# DataFlow **Repository Path**: xmagictech/DataFlow ## Basic Information - **Project Name**: DataFlow - **Description**: 基于大模型算子和工作流的高效文本大模型训练数据合成框架 - **Primary Language**: Python - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: https://opendcai.github.io/DataFlow-Doc/zh/ - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 1 - **Created**: 2026-04-13 - **Last Updated**: 2026-04-13 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # DataFlow
**Generate, Clean, and Prepare LLM Data, All-in-One** [![](https://img.shields.io/github/stars/OpenDCAI/DataFlow?style=social)](https://github.com/OpenDCAI/DataFlow) [![](https://img.shields.io/github/issues-raw/OpenDCAI/DataFlow)](https://github.com/OpenDCAI/DataFlow/issues) [![issue resolution](https://img.shields.io/github/issues-closed-raw/opendcai/DataFlow)](https://github.com/OpenDCAI/DataFlow/issues?q=is%3Aissue%20state%3Aclosed) [![](https://img.shields.io/github/issues-pr-raw/OpenDCAI/DataFlow)](https://github.com/OpenDCAI/DataFlow/pulls) [![issue resolution](https://img.shields.io/github/issues-pr-closed-raw/opendcai/DataFlow)](https://github.com/OpenDCAI/DataFlow/pulls?q=is%3Apr+is%3Aclosed) [![](https://img.shields.io/github/contributors/OpenDCAI/DataFlow)](https://github.com/OpenDCAI/DataFlow/graphs/contributors) [![](https://img.shields.io/github/repo-size/OpenDCAI/DataFlow?color=green)](https://github.com/OpenDCAI/DataFlow) [![PyPI version](https://img.shields.io/pypi/v/open-dataflow)](https://pypi.org/project/open-dataflow/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/open-dataflow)](https://pypi.org/project/open-dataflow/) [![PyPI - Downloads](https://img.shields.io/pypi/dm/open-dataflow?style=flat&logo=python)](https://pypistats.org/packages/open-dataflow) [![Downloads](https://static.pepy.tech/badge/open-dataflow)](https://pepy.tech/project/open-dataflow) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1haosl2QS4N4HM7u7HvSsz_MnLabxexXl?usp=sharing) [![Docker](https://img.shields.io/badge/Docker-Ready-blue?logo=docker)](https://hub.docker.com/r/molyheci/dataflow) [![Documents](https://img.shields.io/badge/Documentation-Click_here-brightgreen?logo=read-the-docs)](https://OpenDCAI.github.io/DataFlow-Doc/) [![Arxiv](https://img.shields.io/badge/Technical_Report-2512.16676-b31b1b.svg?logo=arxiv)](https://arxiv.org/abs/2512.16676) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/OpenDCAI/DataFlow) [![Discord Online](https://img.shields.io/discord/1479323317096939551?logo=discord&label=discord&color=%235966F0)](https://discord.gg/e4mKEaFptu) [![wechat](https://img.shields.io/badge/wechat-brightgreen?logo=wechat&logoColor=white)](https://github.com/user-attachments/assets/3c2e5d4d-d1ea-4d8c-9146-ff14e657e857) OpenDCAI%2FDataFlow | Trendshift Visual, low-code pipelines with flexible orchestration across domains and use cases.💪 Turn raw data into high-quality LLM training datasets.🔧 🎉 Get smarter LLMs cheaply — give us a star ⭐ on GitHub for the latest update. **Beginner-friendly learning resources (continuously updated)**: [[🎬 Video Tutorials]](https://space.bilibili.com/3546929239689711?spm_id_from=333.337.0.0) [[📚 Written Tutorials]](https://wcny4qa9krto.feishu.cn/wiki/I9tbw2qnBi0lEakmmAGclTysnFd) [简体中文](./README-zh.md) | English
## 📰 0. News * **[2026-02-02] 🖥️ DataFlow WebUI is now available!** Launch the visual pipeline builder with a single command: `dataflow webui`. Build and run DataFlow pipelines through an intuitive web interface. 👉 [WebUI Docs](#dfwebui)
* **[2026-01-20] 🌟 Awesome Works Using DataFlow is now live!** A new section showcasing open-source projects and research built on DataFlow. Contributions are welcome! 👉 [Awesome Works](#awesome-dataflow) * **[2025-12-19] 🎉 Our DataFlow technical report is now available!** Read and cite our work on arXiv: [https://arxiv.org/abs/2512.16676](https://arxiv.org/abs/2512.16676) * **[2025-11-20] 🤖 Introducing New Data Agents for DataFlow!** Try them out and follow the tutorial on Bilibili: [https://space.bilibili.com/3546929239689711/lists/6761342?type=season](https://space.bilibili.com/3546929239689711/lists/6761342?type=season) * **[2025-06-28] 🎉 DataFlow is officially released!** Our data-centric AI system is now public. Stay tuned for future updates. ## 🔍 1. What is DataFlow？ DataFlow is a data preparation and training system designed to **generate, refine, evaluate, and filter** high-quality data for AI from noisy sources (PDF, plain-text, low-quality QA), thereby improving the performance of large language models (LLMs) in specific domains through targeted training (Pre-training, Supervised Fine-tuning, RL training) or RAG system, in domains such as healthcare, finance, legal, and academic research. Through an `operator-based` design, DataFlow turns the entire data cleaning workflow into a reproducible, reusable, and shareable `pipeline`, providing core infrastructure for the Data-Centric AI community. Additionally, we develop an intelligent `DataFlow-agent` capable of dynamically assembling new `pipelines` by recombining existing or creating new `operators` on demand. ![df_overview_final_300](https://github.com/user-attachments/assets/57dd0838-6e24-4814-a89a-02ca0667bd5c) ## 🔍 2. Key Features ### ✅2.1 Ready-to-Use Data Synthesis and Cleaning Pipelines - High-Quality Training Data Generation - Text, Math, and Code data generation (see DataFlow-Instruct-10K for results) - Data generation via tools like AgenticRAG and Text2SQL - Structured Data Extraction - Large-scale PDF → QA conversion - Large-scale book PDF → Visual-QA conversion - Scientific Data Workflow Management - Text2SQL workflow management (Accepted by ICDE 2026) - Math data workflows (Accepted by KDD 2026) ### ⚙️2.2 Flexible Custom Pipeline Orchestration - 10+ core operators define interaction patterns and design principles - 100+ pipeline-specific operators available for reuse or reference - Full support for creating custom operators — plug-and-play, easily packaged and distributed via GitHub or PyPI ### 🧠2.3 Reproducible, Reusable, and Shareable Data-Centric AI System - Data governance algorithms are encapsulated as operator pipelines, enabling reproducibility and fair comparison of different data governance strategies (❤️research-friendly) - Easily reuse swap underlying large models to analyze the relationship between model performance and data quality quickly - Built on Python and Git ecosystems for easy distribution, management, and traceability of high-quality, **user-defined** data governance operators and pipelines (❤️enterprise-friendly) ## 🛠️ 3. DataFlow Suite The DataFlow Suite provides the essential infrastructure to automate and scale LLM data preparation with DataFlow main repository. It comprises four tightly integrated layers: - [DataFlow-WebUI](#dfwebui) – An intuitive, visual interface for constructing and managing complex data pipelines through a drag-and-drop operator workflow. - [DataFlow-Agent](https://github.com/OpenDCAI/DataFlow-Agent) – An AI-powered assistant that dynamically composes, executes, and optimizes operators and pipelines based on high-level user intent. - [DataFlow-Ecosystem](#awesome-dataflow) – A modular distribution layer that standardizes operator registration. It enables domain-specific modules (e.g., [DataFlow-MM](https://github.com/OpenDCAI/DataFlow-MM), DataFlow-AI4S) to contribute extensible libraries under a unified abstraction. - [RayOrch](https://github.com/OpenDCAI/RayOrch) – A high-performance orchestration layer built on Ray, providing distributed compute scheduling and resource management for massive-scale data tasks. Together, these components form a unified, extensible environment that transforms raw data into model-ready intelligence. ## ✅ 4. Why use DataFlow? Data generation and cleaning are crucial for high-quality models, but for both enterprises and individuals, these tasks are often time-consuming, labor-intensive, and costly. **DataFlow provides a one-stop solution to tackle these challenges efficiently.** Compared with systems like Nemo-Curator and Data-Juicer, DataFlow offers: - **Enhanced Support for Data Synthesis Modules** – Seamlessly integrates text, code, and math data generation pipeline for high-quality training datasets. - **PyTorch-like Programming Management** – Clear **Pipeline → Operator → Prompt** hierarchical structure for workflow control. - **Principled and Multi-Category Operator Classification** – Operators are systematically organized into multiple functional categories such as **generation, evaluation, filtering, and refinement**, forming a scientifically grounded, multi-dimensional taxonomy that reflects different stages of data preparation and enables precise operator selection and composition. - **User-Friendly Design for Easy Debugging and Onboarding** – Simplified workflow patterns that reduce the learning curve and accelerate experimentation. ## 🔧 5. How do operators work？ DataFlow operators are designed with **simplicity and clarity** in mind. Operators take structured inputs (JSON, JSONL, CSV) and produce high-quality outputs after intelligent processing. Each operator encapsulates a specific data processing task, providing a clean and consistent API that is easy to understand and integrate. The PyTorch-like design makes them intuitive and ready to use, allowing you to quickly build, combine, and customize pipelines without dealing with complex boilerplate code. For more details, refer to the [Operator Documentation](https://opendcai.github.io/DataFlow-Doc/zh/api/home/). Below is a minimal example demonstrating how to invoke the `PromptedGenerator` operator: ![dataflow_operator](https://github.com/user-attachments/assets/d79a0d8b-09ef-457e-af8b-85af0d03b73d) Example input data (json/jsonl-style): ```json // input.json [ {"problem": "What is 17 + 25?"}, {"problem": "If x = 3, compute 2x^2 + 1."} ] ``` Operator invocation code: ```python from dataflow.operators.core_text import PromptedGenerator from dataflow.utils.storage import FileStorage from dataflow.serving import APILLMServing_request # set input file to global storage class storage = FileStorage(first_entry_file_name="./input.json",) # configure LLM serving (e.g., OpenAI API) # api key needs to be set via `export DF_API_KEY=sk-xxx` llm_serving = APILLMServing_request( api_url="https://api.openai.com/v1/chat/completions", ) prompted_generator = PromptedGenerator( llm_serving=llm_serving, # pre-configured LLM backend system_prompt="Please solve this math problem." ) prompted_generator.run( storage=self.storage.step(), # data management (details omitted) input_key="problem", # read from this column output_key="solution" # write to this column ) ``` After running, the operator will append the generated results into output_key. For example, the output data (json/jsonl-style) becomes: ```json // dataflow_step1.json [ {"problem":"What is 17 + 25?","solution":"42"}, {"problem":"If x = 3, compute 2x^2 + 1.","solution":"19"} ] ```

🛠️ 6. Pipelines (Click to expand)

 ### 🔧 6.1 Ready-to-Use PipeLines Current Pipelines in Dataflow are as follows: - [📝 **Text Pipeline**](https://opendcai.github.io/DataFlow-Doc/en/guide/textpipeline): Mine question-answer pairs from large-scale plain-text data (mostly crawed from InterNet) for use in SFT and RL training. - ![dataflow_text_pipeline](https://github.com/user-attachments/assets/34e3aef2-ba4f-4997-9127-9d21fdb2dede) - [[HuggingFace🤗 demo input & output for **Text Pipeline**]](https://huggingface.co/datasets/Open-Dataflow/dataflow-demo-Text) - [🧠 **Reasoning Pipeline**](https://opendcai.github.io/DataFlow-Doc/en/guide/reasoningpipeline/#_2-question-handling): Enhances existing question–answer pairs with (1) extended chain-of-thought, (2) category classification, and (3) difficulty estimation. - ![dataflow_reasoning_pipeline](https://github.com/user-attachments/assets/fef5829b-3991-4dcb-99ad-d61d95c982ea) - [[HuggingFace🤗 demo input & output for **Reasoning Pipeline**]](https://huggingface.co/datasets/Open-Dataflow/dataflow-demo-Reasonning) - [🗃️ **Text2SQL Pipeline**](https://opendcai.github.io/DataFlow-Doc/en/guide/text2sqlpipeline/): Translates natural language questions into SQL queries, supplemented with explanations, chain-of-thought reasoning, and contextual schema information. - ![dataflow_text2sql_pipeline](https://github.com/user-attachments/assets/bae9914e-851b-4502-8696-291d6c1b8824) - [[HuggingFace🤗 demo input & output for **Text2SQL Pipeline**]](https://huggingface.co/datasets/Open-Dataflow/dataflow-demo-Text2SQL) - [📚 **Knowlege Base Cleaning Pipeline**](https://opendcai.github.io/DataFlow-Doc/en/guide/r51ooua8/): Extract and structure knowledge from unorganized sources like tables, PDFs, and