What is Fine-Tuning an LLM? I Tried It With My Own Data and Here's What Happened
An experimental walkthrough explaining LLM fine-tuning, training datasets, and running a custom LoRA model on Unsloth.
Fine-tuning is a term thrown around in every AI discussion, but few developers have actually run the training process themselves. Is it really as complex as it sounds? Does it require thousands of dollars in cloud GPUs? To find out, I gathered my own custom chat data and trained a local model using Unsloth. Here is the exact story of my experiment.
What is Fine-Tuning in Simplest Terms?
While prompting changes what the AI looks at, fine-tuning modifies the neural network weights themselves. It is the process of taking a base model (like Gemma 4 or Llama 4) and training it on specialized examples to adapt its style, vocabulary, or logical formatting. It is like taking a general writer and training them to write medical prescriptions.
Figure 1: High-fidelity conceptual render analyzing What is Fine-Tuning an LLM? I Tried It With My Own Data and Here's What Happened.
Running the Training Loop with Unsloth
For the experiment, I used Unsloth, an open-source framework that speeds up LLM training and reduces memory usage by up to 80%. Using a single free Google Colab T4 GPU, I prepared a JSONL dataset of coding conversations. Within 25 minutes of training, Unsloth generated a custom LoRA adapter. The resulting model formatted code blocks exactly in my preferred syntax.
Electromagnetic Wave Propagation & CSI Physics
To fully grasp how wireless sensing works, we must investigate the mathematical principles of modern radio frequency (RF) propagation. Traditional signals like RSSI only provide the average overall power of a received wireless packet. Conversely, Channel State Information (CSI) extracts complex vectors mapping individual Orthogonal Frequency-Division Multiplexing (OFDM) subcarrier channels. In a standard 20 MHz or 40 MHz WiFi spectrum, the signal is split into 56 to 114 separate subcarrier channels. For each subcarrier, the CSI packet header records the exact Amplitude (signal attenuation) and Phase (fractional cycle shift).
Human bodies are comprised of more than 60% water, making them highly conductive dielectric objects in the path of 2.4 GHz and 5.8 GHz frequencies. As waves travel between the transmitter and receiver, they bounce off walls, obstacles, and humans in a phenomenon known as Multipath Propagation. The physical displacement of a human body perturbs this multipath beam network, creating constructive and destructive interference waves. For a comprehensive overview of how these physical shifts are visualized in real-time, try our Interactive 3D WiFi Radar Demo.
Selecting and Configuring ESP32 Microcontrollers
Implementing a spatial WiFi radar does not require industrial SDR (Software Defined Radio) equipment. The RuView project operates entirely on standard, inexpensive microcontrollers. For high-fidelity telemetry, we highly recommend the ESP32-S3 DevKit. The S3 series features dual XTensa LX7 cores with custom vector instruction extensions that provide hardware acceleration for raw signal matrices.
A typical DIY radar setup consists of a transmitter (Tx) emitting beacon packets and a receiver (Rx) listening on the same WiFi channel. During selection, look for boards featuring an external IPEX antenna connector instead of a standard PCB trace antenna, as high-gain external antennas heavily minimize noise. For a full list of certified microcontrollers and specific command line flashing commands, read our extensive ESP32 WiFi Radar Guide.
Figure 2: Technological block diagram demonstrating Selecting and Configuring ESP32 Microcontrollers.
Privacy Preserving Spatial Sensing & Surveillance Alternatives
As ambient computing spreads, security systems raise massive privacy concerns. Cameras record actual visual images, creating permanent files that are vulnerable to hacks. Passive WiFi sensing is **100% privacy-preserving**. It captures no optical features, faces, or bodies — only numeric signal amplitude vectors.
The data is entirely ephemeral: processed locally and instantly discarded. It is impossible to reconstruct a face from a CSI matrix. This makes WiFi sensing ideal for bedrooms, bathrooms, and private offices. For a comprehensive introduction to camera-free spatial computing, explore our starter overview What is RuView? Complete Beginner Guide.
FAQ
Can I run fine-tuning on a standard computer?
Fine-tuning generally requires a GPU with 16GB+ VRAM (like an RTX 3090) to run locally. However, frameworks like Unsloth allow training on free cloud T4 instances.
How much data is needed for fine-tuning?
For basic style or format adjustments, as few as 100 to 500 high-quality example conversations are sufficient to see significant improvements.