- Fine Tuning Techniques
Compares full fine-tuning, LoRA, and QLoRA for adapting LLMs, with pros, cons, and guidance on when to use each, plus fine-tuning tools.
- RAG Fine Tuning
Notes on choosing between prompting, RAG, and fine-tuning for LLM applications, covering PEFT, RLHF, chunking, RAG vs CAG, vector databases, and Vietnamese embedding models.
- Senator Knowledge Gap Detection
Explains SENATOR, a framework that uses structural entropy, a knowledge graph, and MCTS to find LLM knowledge gaps and patch them with targeted synthetic-data fine-tuning.