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llama_cpp

LlamaCppLLM

Bases: LLM

Source code in src/distilabel/llm/llama_cpp.py 
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class LlamaCppLLM(LLM):
    def __init__(
        self,
        model: "Llama",
        task: "Task",
        max_new_tokens: int = 128,
        temperature: float = 0.8,
        top_p: float = 0.95,
        top_k: int = 40,
        repeat_penalty: float = 1.1,
        prompt_format: Union[SupportedFormats, None] = None,
        prompt_formatting_fn: Union[Callable[..., str], None] = None,
    ) -> None:
        """Initializes the LlamaCppLLM class.

        Args:
            model (Llama): the llama-cpp model to be used.
            task (Task): the task to be performed by the LLM.
            max_new_tokens (int, optional): the maximum number of tokens to be generated.
                Defaults to 128.
            temperature (float, optional): the temperature to be used for generation.
                Defaults to 0.8.
            top_p (float, optional): the top-p value to be used for generation.
                Defaults to 0.95.
            top_k (int, optional): the top-k value to be used for generation.
                Defaults to 40.
            repeat_penalty (float, optional): the repeat penalty to be used for generation.
                Defaults to 1.1.
            prompt_format (Union[SupportedFormats, None], optional): the format to be used
                for the prompt. If `None`, the default format of the task will be used, available
                formats are `openai`, `chatml`, `llama2`, `zephyr`, and `default`. Defaults to `None`,
                but `default` (concatenation of `system_prompt` and `formatted_prompt` with a line-break)
                will be used if no `prompt_formatting_fn` is provided.
            prompt_formatting_fn (Union[Callable[..., str], None], optional): a function to be
                applied to the prompt before generation. If `None`, no formatting will be applied.
                Defaults to `None`.

        Examples:
            >>> from llama_cpp import Llama
            >>> from distilabel.tasks.text_generation import TextGenerationTask as Task
            >>> from distilabel.llm import LlamaCppLLM
            >>> model = Llama(model_path="path/to/model")
            >>> task = Task()
            >>> llm = LlamaCppLLM(model=model, task=task)
        """
        super().__init__(
            task=task,
            prompt_format=prompt_format,
            prompt_formatting_fn=prompt_formatting_fn,
        )

        if not _LLAMA_CPP_AVAILABLE:
            raise ImportError(
                "`LlamaCppLLM` cannot be used as `llama_cpp` is not installed, please "
                " install it with `pip install llama-cpp-python`."
            )

        self.max_tokens = max_new_tokens
        self.temperature = temperature
        self.top_p = top_p
        self.top_k = top_k
        self.repeat_penalty = repeat_penalty

        self.model = model

    def __rich_repr__(self) -> Generator[Any, None, None]:
        yield from super().__rich_repr__()
        yield (
            "parameters",
            {
                "max_new_tokens": self.max_tokens,
                "temperature": self.temperature,
                "top_p": self.top_p,
                "top_k": self.top_k,
                "repeat_penalty": self.repeat_penalty,
            },
        )

    @property
    def model_name(self) -> str:
        """Returns the name of the llama-cpp model, which is the same as the model path."""
        return self.model.model_path

    def _generate(
        self, inputs: List[Dict[str, Any]], num_generations: int = 1
    ) -> List[List[LLMOutput]]:
        """Generates `num_generations` for each input in `inputs`.

        Args:
            inputs (List[Dict[str, Any]]): the inputs to be used for generation.
            num_generations (int, optional): the number of generations to be performed for each
                input. Defaults to 1.

        Returns:
            List[List[LLMOutput]]: the generated outputs.
        """
        prompts = self._generate_prompts(
            inputs, default_format=None, expected_output_type=str
        )
        outputs = []
        for prompt in prompts:
            output = []
            for _ in range(num_generations):
                raw_output = self.model.create_completion(
                    prompt,
                    max_tokens=self.max_tokens,
                    temperature=self.temperature,
                    top_p=self.top_p,
                    top_k=self.top_k,
                    repeat_penalty=self.repeat_penalty,
                )
                try:
                    parsed_output = self.task.parse_output(
                        raw_output["choices"][0]["text"].strip()
                    )
                except Exception as e:
                    logger.error(f"Error parsing llama-cpp output: {e}")
                    parsed_output = None
                output.append(
                    LLMOutput(
                        model_name=self.model_name,
                        prompt_used=prompt,
                        raw_output=raw_output,
                        parsed_output=parsed_output,
                    )
                )
            outputs.append(output)
        return outputs

model_name: str property

Returns the name of the llama-cpp model, which is the same as the model path.

