没有标记数据集，如何做大模型指令微调？介绍一款有潜力的标记数据集生成模型

class Bonito(LLM, AbstractBonito):
    def generate_tasks(
        self,
        text_dataset: Dataset,
        context_col: str,
        task_type: str,
        sampling_params: SamplingParams,
        **kwargs,
    ):
        """
        Generates tasks using the Bonito model.


        This method takes a text dataset, a context column name,
        a task type, and sampling parameters, and generates tasks
        using the Bonito model. It processes the input dataset,
        generates outputs, collects multiple generations into
        one dataset object, and filters out the examples that
        cannot be parsed.


        Args:
            text_dataset (Dataset): The dataset that provides the text
                for the tasks.
            context_col (str): The name of the column in the dataset
                that provides the context for the tasks.
            task_type (str): The type of the tasks. This can be a
                short form or a full form.
            sampling_params (SamplingParams): The parameters for
                sampling.
            **kwargs: Additional keyword arguments.


        Returns:
            Dataset: The synthetic dataset with the generated tasks.
        """
        processed_dataset = self._prepare_bonito_input(
            text_dataset, task_type, context_col, **kwargs
        )
        outputs = self.generate(processed_dataset["input"], sampling_params)


        # collect multiple generations into one dataset object
        examples = []
        for i, example in enumerate(text_dataset.to_list()):
            for output in outputs[i].outputs:
                examples.append(
                    {"context": example[context_col], "prediction": output.text.strip()}
                )


        synthetic_dataset = Dataset.from_list(examples)


        # filter out the examples that cannot be parsed
        synthetic_dataset = self._postprocess_dataset(
            synthetic_dataset, context_col="context", **kwargs
        )


        return synthetic_dataset

from bonito import Bonito
from vllm import SamplingParams
from datasets import load_dataset


# Initialize the Bonito model
bonito = Bonito("BatsResearch/bonito-v1")


# load dataset with unannotated text
unannotated_text = load_dataset(
    "BatsResearch/bonito-experiment",
    "unannotated_contract_nli"
)["train"].select(range(10))


# Generate synthetic instruction tuning dataset
sampling_params = SamplingParams(max_tokens=256, top_p=0.95, temperature=0.5, n=1)
synthetic_dataset = bonito.generate_tasks(
    unannotated_text,
    context_col="input",
    task_type="nli",
    sampling_params=sampling_params
)

from typing import Optional, List, Dict
from datasets import Dataset
from awq import AutoAWQForCausalLM
from bonito import AbstractBonito
from transformers import AutoTokenizer




class QuantizedBonito(AbstractBonito):
    def __init__(self, model_name_or_path):
        self.model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True).cuda()
        self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)


    def generate_task(
        self,
        unannotated_paragraph: str,
        task_type: str,
        sampling_params: dict,
    ) -> Dict:
        """
        Generates synthetic instruction tuning pair using the Quantized Bonito model.
        This method takes a text unannotated text, a task type, and sampling parameters,
        and generates synthetic input-output pair.


        Args:
            unannotated_paragraph (str): The unannotated text or a paragraph
            task_type (str): The type of the tasks. This can be a
                short form or a full form.
            sampling_params (dict): The parameters for
                sampling.
            **kwargs: Additional keyword arguments.


        Returns:
            Dict: The synthetic input-output pair for the task type.
        """


        text_dataset = Dataset.from_list([{"input": unannotated_paragraph}])


        processed_dataset = self._prepare_bonito_input(
            text_dataset, task_type, context_col="input"
        )


        outputs = self._generate_text(processed_dataset["input"], sampling_params)
        examples = []
        for i, example in enumerate(text_dataset.to_list()):
            output = outputs[i]
            example["prediction"] = output.strip()
            examples.append(example)


        synthetic_dataset = Dataset.from_list(examples)


        # filter out the examples that cannot be parsed
        synthetic_dataset_dict = self._postprocess_dataset(
            synthetic_dataset, context_col="input"
        ).to_list()[0]


        return synthetic_dataset_dict


    def _generate_text(
        self,
        dataset: Dataset,
        sampling_params: dict,
        ) -> List[str]:
        """
        Generate text using huggingface transformers generate function.


        This method takes a dataset of prompts, encodes them,
        generates text using the model, decodes the generated
        text, and appends it to a list.


        Args:
            dataset (Dataset): A dataset containing prompts for text generation.
            sampling_params (dict): Parameters for sampling during generation.


        Returns:
            List[str]: A list of generated texts corresponding to the prompts.
        """
        generated_texts = []


        for prompt in dataset:
            input_ids = self.tokenizer.encode(prompt, return_tensors="pt")
            input_ids = input_ids.cuda()


            output = self.model.generate(
                input_ids,
                do_sample=True,
                **sampling_params
            )


            generated_text = self.tokenizer.decode(output[0][len(input_ids[0]):], skip_special_tokens=True)
            generated_texts.append(generated_text)


        return generated_texts

sampling_params = {'max_new_tokens':256, 'top_p':0.95, 'temperature':0.7, 'num_return_sequences':1}
synthetic_dataset = bonito.generate_task(
    unannotated_paragraph,
    task_type="ynqa",
    sampling_params=sampling_params
)
pprint("----Generated Instructions----")
pprint(f'Input: {synthetic_dataset["input"]}')
pprint(f'Output: {synthetic_dataset["output"]}')


'----Generated Instructions----'
('Input: Based on the following passage, is a written communication '
 'confidential? 1. “Confidential Information”, whenever used in this '
 'Agreement, shall mean any data, document, specification and other '
 'information or material, that is delivered or disclosed by UNHCR to the '
 'Recipient in any form whatsoever, whether orally, visually in writing or '
 'otherwise (including computerized form), and that, at the time of disclosure '
 'to the Recipient, is designated as confidential.')
'Output: Yes'