Word documents into usable entries for downstream RAG or QA pair generation. - ![dataflow_KnowledgeBaseClean_pipeline](https://github.com/user-attachments/assets/6f21e682-ec10-42af-b5e2-8fec2929eeae) - [🤖 **Agentic RAG Pipeline**](https://opendcai.github.io/DataFlow-Doc/en/guide/agenticrag_pipeline/): Identify and extract QA pairs from existing QA datasets or knowledge bases that require external knowledge to answer, for use in downstream training of Agnetic RAG tasks. - ![dataflow_agenticRAG_pipeline](https://github.com/user-attachments/assets/65e80dca-f286-495b-abb7-804b3fc34a53) ### ⚙️ 6.2 Flexible Operator PipeLines In this framework, operators are categorized into Fundamental Operators, Generic Operators, Domain-Specific Operators, and Evaluation Operators, etc., supporting data processing and evaluation functionalities. Please refer to the [documentation](https://OpenDCAI.github.io/DataFlow-Doc/) for details. ### 🤖 6.3 Agent Guided Pipelines - **DataFlow Agent**: An intelligent assistant that performs data analysis, writes custom `operators`, and automatically orchestrates them into `pipelines` based on specific task objectives. - ![dataflow_agent_pipeline](https://github.com/user-attachments/assets/fe0776fa-55bd-49cd-bfe6-06ad377f62bb) - [[HuggingFace🤗 demo input & output for **DataFlow Agent**]](https://huggingface.co/datasets/Open-Dataflow/dataflow-demo-Agent)
## ⚡ 7. Quick Start ### 🛠️ 7.1 Environment Setup and Installation > DataFlow supports Python>=3.10 environments, tested passed on Windows, Linux, and MacOS with Python 3.10, 3.11, and 3.12. Please use the following commands for environment setup and installation👇 We recommend use [uv](https://docs.astral.sh/uv/) to install DataFlow for speed up. ```shell pip install uv uv pip install open-dataflow ``` If you want to use your own GPU for local inference, please use: ```shell pip install uv uv pip install open-dataflow[vllm] ``` After installation, you can use the following command to check if dataflow has been installed correctly: ```shell dataflow -v ``` If installed correctly, you should see: ```log open-dataflow codebase version: 1.0.0 Checking for updates... Local version: 1.0.0 PyPI newest version: 1.0.0 You are using the latest version: 1.0.0. ``` #### 🐳 7.2 Docker Installation (Alternative) We also provide a **Dockerfile** for easy deployment and a **pre-built Docker image** for immediate use. ##### Option 1: Use Pre-built Docker Image You can directly pull and use our pre-built Docker image: ```shell # Pull the pre-built image docker pull molyheci/dataflow:cu124 # Run the container with GPU support docker run --gpus all -it molyheci/dataflow:cu124 # Inside the container, verify installation dataflow -v ``` ##### Option 2: Build from Dockerfile Alternatively, you can build the Docker image from the provided Dockerfile: ```shell # Clone the repository (HTTPS) git clone https://github.com/OpenDCAI/DataFlow.git # Or use SSH # git clone git@github.com:OpenDCAI/DataFlow.git cd DataFlow # Build the Docker image docker build -t dataflow:custom . # Run the container docker run --gpus all -it dataflow:custom # Inside the container, verify installation dataflow -v ``` > **Note**: The Docker image includes CUDA 12.4.1 support and comes with vLLM pre-installed for GPU acceleration. Make sure you have [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) installed to use GPU features. ### 🚀 7.3 Quick Start with Google Colab You can start your first DataFlow translation project directly on Google Colab. By following the provided guidelines, you can seamlessly scale from a simple translation example to more complex DataFlow pipelines. 👉 [Start DataFlow with Google Colab](https://colab.research.google.com/drive/1haosl2QS4N4HM7u7HvSsz_MnLabxexXl?usp=sharing) ### 📖 7.4 Reference Project Documentation For detailed **usage instructions** and **getting started guide**, please visit our [DataFlow Documentation](https://OpenDCAI.github.io/DataFlow-Doc/). [![Documents](https://img.shields.io/badge/Documentation-Click_here-brightgreen?logo=read-the-docs)](https://OpenDCAI.github.io/DataFlow-Doc/) ### 🖥️ 7.5 DataFlow-WebUI DataFlow provides a **Web-based UI (WebUI)** for visual pipeline construction and execution.
