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.
- ThinkingCap-Qwen3.6-27B: Evaluating LLM Reasoning Efficiency and Accuracy highlights BottleCap AI’s fine-tuned variant of Qwen 3.6, which achieves comparable accuracy to the base model while reducing “thinking” overhead by 36%, demonstrating gains in reasoning efficiency.