Llama - Environment Setup

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The environment set up for Llama is a very few key steps that include installing a dependency, Python with its libraries, and configuration of your IDE for more efficient development. Now, you have your working environment in order, to play comfortably and develop using Llama. If developing NLP models or generally experimenting with text generation are what interest you, this would ensure a very smooth start into your AI journey.

Let's progress with installing dependencies and IDE configurations so that we can have a run for our code and its proper configurations.

Installation of Dependencies

As a precondition to get ahead with the code, you must check if all the prerequisites have been installed. There are many libraries and packages on which Llama depends to make things work smoothly in natural language processing as well as in AI-based tasks.

Step 1: Installation Python

First of all, you should ensure that Python exists on your machine. The Llama requires at least a version of 3.8 or higher for Python to install successfully. You can acquire it from the official Python website if it is not already installed.

Step 2: Install PIP

You must install PIP, Python's package installer. Here is how you check if PIP is installed −

pip --version

If that's not the case, installation can be done using the command −

python -m ensurepip upgrade

Step 3: Installing Virtual Environment

It is vital to use a digital environment to hold your project's dependencies apart.

Installation

pip install virtualenv

Creating a virtual environment for your Llama project −

virtualenv Llama_env

Activating the virtual environment −

Windows

Llama_env\Scripts\activate

Mac/Linux

source Llama_env/bin/activate

Step 4: Installing Libraries

Llama needs several Python libraries to run. To install them, type the following command into your terminal.

pip install torch transformers datasets

These libraries are comprised of −

• torch − Deep learning-related tasks.
• transformers − Pre-trained models.
• datasets − To deal with huge datasets.

Try importing the following libraries in Python to check the installation.

import torch
import transformers
import datasets

If there is no error message, then installation is done.

Setup Python and Libraries

Setup dependencies followed by installing Python, and the libs to build Llama.

Step 1: Verify installation of Python

Open a Python interpreter and execute the following code to verify that both Python and the requisite libraries are installed −

import torch
import transformers

print(f"PyTorch version: {torch.__version__}")
print(f"Transformers version: {transformers.__version__}")

Output

PyTorch version: 1.12.1
Transformers version: 4.30.0

Step 2: Installing additional Libraries (Optional)

You might require some additional libraries depending upon the use cases you have with Llama. Below is the list of optional libraries, but very useful to you −

• scikit-learn − For machine learning models.
• matplotlib − For visualizations.
• numpy − For scientific computing.

Install them with the following commands −

pip install scikit-learn matplotlib numpy

Step 3: Test Llama using a Small Model

We will load a small, pre-trained model to check everything is running smoothly.

from transformers import pipeline

# Load the Llama model
generator = pipeline('text-generation', model='EleutherAI/gpt-neo-125M')
 
# Generating text
output = generator("Llama is a large language model", max_length=50, num_return_sequences=1)
print(output)

Output

[{'generated_text': 'Llama is a large language model, and it is a language 
model that is used to describe the language of the world. The language model
is a language model that is used to describe the language of the world. 
The language model is a language'}]

This is an indication that the configuration was proper, and we can now embed Llama into our application.

Configuring Your IDE

Selecting the right IDE and configuring it properly will make development very smooth.

Step 1: Choosing an IDE

Here are some of the most popular choices of IDEs to work with Python −

Visual Studio Code VS Code PyCharm

For this tutorial, we'd choose VS Code because it's lightweight and has tremendous extensions exclusive to Python.

Step 2: Install Python Extension for VS Code

To begin working with Python development in VS Code, you need the Python extension. It may be installed through extensions directly in VS Code.

• Open VS Code
• You can navigate to the Extensions view, clicking on the Extensions icon, or using Ctrl + Shift + X.
• Search for "Python" and install the official extension by Microsoft.

Step 3: Configure Python Interpreter

We set up the Python interpreter to make use of this virtual environment that we created earlier by doing exactly this −

• Ctrl+Shift+P − opens command palette
• Python − Select Interpreter and select the available interpreter in the virtual environment; we're selecting the one located in Llama_env

Step 4: Create a Python File

Now that you have chosen your interpreter, you can create a new Python file and save it under any name you wish (for example, Llamam_test.py). Here is how you can load and run a text generation model with Llama −

from transformers import pipeline
generator = pipeline('text-generation', model='EleutherAI/gpt-neo-125M')
# Text generation
output = generator("Llama is a large language model", max_length=50, num_return_sequences=1)
print(output)

In the output, you will see how the Python environment is configured, the code is written within the integrated development environment, and the output is shown in the terminal.

Output

[{'generated_text': 'Llama is a large language model, and it is a language
model that is used to describe the language of the world. The language 
model is a language model that is used to describe the language of 
the world. The language model is a language'}]

Step 5: Running the Code

How to run the code?

• Right-click on the Python file and run Python File in Terminal.
• By default, it will display output automatically in the integrated terminal.

Step 6: Debugging in VS Code

Besides great support for debugging, VS Code gives you excellent debugging support. You can create breakpoints by clicking to the left of the code line number and start debugging with F5. This will help you step through your code and inspect variables.