How to Fine-Tune an LLM: SFT, LoRA, QLoRA and DPO Explained

📰 Medium · Machine Learning

Learn to fine-tune LLM models using SFT, LoRA, QLoRA, and DPO for improved performance

intermediate Published 17 May 2026
Action Steps
  1. Read about the basics of fine-tuning and its importance in LLM models
  2. Explore the different fine-tuning methods such as SFT, LoRA, QLoRA, and DPO
  3. Apply SFT to a pre-trained LLM model to adapt it to a specific task
  4. Use LoRA to update the model's weights and improve its performance
  5. Implement QLoRA to reduce the computational cost of fine-tuning
  6. Experiment with DPO to dynamically adjust the learning rate during fine-tuning
Who Needs to Know This

Machine learning engineers and researchers can benefit from this article to improve their LLM models, while data scientists and AI engineers can apply these techniques to their projects

Key Insight

💡 Fine-tuning is essential to adapt pre-trained LLM models to specific tasks and improve their performance

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🤖 Fine-tune your LLM models with SFT, LoRA, QLoRA, and DPO for better results!
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