How to Run Locally Made LLMs Easily: A Tutorial by Ollama on KDnuggets

Local Linear Models (LLMs) are a powerful tool in machine learning for making predictions based on local data points. They are particularly useful when dealing with non-linear relationships between variables, as they can capture the nuances of the data in a more granular way than traditional linear models.

In this tutorial, we will walk you through how to run locally made LLMs easily using the Ollama package on KDnuggets. Ollama is a user-friendly Python library that simplifies the process of building and running LLMs, making it accessible to both beginners and experienced data scientists.

Step 1: Install Ollama
The first step is to install the Ollama package. You can do this by running the following command in your terminal:

pip install ollama

Step 2: Import the necessary libraries
Next, you will need to import the Ollama library along with any other libraries you may need for your analysis. Here is an example of how to do this:

import ollama
import numpy as np
import pandas as pd

Step 3: Load your data
Now that you have installed Ollama and imported the necessary libraries, you can load your data into a pandas DataFrame. Make sure your data is in the correct format for running LLMs. Here is an example of how to load your data:

data = pd.read_csv(‘your_data.csv’)

Step 4: Build your LLM model
Once you have loaded your data, you can build your LLM model using the Ollama library. Specify the target variable and the features you want to include in your model. Here is an example of how to build an LLM model:

model = ollama.LLM(target=’target_variable’, features=[‘feature1’, ‘feature2’], data=data)

Step 5: Fit your model
After building your LLM model, you can fit it to your data using the fit() method. This will train the model on your data and prepare it for making predictions. Here is an example of how to fit your model:

model.fit()

Step 6: Make predictions
Once your model is trained, you can use it to make predictions on new data points. Simply pass the new data into the predict() method to get the predicted values. Here is an example of how to make predictions:

new_data = pd.DataFrame({‘feature1’: [1, 2, 3], ‘feature2’: [4, 5, 6]})
predictions = model.predict(new_data)

Running locally made LLMs using the Ollama package on KDnuggets is a straightforward process that can yield powerful insights from your data. By following this tutorial, you can easily build and run LLM models to make accurate predictions in your machine learning projects.

• Source Link: https://zephyrnet.com/ollama-tutorial-running-llms-locally-made-super-simple-kdnuggets/