To start `DataFlow-WebUI`, simply run following command after install the DataFlow main repository: ```bash dataflow webui ``` This will automatically download and launch the latest **DataFlow-WebUI** and open it in your browser (`http://localhost:8000/` if it does not open automatically). #### 📚 7.5.1 WebUI Documentation * Chinese: [DataFlow-WebUI Documentation: https://wcny4qa9krto.feishu.cn/wiki/F4PDw76uDiOG42k76gGc6FaBnod](https://wcny4qa9krto.feishu.cn/wiki/F4PDw76uDiOG42k76gGc6FaBnod) * English: [DataFlow-WebUI Documentation: https://wcny4qa9krto.feishu.cn/wiki/SYELwZhh9ixcNwkNRnhcLGmWnEg](https://wcny4qa9krto.feishu.cn/wiki/SYELwZhh9ixcNwkNRnhcLGmWnEg) #### 🛠️ 7.5.2 Development Repository * [https://github.com/OpenDCAI/DataFlow-webui](https://github.com/OpenDCAI/DataFlow-webui) ## 🧪 8. Experimental Results ### 8.1 DataFlow-Instruct-10k **DataFlow-Instruct-10K** is a unified multi-domain instruction dataset generated by the DataFlow framework. It is constructed through several automated data preparation pipelines spanning mathematical reasoning, code, and general text instructions. Each pipeline follows a generate–evaluate–filter–refine workflow to synthesize and curate high-quality instruction–response pairs. The resulting dataset contains approximately 10K samples and provides high-quality supervision for instruction tuning, enabling base models to approach the performance of fully trained instruct models with significantly fewer training examples. For Detailed Experiments setting, please visit our [DataFlow Technical Report](https://arxiv.org/abs/2512.16676). | Model | Math-Avg | Code-Avg | Knowledge-Avg | |------|------|------|------| | **Qwen2-7B Series** |||| | Base | 20.1 | 66.3 | 76.2 | | + Infinity-Instruct-10K | 29.0 | 67.8 | 76.2 | | + Infinity-Instruct-1M | 27.9 | **68.2** | **76.2** | | + **DataFlow-Instruct-10K** | **32.4** | 66.2 | 76.1 | | **Qwen2.5-7B Series** |||| | Base | 37.1 | 76.5 | 76.0 | | + Infinity-Instruct-10K | 22.6 | 77.6 | 75.8 | | + Infinity-Instruct-1M | 33.3 | 78.0 | 75.8 | | + **DataFlow-Instruct-10K** | **46.7** | **78.6** | **76.2** |

🛠️ 8.2 Other Pipeline Results (Click to expand)

 #### 8.2.1 Text Pipeline ##### 8.2.1.1 Pre-training data filter pipeline From the SlimPajama-627B corpus, we extract a 100B-token subset and apply multiple DataFlow text-pretraining filters. We train a Qwen2.5-0.5B model from scratch for 30B tokens using the Megatron-DeepSpeed framework, the results are as follows: | Methods | ARC-C | ARC-E | MMLU | HellaSwag | WinoGrande | Gaokao-MathQA | Avg | | --- | --- | --- | --- | --- | --- | --- | --- | | **Random-30B** | 25.26 | 43.94 | 27.03 | 37.02 | 50.99 | 27.35 | 35.26 | | **Qurating-30B** | 25.00 | 43.14 | 27.50 | 37.03 | 50.67 | 26.78 | 35.02 | | **FineWeb-Edu-30B** | 26.45 | 45.41 | 27.41 | 38.06 | 50.43 | 25.64 | 35.57 | | **DataFlow-30B** | 25.51 | 45.58 | 27.42 | 37.58 | 50.67 | 27.35 | **35.69** | ##### 8.2.1.2 SFT data filter and synthesis pipeline To study small-scale SFT data quality, we fine-tune the Qwen2.5-7B base model using LLaMA-Factory on WizardLM and Alpaca datasets. For each dataset, we compared a randomly sampled set of 5K instances against a set of 5K instances filtered by DataFlow's SFT pipeline. Additionally, we synthesize a 15k-size dataset, DataFlow-SFT-15K, using