创建自定义视觉-语言数据集以微调Qwen-2-VL与LLaMA-Factory的逐步指南
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Fine-tuning large language models (LLMs) for specialized tasks often requires a well-curated dataset, especially when working with vision-language models like Qwen-2-VL. Qwen-2-VL is a powerful tool for tasks that involve understanding and interpreting both text and images, making it ideal for scenarios like document analysis, visual question answering (VQA), and more. However, creating a custom dataset tailored to the requirements of such models can be challenging.
微调大型语言模型（LLMs）以适应特定任务通常需要一个精心策划的数据集，特别是在处理像Qwen-2-VL这样的视觉语言模型时。Qwen-2-VL是一个强大的工具，适用于理解和解释文本与图像的任务，使其非常适合文档分析、视觉问答（VQA）等场景。然而，创建一个定制的数据集以满足此类模型的要求可能会很具挑战性。
In this article, I’ll walk you through the entire process of creating a vision-language dataset for fine-tuning Qwen-2-VL using LLaMA-Factory, an open-source library designed for training and fine-tuning models. We’ll cover everything from preparing the data to uploading it to Hugging Face and finally integrating it into a fine-tuning script.
在本文中，我将带您了解使用 LLaMA-Factory 创建视觉语言数据集以微调 Qwen-2-VL 的整个过程，这是一个旨在训练和微调模型的开源库。我们将涵盖从准备数据到将其上传到 Hugging Face，最后将其集成到微调脚本中的所有内容。
Prerequisites
前提条件
Before we dive in, make sure you have:
在我们深入之前，请确保您具备：
- Basic knowledge of Python programming.
- 对 Python 编程的基本知识。
- A OpenAI and Hugging Face account and grab API tokens.
- 创建一个 OpenAI 和 Hugging Face 账户并获取 API 令牌。
- Basic understanding of Finetuning, LLaMA-Factory and Qwen-2-VL. If you’re new to these tools, you can check out their respective documentation:
- 对微调、LLaMA-Factory 和 Qwen-2-VL 的基本理解。如果您是这些工具的新手，可以查看它们各自的文档：
- Qwen-2-VL Fine-tuning Script
- Qwen-2-VL微调脚本
- LLaMA-Factory
- LLaMA-Factory
Step 1: Setting Up Your Environment
步骤 1：设置您的环境
Make sure to install the required libraries before starting:
在开始之前，请确保安装所需的库：
pip install openai pillow pandas datasets huggingface_hub
pip install openai pillow pandas ...