Build and Fine-Tune Custom LLMs with DeepSeek and Rented GPUs

Building domain-specific large language models no longer requires massive clusters or exorbitant budgets. Modern workflows allow developers to leverage frontier models to generate synthetic training data, filter it programmatically, and fine-tune compact open-weights models like Llama 3.2 or Gemma on rented infrastructure.

In this tutorial, we will walk through a complete pipeline: writing local documentation, using DeepSeek-V4.1-Flash through n1n.ai to generate question-and-answer pairs, cleaning the dataset with a Python script, and training a LoRA adapter using LLaMA-Factory on a remote GPU instance.

Prerequisites and Setup

Before writing any code, ensure you have the required environment configured. We will generate the dataset locally on your machine and rent a GPU solely for the training phase to minimize costs.

Required tools and accounts:

• Python 3 installed locally.
• The openai Python package (pip install openai).
• A valid account and API key from n1n.ai.
• 3 to 10 Markdown files of your own documentation located in a docs/ folder.

Set your API key as an environment variable in your terminal:

export N1N_API_KEY="your-api-key-here"

Verify your setup by running a quick python check to ensure the environment variable is recognized:

python3 -c "import os; print(bool(os.environ.get('N1N_API_KEY')))"

This should print True.

Step 1: Generate Synthetic Data with DeepSeek

We will use a Python script to split your Markdown documentation at every ## heading, send each section to DeepSeek via the n1n.ai OpenAI-compatible endpoint, and output JSONL formatted training data.

Save the following script as gen_data.py:

import json
import os
import re
import sys
import time
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from openai import OpenAI

client = OpenAI(
    base_url="https://n1n.ai/v1