DataFlow’s Condor Generator and Condor Refiner pipeline, followed by DataFlow’s SFT filtering pipeline (excluding the Instagram filter). Benchmarks include comprehensive Math, Code, and Knowledge evaluation suites. #### 8.2.2 Math Benchmarks | Methods | math | gsm8k | aime24 | minerva | olympiad | Avg | | --- | --- | --- | --- | --- | --- | --- | | **Alpaca (random)** | 54.9 | 77.2 | 13.3 | 14.0 | 27.0 | 37.3 | | **Alpaca (filtered)** | 60.3 | 80.0 | 13.3 | 14.7 | 30.7 | 39.8 | | **WizardLM (random)** | 61.1 | 84.2 | 6.7 | 18.0 | 29.3 | 39.9 | | **WizardLM (filtered)** | 69.7 | 88.8 | 10.0 | 19.9 | 35.4 | 44.8 | | **DataFlow-SFT-15K (random)** | 72.6 | 89.6 | 13.3 | 37.9 | 32.9 | **49.3** | | **DataFlow-SFT-15K (filtered)** | 73.3 | 90.2 | 13.3 | 36.0 | 35.9 | **49.7** | #### 8.2.3 Code Benchmarks | Methods | HumanEval | MBPP | Avg | | --- | --- | --- | --- | | **Alpaca (random)** | 71.3 | 75.9 | 73.6 | | **Alpaca (filtered)** | 73.8 | 75.7 | 74.8 | | **WizardLM (random)** | 75.6 | 82.0 | **78.8** | | **WizardLM (filtered)** | 77.4 | 80.4 | **78.9** | | **DataFlow-SFT-15K (random)** | 79.9 | 75.9 | 77.9 | | **DataFlow-SFT-15K (filtered)** | 82.9 | 74.9 | **78.9** | #### 8.2.4 Knowledge Benchmarks | Methods | MMLU | C-EVAL | Avg | | --- | --- | --- | --- | | **Alpaca (random)** | 71.8 | 80.0 | 75.9 | | **Alpaca (filtered)** | 71.8 | 80.0 | 75.9 | | **WizardLM (random)** | 71.8 | 79.2 | 75.5 | | **WizardLM (filtered)** | 71.9 | 79.6 | 75.8 | | **DataFlow-SFT-15K (random)** | 72.1 | 80.0 | **76.1** | | **DataFlow-SFT-15K (filtered)** | 72.2 | 80.4 | **76.3** | #### 8.2.5 Conversation Synthesis Pipeline We synthesize DataFlow-Chat-15K using DataFlow's conversation-generation pipeline and fine-tune Qwen2.5-7B-Base on it. Baselines include ShareGPT-15K, UltraChat-15K, and their full (non-truncated) versions. We evaluate on domain-specific tasks (TopDial, Light) and general benchmarks (MMLU, AlpacaEval, Arena-Hard). ##### 8.2.5.1 Conversation Benchmarks | Model | TopDial | Light | Avg | | --- | --- | --- | --- | | **Qwen2.5-7B** | 7.71 | 7.79 | 7.75 | | **+ ShareGPT-15K** | 7.75 | 6.72 | 7.24 | | **+ UltraChat-15K** | 7.72 | 6.83 | 7.28 | | **+ DataFlow-Chat-15K** | **7.98** | **8.10** | **8.04** | ##### 8.2.5.2 General Benchmarks | Model | MMLU | AlpacaEval | Arena-Hard | Avg | | --- | --- | --- | --- | --- | | **Qwen2.5-7B** | 71.45 | 7.05 | 0.60 | 26.36 | | **+ ShareGPT-15K** | 73.09 | 3.70 | 1.30 | 26.03 | | **+ UltraChat-15K** | 72.97 | 3.97 | 0.80 | 25.91 | | **+ DataFlow-Chat-15K** | 73.41 | **10.11** | 1.10 | **28.21** | #### 8.2.6 Reasoning Pipeline We adopt the NuminaMath dataset as a high-quality seed dataset. We compare three training sources: (1) a random 10K subset from Open-R1, (2) a random 10K subset from Synthetic-1, and (3) our 10K synthesized DataFlow-Reasoning-10K dataset constructed using DataFlow. | Setting | Model | gsm8k | math | amc23 | olympiad | gaokao24_mix | minerva | AIME24@32 | AIME25@32 | Avg | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Baseline | **Qwen2.5-32B-Instruct** | 95.8 | 73.5 | 70.0 | 38.5 | 42.9 | 26.5 | 16.8 | 11.6 | 46.95 | | 1 Epoch | **+ SYNTHETIC-1-10k** | 92.9 | 71.8 | 52.5 | 38.4 | 23.1 | 24.3 | 35.6 | 34.0 | 46.6 | | 1 Epoch | **+ Open-R1-10k** | 91.5 | 72.3 | 65.0 | 38.4 | 