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Practical guides to fine-tuning, distillation, and deploying small language models.
Knowledge Distillation Explained: Teacher-Student Training for LLMs
Learn how knowledge distillation works — the teacher-student training process that compresses large language models into small, fast, deployable models without losing accuracy.
Model Distillation Tutorial: From LLM to Deployable SLM
A hands-on tutorial for distilling a large language model into a small, deployable student model. Covers the full pipeline from teacher selection to production deployment.
No-Code Model Fine-Tuning: Train a Custom SLM Without Writing Code
Learn how to fine-tune a small language model without any coding. Discover no-code and low-code platforms that let you create custom NLP models using just a prompt and a few examples.
Teacher-Student Distillation: How It Works and When to Use It
Learn how teacher-student distillation transfers knowledge from a large language model to a small, efficient one. Understand the training process, when it makes sense, and how to get started.
Distillation vs Quantization: Which Shrinks Your Model Better?
Distillation and quantization both reduce model size, but they work in fundamentally different ways. Learn the trade-offs and when to use each approach — or combine them.
Is Fine-Tuning Worth It? When to Fine-Tune vs Prompt
Prompt engineering is fast and flexible, but fine-tuning delivers higher accuracy, lower latency, and lower cost at scale. Learn when each approach makes sense and how to decide.
LoRA vs Full Fine-Tuning: When to Use What
Compare LoRA and full fine-tuning for small language models. Learn the trade-offs in accuracy, speed, and memory so you can pick the right approach for your project.