Fine Tuning
30 bites tagged Fine Tuning — interview questions with model answers, and 60-second explainers.
Learning Rate Scheduling: A Gearbox for Model Training
Think of a learning rate schedule as a training 'gearbox,' starting fast and slowing for precision. It's used when fine-tuning large models to adapt them without breaking them.
Transfer Learning: Don't Train Vision Models from Scratch
Don't train a vision model from scratch. Transfer learning reuses a model trained on a huge dataset (like ImageNet) as a starting point for your specific task. This lets you achieve high accuracy on new image types with much less data and compute.
When would you use LoRA vs full fine-tuning?
This tests your grasp of practical trade-offs in ML systems, specifically training cost versus model customization. A great answer explains that LoRA is a parameter-efficient method ideal for resource-constrained scenarios, reducing trainable parameters by 10,000x and GPU memory by 3x. Full fine-tuning is for high-budget projects requiring deep model changes. A red flag is vaguely saying LoRA is 'cheaper' without quantifying the resource savings or explaining the mechanism.
RAG vs. Fine-Tuning: Key Differences
This tests your understanding of how LLMs incorporate knowledge, specifically the trade-offs between embedding it in model weights versus retrieving it at runtime. A great answer defines RAG as runtime retrieval from an external source and fine-tuning as baking knowledge into model parameters, then contrasts their approaches to knowledge updates, cost, and providing citations. A red flag is stating one is always better, or failing to explain that they solve different problems and can be used tog
Direct Preference Optimization (DPO): Your LLM is a Reward Model
Direct Preference Optimization (DPO) treats your language model as a secret reward model, simplifying alignment with human preferences. Instead of RLHF's complex multi-stage process, DPO directly fine-tunes the model on preference data (e.g., "response A is better than B") using a simple classification loss. This avoids training a separate reward model and the instability of reinforcement learning. The footgun is assuming DPO works without a strong base model and quality preference data.
LoRA: Fine-Tuning LLMs with a Fraction of the Cost
LoRA fine-tunes a massive model by training tiny "adjustment" matrices instead of retraining all its billions of parameters. This allows you to create many specialized versions of a base model like GPT-3 without the prohibitive cost of storing and training full copies. The key advantage is that these adjustments merge into the original weights, so you get specialized models with no added inference latency, a common footgun with other parameter-efficient techniques.
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