__init__(model, task, max_new_tokens=128, temperature=0.8, top_p=0.95, top_k=40, repeat_penalty=1.1, prompt_format=None, prompt_formatting_fn=None)

Initializes the LlamaCppLLM class.

Parameters:

Name Type Description Default
model Llama

the llama-cpp model to be used.

 required
task Task

the task to be performed by the LLM.

 required
max_new_tokens int

the maximum number of tokens to be generated. Defaults to 128.

 128
temperature float

the temperature to be used for generation. Defaults to 0.8.

 0.8
top_p float

the top-p value to be used for generation. Defaults to 0.95.

 0.95
top_k int

the top-k value to be used for generation. Defaults to 40.

 40
repeat_penalty float

the repeat penalty to be used for generation. Defaults to 1.1.

 1.1
prompt_format Union[SupportedFormats, None]

the format to be used for the prompt. If None, the default format of the task will be used, available formats are openai, chatml, llama2, zephyr, and default. Defaults to None, but default (concatenation of system_prompt and formatted_prompt with a line-break) will be used if no prompt_formatting_fn is provided.

 None
prompt_formatting_fn Union[Callable[..., str], None]

a function to be applied to the prompt before generation. If None, no formatting will be applied. Defaults to None.

 None

Examples:

>>> from llama_cpp import Llama
>>> from distilabel.tasks.text_generation import TextGenerationTask as Task
>>> from distilabel.llm import LlamaCppLLM
>>> model = Llama(model_path="path/to/model")
>>> task = Task()
>>> llm = LlamaCppLLM(model=model, task=task)
Source code in src/distilabel/llm/llama_cpp.py 
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def __init__(
    self,
    model: "Llama",
    task: "Task",
    max_new_tokens: int = 128,
    temperature: float = 0.8,
    top_p: float = 0.95,
    top_k: int = 40,
    repeat_penalty: float = 1.1,
    prompt_format: Union[SupportedFormats, None] = None,
    prompt_formatting_fn: Union[Callable[..., str], None] = None,
) -> None:
    """Initializes the LlamaCppLLM class.

    Args:
        model (Llama): the llama-cpp model to be used.
        task (Task): the task to be performed by the LLM.
        max_new_tokens (int, optional): the maximum number of tokens to be generated.
            Defaults to 128.
        temperature (float, optional): the temperature to be used for generation.
            Defaults to 0.8.
        top_p (float, optional): the top-p value to be used for generation.
            Defaults to 0.95.
        top_k (int, optional): the top-k value to be used for generation.
            Defaults to 40.
        repeat_penalty (float, optional): the repeat penalty to be used for generation.
            Defaults to 1.1.
        prompt_format (Union[SupportedFormats, None], optional): the format to be used
            for the prompt. If `None`, the default format of the task will be used, available
            formats are `openai`, `chatml`, `llama2`, `zephyr`, and `default`. Defaults to `None`,
            but `default` (concatenation of `system_prompt` and `formatted_prompt` with a line-break)
            will be used if no `prompt_formatting_fn` is provided.
        prompt_formatting_fn (Union[Callable[..., str], None], optional): a function to be
            applied to the prompt before generation. If `None`, no formatting will be applied.
            Defaults to `None`.

    Examples:
        >>> from llama_cpp import Llama
        >>> from distilabel.tasks.text_generation import TextGenerationTask as Task
        >>> from distilabel.llm import LlamaCppLLM
        >>> model = Llama(model_path="path/to/model")
        >>> task = Task()
        >>> llm = LlamaCppLLM(model=model, task=task)
    """
    super().__init__(
        task=task,
        prompt_format=prompt_format,
        prompt_formatting_fn=prompt_formatting_fn,
    )

    if not _LLAMA_CPP_AVAILABLE:
        raise ImportError(
            "`LlamaCppLLM` cannot be used as `llama_cpp` is not installed, please "
            " install it with `pip install llama-cpp-python`."
        )

    self.max_tokens = max_new_tokens
    self.temperature = temperature
    self.top_p = top_p
    self.top_k = top_k
    self.repeat_penalty = repeat_penalty

    self.model = model