Skip to content
KoishiAI
ไทย
← All tutorials

Fine-Tuning Language Models — When to Do It, How, and How to Know It Worked

Fine-tuning is not the first thing to try. The big providers themselves say start with prompting, and in 2026 fine-tuning through their APIs has shrunk sharply. This chapter helps you tell whether your problem needs a prompt, RAG or fine-tuning.

Contents

  1. 01 When to fine-tune, and when a prompt or RAG is enough Fine-tuning is not the first thing to try. The big providers themselves say start with prompting, and in 2026 fine-tuning through their APIs has shrunk sharply. This chapter helps you tell whether your problem needs a prompt, RAG or fine-tuning.
  2. 02 Data for fine-tuning: file format, chat templates, Thai data and terms of use Data decides the outcome of fine-tuning more than tools do. This chapter covers the messages format tools expect, why the chat template must match in training and in use, the Thai datasets available and their licences, and AI providers' terms that forbid training on their outputs.
  3. 03 LoRA and QLoRA in practice: choosing a Thai base model and tools LoRA trains only small matrices inserted into the model; QLoRA does the same on a model compressed to 4 bits, making fine-tuning possible on a single GPU. This chapter covers the idea, the trade-off, the Thai base models available with their licences, and the current tools.
  4. 04 Evaluating a fine-tune: your own test set, memorising, and forgetting Without an evaluation set you cannot tell whether fine-tuning worked. This chapter covers splitting training and test data, memorising instead of learning, forgetting existing abilities, the Thai benchmarks available, and Thai-specific traps such as answers that drift into English.
  5. 05 Deploying a fine-tuned model: merge the adapter, convert to GGUF, run with Ollama or vLLM From adapter to a model you can run: merge the adapter into the base model, convert to GGUF and shrink it with llama.cpp, import it into Ollama — which stopped accepting separate LoRA adapters in September 2026 — or serve the adapter directly with vLLM, plus the licence conditions when you distribute.

This series is All Rights Reserved — free to read, but copying, downloading, or republishing requires written permission.