Summary
You can unlock the power of AI without a tech background! Using Ollama, anyone can train AI models tailored to their needs. It’s easy to use, runs on your own device, and lets you create smarter, customized solutions—no coding expertise required!
Why Run a Local Bot?
Whether you’re fully into the AI hype or think it’s all a bunch of hot air, AI tools likeChatGPT and Claudeare here to stay. Running a local AI chatbot offers some tangible benefits.
Key Considerations When Using Large Language Models
AnAI large language model (LLM), big or small, can be resource-heavy. They often require powerful hardware like GPUs to do the heavy lifting, a lot of RAM to keep the models in memory, and significant storage for growing datasets.
Parameters are values the model adjusts during training. More parameters lead to better language understanding, but larger models require more resources and time. For simpler tasks, models with fewer parameters, like 2B (billion) or 8B, may be sufficient and faster to train.
Tokens are chunks of text that the model processes. A model’s token limit affects how much text it can handle at once, so larger capacities allow for better comprehension of complex inputs.
Lastly, dataset size matters. Smaller, specific datasets—like those used for customer service bots—train faster. Larger datasets, while more complex, take longer to train. Fine-tuning pre-trained models with specialized data is often more efficient than starting from scratch.
Getting Ollama Up and Running
Ollama is a user-friendly AI platform that enables you torun AI models locally on your computer. Here’s how to install it and get started:
Install Ollama
You can install Ollama on Linux, macOS, and Windows (currently in preview).
FormacOSandWindows,download the installerfrom the Ollama website and follow the installation steps like any other application.
On Linux, open the terminal and run:
Once installed, you’re ready to start experimenting with AI chatbots at home.
Running Your First Ollama AI Model
Once you install Ollama, open the terminal on Linux or macOS, or PowerShell on Windows. To start, we’ll run a popular LLM developed by Meta called Llama 3.1:
Since this is the first time you’re using Ollama, it will fetch the llama 3.1 model, install it automatically, then give you a prompt so you may start asking it questions.
Running Other Models
While Llama 3.1 is often the go-to model for most people just starting out with Ollama, there areother models that you can try. While Llama 3.1 is a great starting point, you may want to explore other models, such as lighter ones that better suit your system’s performance.
When you find a model you think might work for you, your computer hardware, and particular needs, you just execute the same command as you did for Llama 3.1, for example, if you want to download Phi 3:
Again, if this is your first time using the model, Ollama will automatically fetch, install, and run it.
Other Commands You’ll Want to Know
Ollama has quite a few other commands you can use, but here are a few we think you might want to know.
Models take up significant disk space. To clear up space, remove unused models with:
To view models you’ve already downloaded, run:
To see which models are actively running and consuming resources, use:
If you want to stop a model to free up resources, use:
If you want to see the rest of Ollama’s commands, run:
Things You Can Try
If you’ve held off on trying AI chatbots because of concerns about security or privacy, then now’s your time to jump in. Here are a few ideas to try to get started!
Create a to-do list: Ask Ollama to generate a to-do list for the day.
Plan lunch for the week: Need help planning meals for the week? Ask Ollama.
Summarize an article: Short on time? Paste an article into Ollama and ask for a summary.
Feel free to experiment and see how Ollama can assist you with problem-solving, creativity, or everyday tasks.
Congratulations on setting up your very own AI chatbot at home! You’ve taken the first steps into the exciting world of AI, creating a powerful tool that’s tailored to your specific needs. By running the model locally, you’ve ensured greater privacy, faster responses, and the freedom to fine-tune the AI for custom tasks.