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Installing models and working with them in console mode
Nowadays, neural networks have become quite popular. They help create software, write texts, draw quite good pictures in different genres and much more . Most of these neural networks work online, that is, the request is processed and the response is generated somewhere on a remote server .

The user only sends a request, and after some time he receives a response from the server where the neural network is running. In other words, the user's computer works as a transceiver: it sends a request - it receives a response.

It would be much more interesting if the neural network were to live on the same computer, and absolutely free of charge. But for this, it needs to be installed on it. And the Ollama framework will help us with this . This framework provides a fairly large number of large language models that can easily be installed and launched locally on the user's computer.

Before you can start using the models, you need to install Ollama. On this page you can download installers for macOS and Windows operating systems. For these systems, installing Ollama should not cause any difficulties. Just run the downloaded installer, and then everything should happen automatically. But Linux users will have to talk a little with the terminal, in which you need to enter the following command:

curl -fsSL https://ollama.com/install.sh | sh
Press Enter and wait for the installation script to complete. Now portugal telegram data you can launch any model. This page contains a list of all models supported by Ollama. Let's take the codellama model as an example. Here is its separate page with all the necessary information. To launch it, you need to command:

ollama run codellama
After installing and running the model, we will see something like this:

After installing and running the model we will see something like this
The model invites us to send it a message. Judging by the description, this model should be good at code generation. Let's ask it to write a simple program. The terminal does not have syntax highlighting, so for clarity, we provide the text of the request and response outside the terminal.

Request text:

Write the source code of a button-press counter program in Python. The interface should have two buttons. One increases the number by one, and the other decreases it. Write the GUI in Tkinter.
And here is the neural network's response: