Model fine-tuning

The process of adapting a pre-trained large-language-models to a specific dataset or task to enhance performance, accuracy, and specialization in targeted domains.

Methodologies

• Supervised Fine-Tuning (SFT)
• Parameter-Efficient Fine-Tuning (PEFT)
  • LoRA (Low-Rank Adaptation)
  • QLoRA
• Reinforcement Learning from Human Feedback (RLHF)
• instruction-tuning

Recent Developments

• DeepSeek V4 release demonstrates significant advancements in the performance and efficiency of open-source large-language-models.
• New model suites emphasize high-efficiency architectures and refined open-source accessibility.

Related Notes

• 2026 04 24 DeepSeek V4 Next Gen Open Source LLM Performance and Efficiency Analysis