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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.
Can You Combine RAG and Fine-Tuning?
Yes. You fine-tune the generator to answer strictly from retrieved passages, including deliberately irrelevant ones. What the training data looks like and where the combination breaks.
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 can't 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.