LLM Fine-Tuning Explained: The Complete Guide

Fine-tuning has become one of the most important skills being asked for in AI engineers today, and it is still one of those skills that not a lot of people actually understand deeply. Many people have made a fine-tuning API call once or twice. But when you ask what's actually happening under the hood, the answers get fuzzy very fast. In this video, I break down the different kinds of fine-tuning methodologies so that when you run your first fine-tuning project, you're not just making API calls. You actually understand the methods deeply from a fundamental level. I cover pre-training vs post-training, open weight vs closed source models, Parameter Efficient Fine-Tuning (LoRA, QLoRA), full fine-tuning, and reinforcement fine-tuning (RLHF, DPO, verifiable rewards). I also share when to use each method and the two biggest challenges: data quality and evaluation. Key insight: QLoRA is the default starting point for most teams fine-tuning open source models today. You can fine-tune a 70 billion parameter model on a single H100 with QLoRA. What questions do you have about fine-tuning? Drop them in the comments. 👉 Join Mastering Agentic AI Certification by The Gen Academy (Aug & Oct Cohort) https://maven.com/aishwarya-srinivasan/mastering-ai-agents?promoCode=SAVE20 Use code: SAVE20 for 20% discount Resources: Best guide: https://cloud.google.com/use-cases/fine-tuning-ai-models Fine-Tuning Libraries- Hugging Face TRL: https://huggingface.co/docs/trl Hugging Face PEFT: https://huggingface.co/docs/peft Axolotl: https://github.com/OpenAccess-AI-Collective/axolotl Unsloth: https://github.com/unslothai/unsloth Inference Providers- Hugging Face Inference: https://huggingface.co/inference-api Nebius Token Factory: https://nebius.ai/ Fine-Tuning APIs (Closed Source)- OpenAI Fine-Tuning: https://platform.openai.com/docs/guides/fine-tuning Google Vertex AI: https://cloud.google.com/vertex-ai Anthropic: https://www.anthropic.com/ Papers- LoRA Paper: https://arxiv.org/abs