Fine-Tuning Series: GPT-2 to Advanced Techniques
Comprehensive 5-day fine-tuning series covering RAG vs Fine-Tuning fundamentals, instruction fine-tuning GPT-2 from scratch, scaling efficiently with Unsloth, LoRA/QLoRA, Prefix tuning, Soft-Prompts, Multimodal fine-tuning in JAX, and cutting-edge research implementations like Subliminal Learning and RAFT.
Key Learnings
Fine-tuning is about teaching AI to specialize. I learned the fundamental trade-offs: RAG = Dynamic Knowledge + Low Training Cost, while Fine-Tuning = Specialization + High Consistency. According to research on Theoretical Limitations of Embeddings, embeddings alone cannot capture all fine-grained semantic relationships - they flatten meaning into fixed dimensions. That's why Fine-Tuning becomes crucial when tasks demand deep semantic understanding beyond retrieval. I fine-tuned GPT-2 from scratch, learning the anatomy of alignment - how models internalize instruction patterns, how weights shift for behavioral tuning, and the foundations that make InstructGPT and Alpaca possible. Unsloth revolutionized efficiency with 4-bit quantization (QLoRA), LoRA adapters, and optimized kernels enabling 7B models on consumer GPUs with <10GB VRAM. The series culminated in research implementations of Subliminal Learning (behavioral transfer in LLMs) and RAFT (Retrieval-Augmented Fine-Tuning) for domain-specific RAG.