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.
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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.
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