigcode/octocoder", load_in_4bit=True, low_cpu_mem_usage=True, pad_token_id=0)
+model = AutoModelForCausalLM.from_pretrained("bigcode/octocoder", load_in_4bit=True, pad_token_id=0)
 
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 

docs/source/ko/model_doc/chameleon.md

@@ -148,7 +148,6 @@ model_id = "facebook/chameleon-7b"
 model = ChameleonForConditionalGeneration.from_pretrained(
     model_id,
     torch_dtype=torch.bfloat16,
-    low_cpu_mem_usage=True,
     attn_implementation="flash_attention_2"
 ).to(0)
 ```

docs/source/ko/trainer.md

@@ -421,7 +421,7 @@ args = TrainingArguments(
 model_id = "google/gemma-2b"
 
 tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True).to(0)
+model = AutoModelForCausalLM.from_pretrained(model_id).to(0)
 
 trainer = trl.SFTTrainer(
     model=model,