Add examples to the LLMs to be shown in the components gallery

src/distilabel/llms/anthropic.py

@@ -73,6 +73,50 @@ class AnthropicLLM(AsyncLLM):
         - `timeout`: the maximum time in seconds to wait for a response. Defaults to `600.0`.
         - `max_retries`: the maximum number of times to retry the request before failing.
             Defaults to `6`.
+
+    Examples:
+
+        Generate text:
+
+        ```python
+        from distilabel.llms import AnthropicLLM
+
+        llm = AnthropicLLM(model="claude-3-opus-20240229", api_key="api.key")
+
+        llm.load()
+
+        # Synchronous request
+        output = llm.generate(inputs=[[{"role": "user", "content": "Hello world!"}]])
+
+        # Asynchronous request
+        output = await llm.agenerate(input=[{"role": "user", "content": "Hello world!"}])
+        ```
+
+        Generate structured data:
+
+        ```python
+        from pydantic import BaseModel
+        from distilabel.llms import AnthropicLLM
+
+        class User(BaseModel):
+            name: str
+            last_name: str
+            id: int
+
+        llm = AnthropicLLM(
+            model="claude-3-opus-20240229",
+            api_key="api.key",
+            structured_output={"schema": User}
+        )
+
+        llm.load()
+
+        # Synchronous request
+        output = llm.generate(inputs=[[{"role": "user", "content": "Create a user profile for the following marathon"}]])
+
+        # Asynchronous request
+        output = await llm.agenerate(input=[{"role": "user", "content": "Create a user profile for the following marathon"}])
+        ```
     """
 
     model: str

src/distilabel/llms/anyscale.py

@@ -38,6 +38,24 @@ class AnyscaleLLM(OpenAILLM):
             `None` if not set.
         _api_key_env_var: the name of the environment variable to use for the API key.
             It is meant to be used internally.
+
+    Examples:
+
+        Generate text:
+
+        ```python
+        from distilabel.llms import AnyscaleLLM
+
+        llm = AnyscaleLLM(model="google/gemma-7b-it", api_key="api.key")
+
+        llm.load()
+
+        # Synchronous request
+        output = llm.generate(inputs=[[{"role": "user", "content": "Hello world!"}]])
+
+        # Asynchronous request
+        output = await llm.agenerate(input=[{"role": "user", "content": "Hello world!"}])
+        ```
     """
 
     base_url: Optional[RuntimeParameter[str]] = Field(

src/distilabel/llms/azure.py

@@ -46,6 +46,69 @@ class AzureOpenAILLM(OpenAILLM):
 
     Icon:
         `:simple-microsoftazure:`
+
+    Examples:
+
+        Generate text:
+
+        ```python
+        from distilabel.llms import AzureOpenAILLM
+
+        llm = AzureOpenAILLM(model="gpt-4-turbo", api_key="api.key")
+
+        llm.load()
+
+        # Synchrounous request
+        output = llm.generate(inputs=[[{"role": "user", "content": "Hello world!"}]])
+
+        # Asynchronous request
+        output = await llm.agenerate(input=[{"role": "user", "content": "Hello world!"}])
+        ```
+
+        Generate text from a custom endpoint following the OpenAI API:
+
+        ```python
+        from distilabel.llms import AzureOpenAILLM
+
+        llm = AzureOpenAILLM(
+            model="prometheus-eval/prometheus-7b-v2.0",
+            base_url=r"http://localhost:8080/v1"
+        )
+
+        llm.load()
+
+        # Synchronous request
+        output = llm.generate(inputs=[[{"role": "user", "content": "Hello world!"}]])
+
+        # Asynchronous request
+        output = await llm.agenerate(input=[{"role": "user", "content": "Hello world!"}])
+        ```
+
+        Generate structured data:
+
+        ```python
+        from pydantic import BaseModel
+        from distilabel.llms import AzureOpenAILLM
+
+        class User(BaseModel):
+            name: str
+            last_name: str
+            id: int
+
+        llm = AzureOpenAILLM(
+            model="gpt-4-turbo",
+            api_key="api.key",
+            structured_output={"schema": User}
+        )
+
+        llm.load()
+
+        # Synchronous request
+        output = llm.generate(inputs=[[{"role": "user", "content": "Create a user profile for the following marathon"}]])
+
+        # Asynchronous request
+        output = await llm.agenerate(input=[{"role": "user", "content": "Create a user profile for the following marathon"}])
+        ```
     """
 
