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/en/model_doc/mamba.md

@@ -97,39 +97,22 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 - Mamba stacks `mixer` layers which are equivalent to `Attention` layers. You can find the main logic of Mamba in the `MambaMixer` class.
 - The example below demonstrates how to fine-tune Mamba with [PEFT](https://huggingface.co/docs/peft).
 
-   ```py
-   from datasets import load_dataset
-   from trl import 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"
-   )
-   trainer = SFTTrainer(
-       model=model,
-       processing_class=tokenizer,
+  ```py
+  from datasets import load_dataset
+  from trl import SFTConfig, SFTTrainer
+  from peft import LoraConfig
+
+  model_id = "state-spaces/mamba-130m-hf"
+  dataset = load_dataset("Abirate/english_quotes", split="train")
+  training_args = SFTConfig(dataset_text_field="quote")
+  lora_config =  LoraConfig(target_modules=["x_proj", "embeddings", "in_proj", "out_proj"])
+  trainer = SFTTrainer(
+      model=model_id,
       args=training_args,
-       peft_config=lora_config,
-       train_dataset=dataset,
-       dataset_text_field="quote",
-   )
-   trainer.train()
+      train_dataset=dataset,
+      peft_config=lora_config,
+  )
+  trainer.train()
    ```
 
 ## MambaConfig