Setting Up Tabular Regression on Ubuntu using LLaMA.cpp

This guide provides detailed instructions to set up a Tabular Regression system on Ubuntu using LLaMA.cpp. Regression models predict continuous values from tabular data, making them useful for various applications such as forecasting and risk assessment.

1. Install System Prerequisites

Start by updating Ubuntu and installing essential development tools. Open a terminal and run:

sudo apt update
sudo apt upgrade
sudo apt install python3 python3-pip git cmake build-essential
    

2. Install LLaMA.cpp

Clone the LLaMA.cpp repository and build it. LLaMA.cpp will be used to handle the model for inference:

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
mkdir build
cd build
cmake ..
make
    

3. Obtain a Pre-trained Model for Regression

LLaMA.cpp requires a compatible model, which needs to be fine-tuned for regression tasks. Obtain such a model and place it in a folder named models:

mkdir -p ../models
cp /path/to/your/model.bin ../models/
    

4. Install Python Dependencies

Install additional libraries needed for data handling and preprocessing:

pip install numpy pandas scikit-learn
    

5. Prepare Your Tabular Dataset

Place your dataset in a CSV format under the data directory. For example:

mkdir data
cp /path/to/your/data.csv data/
    

6. Write the Regression Script

Create a Python script named tabular_regression.py with the following content:

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error
import llama_cpp as llcpp  # Replace with actual llama_cpp import

# Load and preprocess the dataset
data = pd.read_csv('data/data.csv')
target_column = 'target'  # Replace with the actual target column name

# Split data into features and target
X = data.drop(columns=[target_column])
y = data[target_column]

# Split into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Standardize feature columns
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Load the model using LLaMA
model_path = '../models/your_model.bin'  # Update to your model path
model = llcpp.LLaMA(model_path)

# Function to preprocess input for LLaMA
def preprocess_input(features):
    # Ensure input matches model requirements (e.g., shape, scaling)
    return features.flatten()

# Predict using LLaMA
def predict(features):
    processed_features = preprocess_input(features)
    output = model.infer(processed_features)  # Replace with correct LLaMA regression API call
    return output[0]  # Adjust based on output format

# Perform predictions on test set and calculate error
predictions = []
for i in range(X_test.shape[0]):
    prediction = predict(X_test[i])
    predictions.append(prediction)

# Calculate mean squared error
mse = mean_squared_error(y_test, predictions)
print(f'Mean Squared Error: {mse:.4f}')
    

Ensure that you replace your_model.bin with the path to your LLaMA model and adjust the model inference function to match the LLaMA version used.

7. Run the Tabular Regression Script

Execute the script to run regression predictions on your dataset:

python3 tabular_regression.py
    

This command will output the Mean Squared Error (MSE) of the model's predictions on the test set.

8. Additional Adjustments

Consider experimenting with different scaling methods or model fine-tuning to improve accuracy. For complex datasets, larger hidden layers and different preprocessing may be beneficial.

Conclusion

You have successfully set up a Tabular Regression system on Ubuntu using LLaMA.cpp. This system is now ready for continuous-valued predictions on tabular data and can be customized further based on specific requirements.