In this video, I show you how I distill a large language model into a smaller, faster student—end to end—using Hugging Face + Google Colab (free T4 friendly).
What you’ll learn
🎯 KD basics: why distillation matters for speed, cost, and on-device use
🧩 Applications: edge deployment, privacy, domain-specialized SLMs
🧱 Types: logits, intermediate features, attention, self-distillation
⚙️ Algorithms: DistilBERT, TinyBERT, MiniLM, PKD—when to use which
🧪 Colab demo: GPT-2 (8-bit teacher) → DistilGPT2 (LoRA student) with KD loss
🧰 Stack: Transformers, Datasets, PEFT (LoRA), bitsandbytes
📦 Data: safe streaming chunker (no long-sequence errors), quick perplexity eval
🚀 Inference: generate with the student, save LoRA, optionally merge & ship
Who is this for
👩💻 Engineers shipping LLM features
📚 Students and researchers optimizing latency/cost
🧑⚕️ Domain builders (healthcare, legal, finance) needing compact models
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GitHub: https://github.com/AIAnytime/Knowledge-Distillation-Notebook
DeepSeek Paper: https://arxiv.org/pdf/2501.12948
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