Refactor logits processor handling in LlamaCppLLM

- Replaced the `_set_logits_processor` method with direct assignment of `_logits_processor` using `_prepare_structured_output`.
- Simplified the logic for setting the logits processor in both the `load` and generation methods, enhancing code clarity and maintainability.

src/distilabel/models/llms/llamacpp.py

@@ -194,7 +194,9 @@ def load(self) -> None:
         )
 
         if self.structured_output:
-            self._set_logits_processor(self.structured_output)
+            self._logits_processor = self._prepare_structured_output(
+                self.structured_output
+            )
 
         if self.use_magpie_template or self.magpie_pre_query_template:
             if not self.tokenizer_id:
@@ -221,19 +223,6 @@ def load(self) -> None:
         # out of the model name, which won't be available until the `Llama` instance is created.
         super().load()
 
-    def _set_logits_processor(
-        self, structured_output: Optional[OutlinesStructuredOutputType] = None
-    ) -> None:
-        from distilabel.steps.tasks.structured_outputs.outlines import (
-            outlines_below_0_1_0,
-        )
-
-        processor = self._prepare_structured_output(structured_output)
-        if outlines_below_0_1_0:
-            self._logits_processor = processor
-        else:
-            self._logits_processor = [processor]
-
     @property
     def model_name(self) -> str:
         """Returns the model name used for the LLM."""
@@ -352,8 +341,9 @@ def generate(  # type: ignore
                 # after each generation, so subsequent calls yield nothing. This is a workaround
                 # until is fixed in the `llama_cpp` or `outlines` libraries.
                 if structured_output:
-                    self._set_logits_processor(structured_output)
-
+                    self._logits_processor = self._prepare_structured_output(
+                        structured_output
+                    )
                 if self.tokenizer_id is None:
                     completion = self._generate_chat_completion(
                         input,