20.9 | 24.6 | 43.0 | 33.5 | 48.7 | | 1 Epoch | **+ DataFlow-Reasoning-10K** | 93.9 | 72.3 | 72.5 | 38.7 | 38.5 | 26.5 | 35.9 | 34.5 | **51.6** | | 2 Epochs | **+ SYNTHETIC-1-10k** | 94.5 | 78.4 | 75.0 | 45.0 | 24.2 | 28.3 | 48.4 | 37.9 | 54.0 | | 2 Epochs | **+ Open-R1-10k** | 93.9 | 77.2 | 80.0 | 44.1 | 20.9 | 25.4 | 51.0 | 40.7 | 54.2 | | 2 Epochs | **+ DataFlow-Reasoning-10K** | 94.4 | 76.6 | 75.0 | 45.2 | 42.9 | 25.7 | 45.4 | 40.0 | **55.7** | #### 8.2.7 Code Pipeline We randomly sample 20k instances from the Ling-Coder-SFT corpus and process them through the DataFlow Code Pipeline. This yields three curated code instruction datasets of different scales, DataFlow-Code-1K, DataFlow-Code-5K, and DataFlow-Code-10K, each designed to provide high-quality, pipeline-refined supervision signals for code generation tasks. We compare our synthesized datasets against Code-Alpaca-1k and Self-OSS-Instruct-SC2-Exec-Filter-1k. ##### 8.2.7.1 Trained on Qwen2.5-7B-Instruct | Training Data | BigCodeBench | LiveCodeBench (v6) | CruxEval (I) | CruxEval (O) | HumanEval+ | Avg | | --- | --- | --- | --- | --- | --- | --- | | **Qwen2.5-7B-Instruct** | 35.3 | 23.4 | 44.8 | 43.9 | 72.6 | 44.0 | | **+ Code Alpaca-1K** | 33.3 | 18.7 | 45.6 | 46.4 | 66.5 | 42.1 | | **+ Self-OSS** | 31.9 | 21.4 | 46.9 | 45.9 | 70.1 | 43.2 | | **+ DataFlow-Code-1K** | 35.5 | 25.7 | 48.0 | 45.1 | 72.6 | 45.4 | | **+ DataFlow-Code-5K** | 36.2 | **26.4** | 48.6 | 45.0 | 73.2 | 45.9 | | **+ DataFlow-Code-10K** | **36.8** | 26.0 | **48.8** | **45.4** | **73.8** | **46.2** | ##### 8.2.7.2 Trained on Qwen2.5-14B-Instruct | Training Data | BigCodeBench | LiveCodeBench (v6) | CruxEval (I) | CruxEval (O) | HumanEval+ | Avg | | --- | --- | --- | --- | --- | --- | --- | | **Qwen2.5-14B-Instruct** | 37.5 | 33.4 | 48.0 | 48.5 | 74.4 | 48.4 | | **+ Code Alpaca-1K** | 37.0 | 28.2 | 50.2 | 49.6 | 71.3 | 47.3 | | **+ Self-OSS** | 36.9 | 22.3 | 52.6 | 50.1 | 68.3 | 46.0 | | **+ DataFlow-Code-1K** | 41.4 | **33.7** | 51.0 | 50.9 | **77.3** | 50.9 | | **+ DataFlow-Code-5K** | 41.1 | 33.2 | 52.5 | 50.6 | 76.2 | 50.7 | | **+ DataFlow-Code-10K** | **41.9** | 33.2 | **52.9** | **51.0** | 76.2 | **51.0** |
## 📄 9. Publications Our team has published the following papers that form core components of the DataFlow system: | Paper Title | DataFlow Component | Venue | Year | | ----------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- | ----- | ---- | | [AgenticRAGTracer: A Clear and Stepwise-Process Benchmark for Agentic RAG](https://arxiv.org/abs/2602.19127v1) | Agentic RAG Data Synthesis | ACL Findings | 2026 | | [Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQL](https://arxiv.org/abs/2505.13903) | Text2SQL Data Augmentation | ICDE | 2026 | | [Let's Verify Math Questions Step by Step](https://arxiv.org/abs/2505.13903) | Math question quality evaluation | KDD | 2026 | | [MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification](https://arxiv.org/pdf/2502.13383) | Multimodal reasoning verification framework for data processing and evaluation | ACL | 2025 | | [Efficient Pretraining Data Selection for Language Models via Multi-Actor Collaboration](https://arxiv.org/pdf/2410.08102) | Multi-actor collaborative data selection mechanism for enhanced data filtering and processing | ACL | 2025 | **Contributing