     base_url: Optional[RuntimeParameter[str]] = Field(

src/distilabel/llms/cohere.py

@@ -70,6 +70,46 @@ class CohereLLM(AsyncLLM):
             to `120`.
         - `client_name`: the name of the client to use for the API requests. Defaults to
             `"distilabel"`.
+
+    Examples:
+
+        Generate text:
+
+        ```python
+        from distilabel.llms import CohereLLM
+
+        llm = CohereLLM(model="CohereForAI/c4ai-command-r-plus")
+
+        llm.load()
+
+        # Call the model
+        output = llm.generate(inputs=[[{"role": "user", "content": "Hello world!"}]])
+
+        Generate structured data:
+
+        ```python
+        from pydantic import BaseModel
+        from distilabel.llms import CohereLLM
+
+        class User(BaseModel):
+            name: str
+            last_name: str
+            id: int
+
+        llm = CohereLLM(
+            model="CohereForAI/c4ai-command-r-plus",
+            api_key="api.key",
+            structured_output={"schema": User}
+        )
+
+        llm.load()
+
+        # Synchronous request
+        output = llm.generate(inputs=[[{"role": "user", "content": "Create a user profile for the following marathon"}]])
+
+        # Asynchronous request
+        output = await llm.agenerate(input=[{"role": "user", "content": "Create a user profile for the following marathon"}])
+        ```
     """
 
     model: str

src/distilabel/llms/groq.py

@@ -61,6 +61,46 @@ class GroqLLM(AsyncLLM):
             failing. Defaults to `2`.
         - `timeout`: the maximum time in seconds to wait for a response from the API. Defaults
             to `120`.
+
+    Examples:
+
+        Generate text:
+
+        ```python
+        from distilabel.llms import GroqLLM
+
+        llm = GroqLLM(model="llama3-70b-8192")
+
+        llm.load()
+
+        # Call the model
+        output = llm.generate(inputs=[[{"role": "user", "content": "Hello world!"}]])
+
+        Generate structured data:
+
+        ```python
+        from pydantic import BaseModel
+        from distilabel.llms import GroqLLM
+
+        class User(BaseModel):
+            name: str
+            last_name: str
+            id: int
+
+        llm = GroqLLM(
+            model="llama3-70b-8192",
+            api_key="api.key",
+            structured_output={"schema": User}
+        )
+
+        llm.load()
+
+        # Synchronous request
+        output = llm.generate(inputs=[[{"role": "user", "content": "Create a user profile for the following marathon"}]])
+
+        # Asynchronous request
+        output = await llm.agenerate(input=[{"role": "user", "content": "Create a user profile for the following marathon"}])
+        ```
     """
 
     model: str

src/distilabel/llms/huggingface/transformers.py

@@ -65,6 +65,21 @@ class TransformersLLM(LLM, CudaDevicePlacementMixin):
 
     Icon:
         `:hugging:`
+
+    Examples:
+
+        Generate text:
+
+        ```python
+        from distilabel.llms import TransformersLLM
+
+        llm = TransformersLLM(model="microsoft/Phi-3-mini-4k-instruct")
+
+        llm.load()
+
+        # Call the model
+        output = llm.generate(inputs=[[{"role": "user", "content": "Hello world!"}]])
+        ```
     """
 
     model: str

src/distilabel/llms/litellm.py

@@ -39,6 +39,46 @@ class LiteLLM(AsyncLLM):
 
     Runtime parameters:
         - `verbose`: whether to log the LiteLLM client's logs. Defaults to `False`.
+
+    Examples:
+
+        Generate text:
+
+        ```python
+        from distilabel.llms import LiteLLM
+
+        llm = LiteLLM(model="gpt-3.5-turbo")
+
+        llm.load()
+
+        # Call the model
+        output = llm.generate(inputs=[[{"role": "user", "content": "Hello world!"}]])
+
+        Generate structured data:
+
+        ```python
+        from pydantic import BaseModel
+        from distilabel.llms import LiteLLM
+
+        class User(BaseModel):
+            name: str
+            last_name: str
+            id: int
+
+        llm = LiteLLM(
+            model="gpt-3.5-turbo",
+            api_key="api.key",
+            structured_output={"schema": User}
+        )
+
+        llm.load()
+
+        # Synchronous request
+        output = llm.generate(inputs=[[{"role": "user", "content": "Create a user profile for the following marathon"}]])
+
+        # Asynchronous request
+        output = await llm.agenerate(input=[{"role": "user", "content": "Create a user profile for the following marathon"}])
+        ```
     """
 
     model: str

src/distilabel/llms/llamacpp.py

@@ -59,6 +59,58 @@ class LlamaCppLLM(LLM):
     References:
         - [`llama.cpp`](https://github.com/ggerganov/llama.cpp)
         - [`llama-cpp-python`](https://github.com/abetlen/llama-cpp-python)
+
+    Examples:
+
+        Generate text:
+
+        ```python
+        from pathlib import Path
+        from distilabel.llms import LlamaCppLLM
+
+        # You can follow along this example downloading the following model running the following
+        # command in the terminal, that will download the model to the `Downloads` folder:
+        # curl -L -o ~/Downloads/openhermes-2.5-mistral-7b.Q4_K_M.gguf https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/resolve/main/openhermes-2.5-mistral-7b.Q4_K_M.gguf
+
+        model_path = "Downloads/openhermes-2.5-mistral-7b.Q4_K_M.gguf"
+
+        llm = LlamaCppLLM(
+            model_path=str(Path.home() / model_path),
+            n_gpu_layers=-1,  # To use the GPU if available
+            n_ctx=1024,       # Set the context size
+        )
+
+        llm.load()
+
+        # Call the model
+        output = llm.generate(inputs=[[{"role": "user", "content": "Hello world!"}]])
+        ```
+
+        Generate structured data:
+
+        ```python
+        from pathlib import Path
+        from distilabel.llms import LlamaCppLLM
+
+        model_path = "Downloads/openhermes-2.5-mistral-7b.Q4_K_M.gguf"
+
+        class User(BaseModel):
+            name: str
+            last_name: str
+            id: int
+
+        llm = LlamaCppLLM(
+            model_path=str(Path.home() / model_path),  # type: ignore
+            n_gpu_layers=-1,
+            n_ctx=1024,
+            structured_output={"format": "json", "schema": Character},
+        )
+
+        llm.load()
+
+        # Call the model
+        output = llm.generate(inputs=[[{"role": "user", "content": "Create a user profile for the following marathon"}]])
+        ```
     """
 
     model_path: RuntimeParameter[FilePath] = Field(