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kitsdk/
bonito-chinese-v1

Name: bonito-chinese-v1
Availability: InStock

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qwen2

kitsdk/ctga-v1-1w-chinese

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Bonito（支持中文版本）

Bonito is an open-source model for conditional task generation: the task of converting unannotated text into task-specific training datasets for instruction tuning. This repo is a lightweight library for Bonito to easily create synthetic datasets built on top of the Hugging Face transformers and vllm libraries.

Paper: Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation
Model: bonito-v1(English)
Model: bonito-chinese-v1(中文)
Demo: Bonito on Spaces (English)
Demo: Google Colab(正文)
Dataset: ctga-v1
Code: To reproduce experiments in our paper, see nayak-aclfindings24-code.

This version supports the Chinese language

Because of the training data limitations, this version supports only the 3 task types

🐠 1.question generation.
🐡 2.multiple-choice question answering.
🐟 3.question answering without choices.

Google Colab: Demo

Basic Usage

To generate synthetic instruction tuning dataset using Bonito, you can use the following code:
pip3 install bonito-llm

python

from pprint import pprint
from datasets import Dataset
from vllm import SamplingParams
from transformers import set_seed
from bonito import Bonito

unannotated_paragraph = """灌区以往的闸门控制系统在实际应用过程中普遍以人工操作为主，容易受到多种因素的影响，不可避免出现较多缺陷。如操作人员自身的综合能力、业务水平、工作态度等对工作质量和效率产生较大影响；工作人员实践操作中遇到极端气候、工作环境恶劣等问题，大大增加了工作难度，并存在较多安全隐患。"""
pprint(unannotated_paragraph)
bonito = Bonito("kitsdk/bonito-chinese-v1")
set_seed(2)

def convert_to_dataset(text):
  dataset = Dataset.from_list([{"input": text}])
  return dataset

sampling_params = SamplingParams(max_tokens=256, top_p=0.95, temperature=0.5, n=1)
synthetic_dataset = bonito.generate_tasks(
    convert_to_dataset(unannotated_paragraph),
    context_col="input",
    task_type="mcqa",
    sampling_params=sampling_params
)
pprint("----Generated Instructions----")
pprint(f'Input: {synthetic_dataset[0]["input"]}')
pprint(f'Output: {synthetic_dataset[0]["output"]}')