Simplify and update trl examples (#38772)

* Simplify and update trl examples

* Remove optim_args from SFTConfig in Trainer documentation

* Update docs/source/en/trainer.md

* Apply suggestions from code review

* Update docs/source/en/trainer.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

---------

Co-authored-by: Quentin Gallouédec <qgallouedec@Quentins-MacBook-Pro.local>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

docs/source/ko/model_doc/mamba.md

@@ -58,34 +58,18 @@ print(tokenizer.batch_decode(out))
 
 ```python 
 from datasets import load_dataset
-from trl import SFTTrainer
+from trl import SFTConfig, SFTTrainer
 from peft import LoraConfig
-from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
+
 model_id = "state-spaces/mamba-130m-hf"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id)
 dataset = load_dataset("Abirate/english_quotes", split="train")
-training_args = TrainingArguments(
-    output_dir="./results",
-    num_train_epochs=3,
-    per_device_train_batch_size=4,
-    logging_dir='./logs',
-    logging_steps=10,
-    learning_rate=2e-3
-)
-lora_config =  LoraConfig(
-        r=8,
-        target_modules=["x_proj", "embeddings", "in_proj", "out_proj"],
-        task_type="CAUSAL_LM",
-        bias="none"
-)
+training_args = SFTConfig(dataset_text_field="quote")
+lora_config =  LoraConfig(target_modules=["x_proj", "embeddings", "in_proj", "out_proj"])
 trainer = SFTTrainer(
-    model=model,
-    tokenizer=tokenizer,
+    model=model_id,
     args=training_args,
-    peft_config=lora_config,
     train_dataset=dataset,
-    dataset_text_field="quote",
+    peft_config=lora_config,
 )
 trainer.train()
 ```