Learn how to load and use an open-source language model in Python, no own GPU, no high costs, full control over your data.
A practical, beginner-friendly tutorial for software engineers who want to get started with open-source Large Language Models (LLMs). You'll learn step by step how to load and use Meta's Llama 3.1 8B Instruct via Google Colab - including prompt engineering, parameter tuning, and a real-world spell-correction example.
This tutorial is aimed at developers who:
- Are new to LLMs and open-source AI
- Are building privacy-sensitive applications where data cannot leave your own infrastructure
- Want to be independent from paid services like ChatGPT or Gemini
- What LLMs are and the difference between open-source and closed models
- How to set up Google Colab with a free GPU
- How to load Llama 3.1 8B using Unsloth
- How to control the model using
systemanduserprompts - How to tune
temperatureandmax_new_tokens - How to apply the model to tasks like spell correction and Q&A
| What | Details |
|---|---|
| Prior knowledge | Basic Python |
| Google account | For Google Colab |
| Hardware | No own GPU needed - free T4 GPU via Colab |
| Cost | Free (using the free tier of Google Colab) |
- Click the Open in Colab badge above
- In Colab, go to Runtime → Change runtime type → T4 GPU
- Run the cells step by step
Or clone the repo locally:
git clone https://github.com/codershiyar/llama-google-colab-tutorial.git cd llama-google-colab-tutorial📁 llama-google-colab-tutorial/ ├── tutorial.ipynb # Main notebook with the full tutorial └── README.md # This file | Section | Topic |
|---|---|
| 1 | Introduction and learning goals |
| 2 | What are LLMs? Open-source vs. closed models |
| 3 | Setting up Google Colab with GPU |
| 4 | Installing Unsloth |
| 5 | Loading Llama 3.1 8B |
| 6 | First test: using the model |
| 7 | System and user roles in prompts |
| 8 | Parameters: temperature and max_new_tokens |
| 9 | Conclusion, next steps, and common issues |
Hit the GPU limit?
The free Colab tier has a daily GPU limit. Wait 24 hours or upgrade to Colab Pro.
Model won't load?
Hugging Face can occasionally be overloaded. Wait 5–10 minutes and try again.
Model responding slowly?
Check if the GPU is active by running !nvidia-smi in a cell. If no GPU table appears, reconfigure the runtime.
After this tutorial, you can move on to:
- Other open-source models like Mixtral or DeepSeek-V2
- Building a REST API with Flask or FastAPI around the model
- Creating your own chatbot or virtual assistant
- Running larger Llama variants (70B) via Colab Pro or your own server
Built with ❤️ by Coder Shiyar
Sharing knowledge about open-source AI, one tutorial at a time.
