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Practical guides to fine-tuning, distillation, and deploying small language models.
How to Distill a Large Language Model into a Small One
A practical guide to distilling large language models into small, deployable models. Learn the end-to-end process — from choosing a teacher to deploying a student that matches its accuracy.
How to Fine-Tune a Small Language Model (Step-by-Step Guide)
Learn how to fine-tune a small language model for your specific use case. This step-by-step guide covers data preparation, training configuration, LoRA adapters, and deployment.
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.
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.
Air-Gapped and On-Premise LLM Deployment
Running a language model inside a network with no outbound internet access — why frontier APIs are structurally unavailable there, why a distilled small model fits the hardware that already exists, and what a working deployment contains.
Do You Need to Fine-Tune the Tokenizer?
Almost never — the tokenizer that shipped with a checkpoint is bound to its embedding matrix, and changing the vocabulary throws away pretrained representations you cannot cheaply relearn.
Does the Student Need the Same Architecture as the Teacher?
No — text-level distillation transfers behaviour through generated examples, so student and teacher can come from unrelated model families. When architecture does start to matter.
Full Fine-Tuning vs Adapters
Full fine-tuning rewrites every weight; adapters leave the base frozen and train a small add-on. Here is what counts as an adapter and where the two approaches genuinely diverge.