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AutocompleteRunner

The AutocompleteRunner class is designed for running and training a Llama V2 model, using system and user prompts.

Methods Reference

__call__(self, inputs: Union[str, List[str]]) -> Union[str, List[str]]

Get the output to a single input or list of inputs (batched).

Args:

  • input (str): The input to the model.

Returns:

  • output (str): The output of the model.

evaluate_autocomplete(self, data: Union[str, List[str]]) -> Union[str, List[str]](self, data)

Return dictionary of paired prompts, targets, and predictions

Args:

  • data (List[dict]): A list of dictionaries representing input-output pairs.

load_data(self, data)

Load a list of dictionaries with input-output pairs into the model. Each dictionary must have "input" and "output" as keys.

Args:

  • data (List[dict]): A list of dictionaries representing input-output pairs.

load_data_from_jsonlines(self, file_path: str)

Load a jsonlines file with input-output pairs into the model. Each line in the file must be a JSON object with "input" and "output" as keys.

Args:

  • file_path (str): The path to the jsonlines file.

load_data_from_dataframe(self, df: pd.DataFrame)

Load a pandas DataFrame with input-output pairs into the model. Each row must have "input" and "output" as keys.

Args:

  • df (pd.DataFrame): The pandas DataFrame containing the input-output pairs.

load_data_from_csv(self, file_path: str)

Load a CSV file with input-output pairs into the model. Each row must have "input" and "output" as keys.

Args:

  • file_path (str): The path to the CSV file.

clear_data(self)

Clear the data from the model, including loaded documents and input-output pairs.

train(self, verbose: bool = False)

Train the model on the loaded data. This function blocks until training is complete.

Args:

  • verbose (bool): Whether to print verbose training progress. Default is False.
  • (Optional) finetune_args (dict): key=hyper-parameter name, value=parameter value. Same as huggingface's training arguments

evaluate(self) -> dict

Get the evaluation results of the trained model.

Returns:

  • evaluation (List): A dict of evaluation results.

Also, sets the self.evaluation attribute to the evaluation results.

Please note that this documentation assumes the presence of relevant imports (e.g., List, str, pd) and required external dependencies like the LLMEngine class and other libraries.