Project prerequisites
In this project, you'll fine-tune a small open-source language model so it follows instructions in your style. We'll do it step by step, and you'll end with a working checkpoint you can run locally for inference.
This isn't a toy example. You can treat this as your first "real" fine-tuning build, following the complete workflow that professionals use: dataset → preprocessing → training → evaluation → inference. The same process that powers production instruction-tuned models at companies—just at a smaller, more manageable scale.
The goal here is to build something that works and that you can iterate on. By the end, you'll have a model checkpoint saved on your machine, ready to generate responses in the style you've trained it on. You'll also have the infrastructure to improve it: add more data, refine your examples, and retrain as many times as you want.
What you'll build
A fine-tuned model that can answer a narrow set of instruction types (for example: short explanations, summaries, or a specific "brand voice") based on a dataset you control.
We'll keep the dataset small at first (so it trains quickly), but we'll build the pipeline in a way that scales.
Why start narrow? Because specificity is your friend when you're learning. A model trained on 200 high-quality examples of one task will outperform a model trained on 2,000 mixed examples of ten different tasks. Once you've proven the pipeline works on a focused use case, you can expand the scope: add more instruction types, more variety, more complexity.
Think of this as your "hello world" for instruction tuning—but one that actually produces a useful artifact you can deploy, test with real prompts, and show to others.
Before you start, let's make sure you have the right setup. This project is designed to be accessible, but there are a few hardware and software requirements that will make your life much easier.
Recommended hardware
- Best: 1 GPU with 12–24GB VRAM (RTX 3060 12GB works; 16GB+ is nicer).
- Still doable: smaller model + smaller batch + gradient accumulation.
- CPU-only: possible for very small models, but slow. (I'll include a "CPU fallback" note.)
If you're working with a consumer GPU like an RTX 3060 or 3070, you're in good shape. If you have less VRAM, don't worry—we'll cover memory-saving tricks like reducing batch size, lowering sequence length, and using gradient accumulation to simulate larger batches. These techniques let you train on hardware that would otherwise be too constrained.
If you're on a laptop with no dedicated GPU, you can still follow along using a very small model (like TinyLlama 1.1B), but expect training to be slow. In that case, I'd recommend starting with an even smaller dataset (maybe 50–100 examples) just to get through the loop faster.
Don't have a GPU at all? Consider using a cloud platform like Google Colab (free tier includes a T4 GPU), Paperspace, or Lambda Labs. You can often get a few hours of GPU time for free or very cheap, which is more than enough to complete this project.
What model should you use?
To keep this practical and accessible, use a model in the 1B–3B range for your first run.
Why this size? Models in this range are small enough to fine-tune on consumer hardware, but large enough to actually learn meaningful behavior from your dataset. They're also fast to experiment with: a full training run might take 20 minutes instead of 2 hours, which means you can iterate quickly.
Good training-friendly choices:
TinyLlama/TinyLlama-1.1B-Chat-v1.0(very approachable)Qwen/Qwen2.5-1.5B-Instructmicrosoft/phi-2(small, capable, but licensing/usage depends—verify your use case)
In the examples below, I'll use TinyLlama 1.1B Chat because it's small and widely used for learning.
TinyLlama is a great choice for a first project because it's been pretrained on a diverse corpus and already has some instruction-following ability. It's also well-documented and widely adopted in the community, which means if you run into issues, there's a good chance someone else has encountered (and solved) them before.
Once you've completed this project with TinyLlama, you can repeat the exact same process with a larger model (like a 3B or 7B model) if you have the hardware. The code won't change much—you'll just need to adjust memory settings and maybe train for a bit longer.