Running or licensing large language models (LLMs) requires significant resources and money, and due to their increased parameters, larger models take more time to generate text.
👉 Model distillation offers a quick fix to the above.
Model distillation is a method where a smaller, simpler model (student) learns to mimic a larger, more complex model (teacher) by studying its probability distributions over outputs.
👉 Think of it as mentorship for machines! 👩🏫➡️👩🎓
⚒️ How Does It Work?
👉 Teacher-Student Architecture
The teacher is a large, complex model trained to perfection. The student is a smaller, simpler model that learns by mimicking the teacher's behaviour. The student mimics not just the teacher's final answers but its thought process (probabilities) for every possible outcome, meaning the student does not just memorise but learns how to think like the teacher! 🧠📚✨
🔑 Key Steps
1️⃣ Teacher model generates outputs, including probabilities for all possible classes, not just the final label. 🧠
2️⃣ Student model learns from these probabilities, absorbing nuanced insights about the task. 🎓
3️⃣ This results in a smaller model that's specialised and performs efficiently without sacrificing accuracy. ⚡
🖊️ Why does it matter?
Because LLMs are resource-intensive and require significant computational power and money to deploy, model distillation helps tackle this by training compact models that maintain similar performance but are far more efficient for specific tasks.
🔑 Key Features
✔️ Fast and light: Smaller models mean reduced computational requirements, faster responses, and easier deployment on resource-constrained devices.
✔️ Smart learning: Smaller models capture subtle insights from the teacher that are sometimes even improving at specialised tasks!
👉 Model distillation is a game-changer, transforming LLMs and making them smarter, faster, and more accessible.
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