distflow 0.0.0

Distance Computation Package for Data Preparation Bench

Data-Preparation-Bench

A benchmark for evaluating the data preparation capabilities of large language models (LLMs). The benchmark is organized into two modules:

Modules

1. Data Synthesis & Augmentation

Given raw metadata, the model is tasked with synthesizing or augmenting datasets to improve downstream model training.

2. Data Quality Assessment

Given raw metadata, the model is tasked with predicting the training data's impact on downstream task performance.

Quick Start

Usage

The package is published on PyPI and can be installed via pip:

pip install distflow

For vLLM embedding support, install the optional dependency:

pip install distflow[vllm]

This project uses uv for dependency management. To get started:

git clone https://github.com/haolpku/Data-Preparation-Bench.git
cd Data-Preparation-Bench
uv sync

To use your own datasets, modify the configuration dictionaries and formatters in compute_mmd.py:

DS1_CONFIG = {
    "name": "oda-math",
    "data_path": "OpenDataArena/ODA-Math-460k",
    "data_size": 5000,
    "split": "train",
    "shuffle_seed": 42,
}
formatter1 = AlpacaFormatter(
    user_key="question",
    assistant_key="response",
)

DS2_CONFIG = {
    "name": "infinity-instruct",
    "data_path": "BAAI/Infinity-Instruct",
    "data_size": 5000,
    "split": "train",
    "shuffle_seed": 42,
}
formatter2 = ShareGptFormatter(
    conversations_key="conversations",
)

Typically, you only need to update data_path with your dataset and define a formatter that converts raw items to the required format. After making these changes, run the MMD computation with:

uv run examples/compute_mmd.py

Development

To set up the development environment locally:

uv sync --extra dev
uv run pre-commit install

Before committing, format and lint the code:

uv run pre-commit run --all-files

Experiment Settings

Please refer to Experiment.md for detailed experiment configurations.