diff --git a/src/distilabel/models/llms/llamacpp.py b/src/distilabel/models/llms/llamacpp.py
index ba30735f61..652b9f2a7e 100644
--- a/src/distilabel/models/llms/llamacpp.py
+++ b/src/distilabel/models/llms/llamacpp.py
@@ -26,10 +26,7 @@
 if TYPE_CHECKING:
     from llama_cpp import CreateChatCompletionResponse, Llama, LogitsProcessorList
 
-    from distilabel.steps.tasks.typing import (
-        FormattedInput,
-        StandardInput,
-    )
+    from distilabel.steps.tasks.typing import FormattedInput, StandardInput
 
 
 class LlamaCppLLM(LLM, MagpieChatTemplateMixin):
@@ -173,7 +170,7 @@ class User(BaseModel):
     _logits_processor: Optional["LogitsProcessorList"] = PrivateAttr(default=None)
     _model: Optional["Llama"] = PrivateAttr(...)
 
-    @model_validator(mode="after")  # type: ignore
+    @model_validator(mode="after")
     def validate_magpie_usage(
         self,
     ) -> "LlamaCppLLM":
@@ -195,7 +192,7 @@ def load(self) -> None:
             ) from ie
 
         self._model = Llama(
-            model_path=self.model_path.as_posix(),  # type: ignore
+            model_path=self.model_path.as_posix(),
             seed=self.seed,
             n_ctx=self.n_ctx,
             n_batch=self.n_batch,
@@ -223,7 +220,7 @@ def load(self) -> None:
                 from transformers import AutoTokenizer
             except ImportError as ie:
                 raise ImportError(
-                    "Transformers is not installed. Please install it using `pip install transformers`."
+                    "Transformers is not installed. Please install it using `pip install 'distilabel[hf-transformers]'`."
                 ) from ie
             self._tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_id)
             if self._tokenizer.chat_template is None:
diff --git a/src/distilabel/models/llms/ollama.py b/src/distilabel/models/llms/ollama.py
index ae3f2715ce..b303bd526a 100644
--- a/src/distilabel/models/llms/ollama.py
+++ b/src/distilabel/models/llms/ollama.py
@@ -14,7 +14,6 @@
 
 from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Sequence, Union
 
-from llama_cpp.llama_types import CreateChatCompletionResponse
 from pydantic import Field, PrivateAttr, model_validator, validate_call
 from typing_extensions import TypedDict
 
@@ -26,13 +25,11 @@
 from distilabel.steps.tasks.typing import InstructorStructuredOutputType, StandardInput
 
 if TYPE_CHECKING:
-    from llama_cpp import CreateChatCompletionResponse
     from ollama import AsyncClient
+    from ollama._types import ChatResponse, GenerateResponse
 
     from distilabel.models.llms.typing import LLMStatistics
-    from distilabel.steps.tasks.typing import (
-        StandardInput,
-    )
+    from distilabel.steps.tasks.typing import StandardInput
 
 
 # Copied from `ollama._types.Options`
@@ -146,7 +143,7 @@ class OllamaLLM(AsyncLLM, MagpieChatTemplateMixin):
         "`llama3`, `qwen2` or another pre-query template provided.",
     )
     _num_generations_param_supported = False
-    _aclient: Optional["AsyncClient"] = PrivateAttr(...)
+    _aclient: Optional["AsyncClient"] = PrivateAttr(...)  # type: ignore
 
     @model_validator(mode="after")  # type: ignore
     def validate_magpie_usage(
@@ -183,7 +180,7 @@ def load(self) -> None:
                 from transformers import AutoTokenizer
             except ImportError as ie:
                 raise ImportError(
-                    "Transformers is not installed. Please install it using `pip install transformers`."
+                    "Transformers is not installed. Please install it using `pip install 'distilabel[hf-transformers]'`."
                 ) from ie
             self._tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_id)
             if self._tokenizer.chat_template is None:
@@ -202,7 +199,7 @@ async def _generate_chat_completion(
         format: Literal["", "json"] = "",
         options: Union[Options, None] = None,
         keep_alive: Union[bool, None] = None,
-    ) -> "CreateChatCompletionResponse":
+    ) -> "ChatResponse":
         return await self._aclient.chat(
             model=self.model,
             messages=input,
@@ -239,7 +236,7 @@ async def _generate_with_text_generation(
         format: Literal["", "json"] = None,
         options: Union[Options, None] = None,
         keep_alive: Union[bool, None] = None,
-    ) -> "CreateChatCompletionResponse":
+    ) -> "GenerateResponse":
         input = self.prepare_input(input)
         return await self._aclient.generate(
             model=self.model,
@@ -281,10 +278,8 @@ async def agenerate(
                 )
                 text = completion["message"]["content"]
             else:
-                completion: CreateChatCompletionResponse = (
-                    await self._generate_with_text_generation(
-                        input, format, options, keep_alive
-                    )
+                completion = await self._generate_with_text_generation(
+                    input, format, options, keep_alive
                 )
                 text = completion.response
         except Exception as e: