Core

Fine-Tuning Hyperparameters and Overfitting

Choosing to fine-tune is the easy part. The run succeeds or fails on a short list of dials: learning rate, epochs, effective batch size, LoRA rank and alpha, which modules you target, and max sequence length. Overfitting is the default outcome when those dials are set by copying a blog post, and its signature is a train loss that keeps falling while eval loss turns up. AI, ML, and GenAI interviews probe this because it separates people who have actually run a fine-tune from people who have only read about one.

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RELATED CONCEPTS

LoRA and Parameter-Efficient Fine-Tuning Prompting vs RAG vs Fine-Tuning Overfitting and Regularization Hyperparameter Optimization Catastrophic Forgetting and Continual Learning

PRACTICE THIS IN REAL QUESTIONS

LLM & GenAI FundamentalsWhat are the key hyperparameters for fine-tuning an LLM, and how do you actually set them?→LLM & GenAI FundamentalsYour fine-tuned model reproduces training examples word for word instead of generalizing. How do you fix it?→LLM & GenAI FundamentalsYou fine-tuned a model and the task metric went up. How do you prove you did not break everything else?→LLM & GenAI FundamentalsExplain LoRA, QLoRA, and parameter-efficient fine-tuning. Why train a fraction of the parameters?→LLM & GenAI FundamentalsWhat is instruction tuning, and how does it differ from pretraining and alignment?→ML Infrastructure & GPUsHow do you serve many fine-tuned model variants efficiently (multi-LoRA serving)?→

COMPANIES THAT ASSUME THIS

OpenAI Anthropic Google Microsoft Scale AI Decagon

NEXT IN FOUNDATIONS OF LLMS & GENAIContext Compression and Prompt Compaction→