Model Adaptation Techniques

An interactive guide to comparing Fine-Tuning, LoRA, and Distillation for adapting large AI models.

Explore the Techniques

This tool helps you compare three key techniques for adapting large AI models. Click the buttons below to explore each method and see how they stack up in our summary chart.

Fine-Tuning — Same Model, Smarter Brain

Goal: Adapt the model to your specific domain, task, or tone.

  • 🔸
    Effect on Size: No change. The model stays the same size (e.g., 7B parameters remains 7B).
  • 💰
    Cost: Moderate. Depends on the size of your high-quality dataset (e.g., 1K-20K examples).
  • 🎯
    Best For: Specialization (adapting for a specific tone, domain like legal/medical, or intent).

🧠 Example: Llama 3–8B fine-tuned on 10,000 legal Q&As → still 8B, but performs like a legal expert.

Comparative Analysis

The chart below visualizes the key trade-offs between these methods. "Relative Training Cost" shows how expensive the process is (Higher = More Expensive). "Relative Model Size Change" shows the impact on the final model size (0 = No Change, 1 = Slightly Bigger, -2 = Major Reduction).

Quick Summary

Here is all the data in a simple table for easy side-by-side comparison.

Method Model Size Training Cost Purpose
Fine-Tuning Same Medium Domain adaptation
LoRA / QLoRA Slightly bigger Low Cost control, experiments
Distillation Major reduction High On-device, low latency