Explaining Parameter-Efficient Fine-Tuning (PEFT) Using Qwen as an Example(Development of Large Model Applications 14)

Learn how to fine-tune large language models efficiently using PEFT and LoRA techniques, with practical examples from Qwen. Boost model performance with minimal resources.

Hello everyone, welcome to the "Development of Large Model Applications" column.

In the Era of Large Model Applications, Everyone Can Be a Programmer (Development of large model applications 1)

Order Management Using OpenAI Assistants' Functions(Development of large model applications 2)

Thread and Run State Analysis in OpenAI Assistants(Development of large model applications 3)

Using Code Interpreter in Assistants for Data Analysis(Development of large model applications 4)

Using the File Search (RAG) Tool in Assistants for Knowledge Retrieval(Development of large model applications 5)

5 Essential Prompt Engineering Tips for AI Model Mastery(Development of large model applications 6)

5 Frameworks to Guide Better Reasoning in Models (Development of Large Model Applications 7)

Using Multi-Step Prompts to Automatically Generate Python Unit Test Code (Development of Large Model Applications 8)

Using Large Models for Natural Language SQL Queries(Development of Large Model Applications 9)

Building a PDF-Based RAG System with Image Recognition (Development of Large Model Applications 10)

Building a Keyword-Based Recommendation System Using Embeddings(Development of Large Model Applications 11)

Strategies for Summarizing and Evaluating Long PDF Documents(Development of Large Model Applications 12)

Generating Business Report PPTs with Assistants' Independent Thinking(Development of Large Model Applications 13)

Fine-tuning large models is a field requiring a deep understanding of both theory and practical experience. Beginners face at least three major obstacles:

• Lack of Quality Resources: Successful fine-tuning practitioners are often busy researchers or key company personnel who rarely have time to create detailed documentation explaining the intricacies of fine-tuning.

• High Learning Curve: Mastering fine-tuning demands a comprehensive understanding of the entire technology stack of large models.

• Rapid Evolution: Fine-tuning techniques are numerous and evolve quickly, including full fine-tuning, instruction tuning, reinforcement learning from human feedback (RLHF), and parameter-efficient fine-tuning (PEFT). PEFT encompasses methods like prompt tuning, prefix tuning, P-tuning, and low-rank adaptation (LoRA). This abundance of new terms and complex technologies can overwhelm beginners.

Starting from the basics, this lesson demystifies parameter-efficient fine-tuning and LoRA. Although a single lesson cannot cover every detail, it will outline the essential process, enabling you to understand and perform basic fine-tuning, laying a solid foundation for further study and application.