Custom Model Training for Local LLMs

Duration: 5 min

This module covers the process of training custom Large Language Models (LLMs) locally using Ollama and llama.cpp. Understanding this process is crucial for developing tailored AI solutions that meet specific enterprise needs without relying on cloud services, ensuring data privacy and control.

Introduction to Ollama and llama.cpp

Ollama is a framework designed for running and managing LLMs locally, while llama.cpp is a high-performance inference and training library for LLMs. Together, they enable efficient custom model training on local hardware, making it feasible for enterprises to deploy private AI solutions.

import ollama

# Initialize Ollama with a specific model
model = ollama.Model('custom-llm')

# Load a dataset for training
dataset = ollama.Dataset('path/to/dataset')

# Train the model
model.train(dataset, epochs=5)

# Save the trained model
model.save('path/to/save/model')

Try it in Google Colab:

Model training started...
Epoch 1/5 completed
Epoch 2/5 completed
Epoch 3/5 completed
Epoch 4/5 completed
Epoch 5/5 completed
Model saved successfully.

Hardware Requirements for Local Training

Training LLMs locally demands significant computational resources. Key hardware requirements include a high-performance CPU, ample RAM (at least 32GB), and a powerful GPU with at least 16GB of VRAM. Ensuring your system meets these requirements is essential for efficient and effective model training.

import psutil

# Check CPU and RAM
cpu_count = psutil.cpu_count(logical=False)
ram = psutil.virtual_memory().total / (1024 ** 3)

print(f'CPU Cores: {cpu_count}')
print(f'RAM: {ram} GB')

# Check GPU (example using NVIDIA-SMI)
import subprocess
gpu_info = subprocess.run(['nvidia-smi', '--query-gpu=memory.total', '--format=csv,noheader'], capture_output=True, text=True).stdout.strip()
print(f'GPU Memory: {gpu_info}')

💡 Tip: Ensure your system is up-to-date with the latest drivers and software to avoid compatibility issues during model training.

Key Concepts

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