Institutions**: PKU HKUST CAS Shanghai AI Lab Baichuan Ant Group ## 🏆 10. Awards & Achievements We are honored to have received **first-place awards** in two major international AI competitions, recognizing the excellence and robustness of DataFlow and its reasoning capabilities: | Competition | Track | Award | Organizer | Date | | ------------------------------------------------------------------------- | ----------------------------------------------------------- | ------------------------------ | --------------------------------------------------------- | --------------- | | **ICML 2025 Challenges on Automated Math Reasoning and Extensions** | Track 2:*Physics Reasoning with Diagrams and Expressions* | 🥇**First Place Winner** | ICML AI for Math Workshop & AWS Codabench | July 18, 2025 | | **2025 Language and Intelligence Challenge (LIC)** | Track 2:*Beijing Academy of Artificial Intelligence* | 🥇**First Prize** | Beijing Academy of Artificial Intelligence (BAAI) & Baidu | August 10, 2025 |
ICML 2025 Certificate
ICML 2025 Automated Math Reasoning Challenge — First Place Winner 		LIC 2025 Certificate
BAAI Language & Intelligence Challenge 2025 — First Prize
## 🌟 11. Awesome Work Using DataFlow & DataFlow Ecosystem This section highlights **projects, research works, and applications** built on top of DataFlow or deeply integrated with the DataFlow ecosystem. **📌 Curated list of featured projects:** [[Awesome Work Using DataFlow](./awesome_dataflow.md)] We warmly welcome the community to contribute new entries via **Pull Requests**. 🙌 [Detailed Guidance](https://opendcai.github.io/DataFlow-Doc/en/guide/df_ecosystem/) can help you creating a Dataflow extension repository from DataFlow-CLI. ## 💐 12. Acknowledgements We sincerely thank [MinerU](https://github.com/opendatalab/MinerU) for their outstanding work, whose powerful PDF/document text extraction capabilities provided essential support for our data loading process. We also thank [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) for offering an efficient and user-friendly framework for large model fine-tuning, which greatly facilitated rapid iteration in our training and experimentation workflows. Our gratitude extends to all contributors in the open-source community—their efforts collectively drive the development of DataFlow. We thank Zhongguancun Academy for their API and GPU support. ## 🤝 13. Community & Support Join the DataFlow open-source community to ask questions, share ideas, and collaborate with other developers! • 📮 [GitHub Issues](../../issues): Report bugs or suggest features • 🔧 [GitHub Pull Requests](../../pulls): Contribute code improvements • 💬 Join our community groups to connect with us and other contributors!
## 📜 14. Citation If you use DataFlow in your research, feel free to give us a cite. ```bibtex @article{liang2025dataflow, title={DataFlow: An LLM-Driven Framework for Unified Data Preparation and Workflow Automation in the Era of Data-Centric AI}, author={Liang, Hao and Ma, Xiaochen and Liu, Zhou and Wong, Zhen Hao and Zhao, Zhengyang and Meng, Zimo and He, Runming and Shen, Chengyu and Cai, Qifeng and Han, Zhaoyang and others}, journal={arXiv preprint arXiv:2512.16676}, year={2025} } ```
Connect with the PKU-DCAI Research Team on Xiaohongshu: 26133106768